The Weather Research and Forecasting (WRF) model is used for regional transport simulations of atmospheric carbon dioxide (referred to as WRF-CO 2 ) for the East Asia region at the horizontal resolution of 27 × 27 km. The domain extends from 18°N to 51°N in latitude and 101°E to 165°E in longitude, including the islands of Japan, South Korea, North Korea and a part of China. The simulation period is limited to the year 2002. To understand the role of surface fluxes and transport, we have simulated atmospheric CO 2 using 5 different CO 2 fluxes from ocean, fossil fuel and terrestrial biospheres at various horizontal resolutions, and at hourly to monthly time intervals. The model simulations are compared with observed time series at 9 stations, which are located under different ecological and climate

Simulation of carbon dioxide (CO 2 ) at hourly/weekly intervals and fine vertical resolution at the continental or coastal sites is challenging because of coarse horizontal resolution of global transport models. Here the regional Weather Research and Forecasting (WRF) model coupled with atmospheric chemistry is adopted for simulating atmospheric CO 2 (hereinafter WRF-CO 2 ) in nonreactive chemical tracer mode. Model results at horizontal resolution of 27 × 27 km and 31 vertical levels are compared with hourly CO 2 measurements from Tsukuba, Japan (36.05 • N, 140.13 o E) at tower heights of 25 and 200 m for the entire year 2002. Using the wind rose analysis, we find that the fossil fuel emission signal from the megacity Tokyo dominates the diurnal, synoptic and seasonal variations observed at Tsukuba. Contribution of terrestrial biosphere fluxes is of secondary importance for CO 2 concentration variability. The phase of synoptic scale variability in CO 2 at both heights are remarkably well simulated the observed data (correlation coefficient >0.70) for the entire year. The simulations of monthly mean diurnal cycles are in better agreement with the measurements at lower height compared to that at the upper height. The modelled vertical CO 2 gradients are generally greater than the observed vertical gradient. Sensitivity studies show that the simulation of observed vertical gradient can be improved by increasing the number of vertical levels from 31 in the model WRF to 37 (4 below 200 m) and using the Mellor-Yamada-Janjic planetary boundary scheme. These results have large implications for improving transport model simulation of CO 2 over the continental sites.

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The present work highlights response of a global spectral model T80L18 with respect to Indian summer monsoon rainfall (ISMR) during 8 years period of 1996-2003. The model performance is evaluated for day-1, day-3 and day-4 retrospective 24-hour accumulated rainfall forecasts from 0300 UTC to the next day 0300 UTC using in-situ rainfall observations of 4491 stations. The model performance is evaluated by assessing: (i) percentage departure and root mean square error (RMSE) of seasonal rainfall forecast, (ii) coefficient of variation (CoV) of seasonal rainfall forecast and observation, along with percentage departure of monthly rainfall forecast and (iii) model performance during a drought and a normal year of 2002 and 2003, respectively. Generally, it is noted that the T80L18 model underestimated high rainfall and overestimated low rainfall, however, with increasing forecast duration prediction over low rainfall areas improved. The model RMSE over central and western India is found to increase with increasing forecast duration; however, the same was found to decrease over Jammu and Kashmir. The CoV of day-1 rainfall forecast is found to be low over all India in comparison to the observed data. In the case of model performance evaluation during a drought and a normal year of 2002 and 2003, it is noted that the model produced higher rainfall over the rainfall deficit regions of observed distribution; whereas the heaviest observed rainfall region (>250 cm) is not well resolved by the model. In general, the T80L18 model performance is noted to be better over central India for mean seasonal rainfall prediction.