Báo cáo nghiên cứu khoa học " On the seasonal prediction of surface climate over Vietnam using Regional Climate Model (RegCM3) " ppt

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,

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

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)

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

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

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 11.5oN and 108.0oE 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

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 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

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, 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

among the simulations are minor and insignificant Reg-Emmanuel is again the most comparable integration to ICBC, especially for its wind field

Figure 2 Mean 850-mb geopotential height (1996-2005) for May (top panel), July (middle

panel) and October (bottom panel) of ICBC compared to Kuo, Emanuel and

Reg-Grell The simulated wind vectors are superimposed (m/s)

ICBC Reg-Kuo Reg-Emanuel Reg-Grell

Figure 3 As Figure 2, but for 500mb

Figure 4 As Figure 2, but for 200mb

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

35

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

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

-30

6

Station

ME

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

Station

Cor.Coef

Figure 7 Correlation coefficients between observed and simulated temperature from May to

October during 1996-2005 at the 58 stations

100

200

300

400

500

600

700

800

Station

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 Kuo, 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

-15

-50

10

Station

ME

Figure 9 As Fig 6, but for precipitation

-0.2

0

0.2

0.4

Station

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

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

Giorgi F., M.R Marinucci, and G.T Bates (1993a): Development of a Second-Generation Regional Climate Model (RegCM2) Part I: Boundary-Layer and Radiative Transfer

Processes Mon Wea Rev., 121, 2791-2813

Giorgi F., M.R Marinucci, and G.T Bates (1993b): Development of a second-generation regional climate model (RegCM2) Part II: Convective processes and assimilation of

boundary conditions Mon Weath Rev., 121, 2814– 2832

Kloizbach P.J and W M Gray (2003): Forecasting September Atlantic Basin Tropical

Cyclone Activity Weather and Forecasting, 18, 1190-1128

Krishnamurti T.N., L Stefanova, A Chakraborty, T.S.V Kumar, S Cocke, D Bachiochi and

B Mackey (2001), Seasonal Forecasts of precipitation anomalies for North American

and Asian Monsoons FSU Report# 01-07, April

Stockdale, T.N (2000): An overview of techniques for seasonal forecasting Stochastic

Environmental Research and Risk Assessment, 14, 305-318

Doblas-Reyes, F.J., R Hagedorn and T.N Palmer (2006): Developments in dynamical

seasonal forecasting relevant to agricultural management Climate Research , 33, 19-26

Cane, M A., S E Zebiak and S C Dolan (1986): Experimental forecasts of El Nino Nature,

321, 827-832

Wang Shaowu, Zhu Jinhong (2001): A review on seasonal climate prediction Advances in

Atmospheric Sciences, 18 (2), pg 197-208

Giorgi F., and Shields C (1999): Tests of precipitation parameterizations available in latest

version of NCAR regional climate model (RegCM) over continental United States, J Geophys Res., Vol 104, pp 6353-6375.

Pal, J S., E E Small, and E A B Eltahir (2000): Simulation of regional-scale water and energy budgets: Representation of subgrid cloud and precipitation processes within

RegCM J Geophys Res.-Atmospheres, 105(D24), 29,579–29,594

Anthes, R A., (1977): A cumulus parameterization scheme utilizing a one-dimensional cloud

model, Mon Wea Rev., 105, 270–286

Emanuel, K A (1991): A scheme for representing cumulus convection in large scalemodels,

J Atmos Sci., 48(21), 2313–2335

Emanuel, K A., and M Zivkovic-Rothman (1999): Development and evaluation of a

convection scheme for use in climate models, J Atmos Sci., 56, 1766–1782

Grell, G (1993): Prognostic evaluation of assumptions used by cumulus parameterizations, Mon Wea Rev., 121, 764–787

Arakawa A, Schubert WH (1974) Interaction of a cumulus cloud ensemble with the

large-scale environment, Part I J Atmos Sci 31:674-701