On the seasonal prediction of surface climate over Vietnam using Regional Climate Model (RegCM3) Phan Van Tan, Nguyen Quang Trung, Ngo Duc Thanh, Le Nhu Quan Hanoi University of Science, VNU Hanoi Abstract In this study the Regional Climate Model version 3.0 (RegCM3) has been employed to simulate climate conditions over Vietnam and adjacent areas during the summer monsoon period, from 00UTC April 01 to 00UTC November 01 with the first one month is used for spin-up time. The main purpose of the study is to assess the performance of RegCM3 in seasonal forecasting for Vietnam. The model, driven by the NCEP/NCAR reanalysis has been run for the period 1996–2005. The model domain centered at 11.5 o N and 108.0 o E, has 145 and 105 grid-points in west-east and south-north directions, respectively, and has 36-km horizontal resolution for both directions. Over the ocean, RegCM3 is forced by the Optimum Interpolation Sea Surface Temperature (OISST) data, which is available on a 1.0 × 1.0° grid mesh and provided by the National Oceanic and Atmospheric Administration (NOAA, USA). The simulated mean sea level pressure, geopotential height and wind field over the interested domain are compared to the NCEP/NCAR reanalysis data. Monthly mean surface air temperature and precipitation from 58 meteorological stations over Vietnam are also used to validate the RegCM3’s results. 1. Introduction Prediction of weather fluctuations on seasonal timescales, so-called seasonal prediction, is of great scientific and societal interest, and is very important for product planning as well as for disaster prevention. Unlike short-range weather forecast which focuses on daily or hourly fluctuations, seasonal prediction interests in time-averaged values (Wang Shaowu et al., 2001). Basic products of seasonal prediction are often monthly mean or seasonal mean of temperature and precipitation. According to Stockdale (2000), seasonal prediction can arise in two distinct ways: empirical and dynamical approaches. In the empirical approach, predictive models are derived based on the relationships between predictants (eg. surface climate elements) and predictors (eg. atmospheric variables, sea surface temperature (SST), soil moisture, etc) using historical observed or model-based datasets. In this case, the physical basis knowledge of seasonal predictability is critically important for making practical forecasts. Up to present, the empirical approach has been used worldwide for predicting tropical cyclone activities, seasonal mean temperature, precipitation, etc (Annamalai et al., 2005; Duffy et al., 2006; Kloizbach et al., 2003; Krishnamurti et al., 2001). In contrast, seasonal forecasts using dynamical approach can be formulated using mathematical models of the climate system, which are extensions of numerical weather prediction models. These models can predict evolutions of the climate system for several months in advance (Doblas-Reyes et al., 2006). An important milestone of this approach was a simple model for predicting El Nino variability in which initial conditions were created by using the observed wind field over the Pacific (Cane et al.,1986). To date, there are a wide variety of such models have been developed, including Global Climate Models (GCMs) (eg. Wang Shaowu et al., 2001; Stockdale, 2000) and Regional Climate Models (RCMs) which are used as tools for dynamically downscaling GCM outputs. 2 Located in the eastern part of the Indochina Peninsula, Vietnam is a region with complex topography, land surface conditions, coastlines, and with a climate largely influenced by mesoscale phenomena. During the summer months (May to October), almost all areas of the country have experienced high-temperature conditions except the high mountain areas. Under the influence of South Asian monsoon, tropical perturbations, such as Inter-tropical Conversion Zone (ITCZ), typhoon activities, etc, and their interactions with topography, in the Northern and Southern Vietnam the rainy season also coincides with this period with rainfall amount from May to October contributes about 80% to the annual total rainfall, while Central Vietnam experiences dry and hot conditions due to the “foehn” phenomena. In this study the Regional Climate Model version 3.0 (RegCM3) has been employed to simulate the climate conditions over Vietnam and adjacent areas during the summer monsoon period. The main purpose is to examine and evaluate the RegCM3 seasonal predictability of circulation features and the two basic surface climate fields of monthly 2m-temperature and precipitation. 2. Experiment design As a first step towards the assessment of the RegCM3 performance in seasonal forecasting for Vietnam, numerical simulations for the summer monsoon period during 1996- 2005 driven driven by the NCEP/NCAR reanalysis (Kalnay et al., 1996) are carried out. RegCM3 is a primitive equation, hydrostatic, compressible, limited-area model with a sigma (σ) vertical coordinate, which was originally developed by Giorgi et al. (1993a, b) and then has undergone a number of improvements described in Giorgi et al. (1999), Pal et al. (2000). The dynamical core of RegCM3 is equivalent to the hydrostatic version of the Mesoscale Model version 5 (MM5). Surface processes are represented by the Biosphere–Atmosphere Transfer Scheme (BATS) and boundary layer physics are formulated following a nonlocal vertical diffusion scheme (Giorgi et al., 1993a). Radiative transfer is computed using the radiation package of the Community Climate Model version 3 (CCM3) (Giorgi et al., 1999). Three integrations using three convective schemes (1) Kuo (Anthes, 1977), (2) MIT– Emanuel (Emanuel, 1991: Emanuel and Zivkovic-Rothman, 1999), and (3) Grell (Grell, 1993) using the Arakawa-Schubert closure assumption (Arakawa and Schubert, 1974) are carried out, and named as Reg-Kuo, Reg-Emanuel, and Reg-Grell, respectively. The model runs with 18 vertical σ-levels, in which 6 levels are under 850 mb in the planetary boundary layer. The top layer is at 50 mb. The model domain centered at 11.5 o N and 108.0 o E with 145 and 105 grid-points in west-east and south-north directions, respectively, and with a horizontal resolution of 36 km for both directions. In this study the normal Mercator conformal projection is used. For each year of the 1996-2005 period, the model is integrated from 00UTC April 01 to 00UTC November 01 with the first one month (i.e. April) is used for spin-up time. Lateral boundary conditions are updated every 6 hours. Over the ocean, the model is forced by the Optimum Interpolation Sea Surface Temperature (OISST) data, which is available on a 1.0° × 1.0° grid mesh and provided by the National Oceanic and Atmospheric Administration (NOAA). For evaluating the quality of the simulations, model’s outputs are compared to various datasets. The simulated circulations over the interested domain are compared to the NCEP/NCAR reanalysis data. Precipitation and 2m air temperature are validated against the Climatic Research Unit (CRU) data, which is available over the continents at 0.5 o horizontal resolution (New et al. 1999, New et al. 2000). Moreover, monthly mean temperature and monthly accumulative precipitation are compared to observations obtained from 58 meteorological stations over Vietnam. 3 3. Results 3.1 Impact of different convective schemes As mentioned above, in order to study the sensitivity of RegCM3 to convective schemes, three experiments Reg-Kuo, Reg-Emanuel, and Reg-Grell were carried out. Firstly, we estimate the impact of these schemes to simulated mean sea level pressure (MSLP) and geopotential height (GH) fields. ICBC Reg-Kuo Reg-Emmanuel Reg-Grell Figure 1. Mean sea-level pressure (1996-2005) for May (top panel), July (middle panel) and October (bottom) of the Initial and Lateral Boundary Conditions (ICBC) compared to Reg- Kuo, Reg-Emanuel and Reg-Grell. Figure 1 shows MSLP averaged over the 1996-2005 period for May, July and October of the Initial and Lateral Boundary Conditions (ICBC) in comparison with the outputs of Reg- Kuo, Reg-Emanuel and Reg-Grell. Although MSLP charts are not really good for estimating wind direction and strength in the tropics, there are noticeable differences in this case. All the three experiments enhance the MSLP of about 4mb compared to ICBC in May and in October. Figure 1 also shows a MSLP decrease on the edge of the low pressure system in July, significantly with the MIT-Emanuel convective scheme. Mean GH at 850 mb for May, July and October is represented in Figure 2. Figures 3 and Figure 4 are the same as Figure 2, but for 500 and 200 mb, respectively. GH and wind field structures at higher altitude are less complicated than at the surface due to less terrain influences. The expriments are in more agreement with each other when the height increases. At 850 mb, Reg-Grell’s results for July and October have large difference with ICBC. Reg- Emmanuel and Reg-Kuo amplify the GH magnitude of about 5m while still well represent ICBC’s pattern. The Reg-Emanuel case is the most similar to ICBC. The wind field is reconstructured without any major change in direction. At 500 and 200 mb, differences 4 among the simulations are minor and insignificant. Reg-Emmanuel is again the most comparable integration to ICBC, especially for its wind field. ICBC Reg-Kuo Reg-Emanuel Reg-Grell Figure 2. Mean 850-mb geopotential height (1996-2005) for May (top panel), July (middle panel) and October (bottom panel) of ICBC compared to Reg-Kuo, Reg-Emanuel and Reg- Grell. The simulated wind vectors are superimposed (m/s). 5 ICBC Reg-Kuo Reg-Emanuel Reg-Grell Figure 3. As Figure 2, but for 500mb. ICBC Reg-Kuo Reg-Emanuel Reg-Grell Figure 4. As Figure 2, but for 200mb. 6 3.2 Validation of temperature and precipitation Simulated monthly mean temperature and precipitation are interpolated to station locations with longitude and latitude, respectively. 0 5 10 15 20 25 30 35 LAICHAU DIENBIEN SONLA MOCCHAU YENCHAU HAGIANG BACQUANG SAPA BAICHAY LANGSON TUYENQUANG YENBAI THAINGUYEN COTO LANG HOABINH PHULIEN NAMDINH NINHBINH BACHLONGVI THAIBINH HOIXUAN THANHHOA TUONGDUONG HATINH HUONGKHE KYANH TUYENHOA DONGHOI DONGHA VINH HUE ALUOI NAMDONG DANANG TRAMY QUANGNGAI BATO QUYNHON TUYHOA NHATRANG PHANRANG PHANTHIET PHUQUY KONTUM PLAYCU AYUNPA BUONMATHUOT DACNONG DALAT BAOLOC VUNGTAU CANTHO RACHGIA CAMAU CONDAO TRUONGSA PHUQUOC Station T av e ( o C) AnthesKuo Emanuel Grell OBS Figure 5. Monthly mean temperature from May to October during the 1996-2005 period at the 58 stations. The simulated and observed monthly mean temperature are represented in Figure 5. Temperature is generally underestimated by the model. The differences between the three integrations are barely discernible, from 0.5 to 1 o C. Temperature is rather well simulated at the stations in the North-West region (eg. Lai Chau, Dien Bien) and in the Western Highlands (eg. Kontum, Pleiku). A particular case is the Sapa station where temperature is overestimated. This is apparently demonstrated in Figure 6 with negative mean errors at almost all stations except Sapa. -12 -9 -6 -3 0 3 6 LAICHAU DIENBIEN SONLA MOCCHAU YENCHAU HAGIANG BACQUANG SAPA BAICHAY LANGSON TUYENQUAN YENBAI THAINGUYEN COTO LANG HOABINH PHULIEN NAMDINH NINHBINH BACHLONGV THAIBINH HOIXUAN THANHHOA TUONGDUO HATINH HUONGKHE KYANH TUYENHOA DONGHOI DONGHA VINH HUE ALUOI NAMDONG DANANG TRAMY QUANGNGAI BATO QUYNHON TUYHOA NHATRANG PHANRANG PHANTHIET PHUQUY KONTUM PLAYCU AYUNPA BUONMATHU DACNONG DALAT BAOLOC VUNGTAU CANTHO RACHGIA CAMAU CONDAO TRUONGSA PHUQUOC Station ME AnthesKuo Emanuel Grell Figure 6. Mean error of monthly mean temperature from May to October during the 1996- 2005 period at the 58 stations. Correlation coefficients of monthly mean temperature between observed and simulated data are relatively high, from 0.3 to 0.7. Low correlation stations are the ones in the coastal or island regions (eg. CoTo, BachLongVi) or those in the Southern part of Viet Nam (eg. Vung Tau, Can Tho). The Northern stations have considerably higher coefficients compared to other regions. -0.2 0 0.2 0.4 0.6 0.8 LAICHAU DIENBIEN SONLA MOCCHAU YENCHAU HAGIANG BACQUANG SAPA BAICHAY LANGSON TUYENQUANG YENBAI THAINGUYEN COTO LANG HOABINH PHULIEN NAMDINH NINHBINH BACHLONGVI THAIBINH HOIXUAN THANHHOA TUONGDUON HATINH HUONGKHE KYANH TUYENHOA DONGHOI DONGHA VINH HUE ALUOI NAMDONG DANANG TRAMY QUANGNGAI BATO QUYNHON TUYHOA NHATRANG PHANRANG PHANTHIET PHUQUY KONTUM PLAYCU AYUNPA BUONMATHU DACNONG DALAT BAOLOC VUNGTAU CANTHO RACHGIA CAMAU CONDAO TRUONGSA PHUQUOC Station Cor.Coef AnthesKuo Emanuel Grell Figure 7. Correlation coefficients between observed and simulated temperature from May to October during 1996-2005 at the 58 stations. 7 0 100 200 300 400 500 600 700 800 LAICHAU DIENBIEN SONLA YENCHAU MOCCHAU HAGIANG BACQUANG SAPA LANGSON TUYENQUA YENBAI THAINGUYE COTO BAICHAY HANOI HOABINH PHULIEN THAIBINH NAMDINH HOIXUAN NINHBINH BACHLONG THANHHOA TUONGDUO VINH HATINH HUONGKHE KYANH TUYENHOA DONGHOI DONGHA HUE ALUOI NAMDONG DANANG TRAMY QUANGNGAI BATO QUYNHON TUYHOA NHATRANG PHANRANG PHANTHIET KONTUM PLEIKU AYUNPA BMTHUOT DAKNONG DALAT BAOLOC PHUQUY VUNGTAU PHUQUOC CANTHO RACHGIA CAMAU TRUONGSA CONDAO Station P (mm) OBS AnthesKuo Emanuel Grell Figure 8. As Fig. 5, but for precipitation. Figure 8 shows significant differences among the numerical integrations for precipitation. Reg-Emanuel gives the closest result to the observed data while Reg-Kuo and Reg-Gre similarly underestimate precipiation. In the stations which have high annual rainfall amount, such as BacQuang, the simulation results are far from the observation. Figure 9 presents the negative mean errors of precipitation estimated by Reg-Kuo, Reg-Grell, and Reg- Emanuel. Low correlation coefficients for precipitation are obtained between observation and simulations, with the highest value is about 0.3 in Central Vietnam (Figure 9). -25 -20 -15 -10 -5 0 5 10 LAICHAU DIENBIEN SONLA YENCHAU MOCCHAU HAGIANG BACQUANG SAPA LANGSON TUYENQUA YENBAI THAINGUYE COTO BAICHAY HANOI HOABINH PHULIEN THAIBINH NAMDINH HOIXUAN NINHBINH BACHLONG THANHHOA TUONGDUO VINH HATINH HUONGKHE KYANH TUYENHOA DONGHOI DONGHA HUE ALUOI NAMDONG DANANG TRAMY QUANGNGAI BATO QUYNHON TUYHOA NHATRANG PHANRANG PHANTHIET KONTUM PLEIKU AYUNPA BMTHUOT DAKNONG DALAT BAOLOC PHUQUY VUNGTAU PHUQUOC CANTHO RACHGIA CAMAU TRUONGSA CONDAO Station ME AnthesKuo Emanuel Grell Figure 9. As Fig. 6, but for precipitation. -0.2 0 0.2 0.4 LAICHAU DIENBIEN SONLA YENCHAU MOCCHAU HAGIANG BACQUANG SAPA LANGSON TUYENQUA YENBAI THAINGUYE COTO BAICHAY HANOI HOABINH PHULIEN THAIBINH NAMDINH HOIXUAN NINHBINH BACHLONG THANHHOA TUONGDUO VINH HATINH HUONGKHE KYANH TUYENHOA DONGHOI DONGHA HUE ALUOI NAMDONG DANANG TRAMY QUANGNGAI BATO QUYNHON TUYHOA NHATRANG PHANRANG PHANTHIET KONTUM PLEIKU AYUNPA BMTHUOT DAKNONG DALAT BAOLOC PHUQUY VUNGTAU PHUQUOC CANTHO RACHGIA CAMAU TRUONGSA CONDAO Station Cor.Coef AnthesKuo Emanuel Grell Figure 10. As Fig. 7, but for precipitation. 4. Conclusions The RegCM3 model was used to simulate climate over Vietnam and the adjacent areas during the summer monsoon period. To test the sensitivity of RegCM3 to different convective schemes, three experiments, namely Reg-Kuo, Reg-Emanuel, and Reg-Grell were carried out. It is shown that the three experiments increase MSLP over the model domain. The simulations add more regional characteristics to MSLP pattern. The Reg-Emanuel’s ouputs are closer to ICBC than the other integrations. At high altitude, the differences among the experiments are 8 insignificant. When comparing to observations at the 58 meteorological stations of Vietnam, it is shown that RegCM3 systematically underestimates 2m-air temperature but the correlation coefficients are relatively high. Significant differences exist for precipitation simulated by the three experiments. Once again, Reg-Emanuel better represents the observation than Reg-Kuo and Reg-Grell. 5. References Annamalai H., J. Potemra, R. Murtugudde, J.P. McCreary (2005), Effect of Preconditioning on the Extreme Climate Events in the Tropical Indian Ocean. Journal of Climate, 18, 3450 3469. Duffy P. B., R.W. Arritt, J. Coquard, W. Gutowski, J. Han, J. Iorio, J. Kim, L.R. Leung, J. Roads, E. Zeledon (2006), Simulations of Present and Future Climates in the Western United States with Four Nested Regional Climate Models. Journal of Climate, 19, 873 895. 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Abstract In this study the Regional Climate Model version 3.0 (RegCM3) has been employed to simulate climate conditions over Vietnam and adjacent areas during the summer monsoon period, from 00UTC