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Seasonal Climatology Studies for Tropical Regions

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Thursday, 10 January 2013
Seasonal Climatology Studies for Tropical Regions
Exhibit Hall 3 (Austin Convention Center)
Prabhat Kumar Thakur, Indian Instiute of Science, Bangalore, Karnataka, India; and R. Mittal, V. Saxena, T. George, L. A. Treinish, A. P. Praino, J. Cipriani, L. Dagar, A. G. Naim, H. Hassan, and S. A. Husain

Poster PDF (436.4 kB)

Rainforests form a critical part of the earth's ecosystem. Accurate climate modeling of the local regions of interest is a prerequisite to investigate the sensitivity of rainforests to climate change. In the current work, we consider the tropical forest region of Borneo and configure a regional climate model to investigate the associated seasonal climate characteristics. Such a regional climate model employs dynamic downscaling to unite the strengths of a global atmospheric climate model with a regional mesoscale model via nesting. Based on our modeling, we observe that the Borneo regional climate shows clear footprints of interaction between atmospheric circulation and dense forest cover. Our current work provides a detailed analysis of our observations and regional climate simulations, which should further enhance our understanding of Borneo, in particular, and tropical rainforests, in general.

The current work employs the WRF-ARW mesoscale model, initialized with the NCEP-NCAR reanalysis (1x1 degree resolution) global atmospheric data. The reanalysis data have been downscaled to horizontal resolutions of 30 and 10 km for the period of 1999 -2011. The model domain covers Borneo at 10-km resolution and most of Southeast Asia at 30-km resolution. The model results are compared against a network of rain gauges in the complex terrain of Brunei. Various regional climate features such as seasonal mean and extreme precipitation, distribution of precipitation rates, as well as precipitation intensity, frequency, and seasonality, are examined. The relationship between precipitation and surface temperature is also analyzed as a means to evaluate how well regional climate studies can be used to simulate surface hydrology. The relationships between precipitation and elevation are also analyzed as diagnostics of the impacts of surface topography and spatial resolution. Unfortunately, the effect of climate change on this area has not been well studied at a regional level in the past. Hence, we could only perform comparison with respect to the rain-gauge network available in Brunei.

Our comparison shows that the WRF model is able to add significant detail to the representation of precipitation and other surface variables such as surface temperature. In particular, we obtained more accurate simulations of the geographical distribution, wet day frequency and extreme values of precipitation due to better representation of the orography. Refining the resolution from 30 to 10 km further increases the prediction accuracy of the model, especially in case of precipitation. Our results indicate that the use of 10-km resolution is advantageous for forecasting regional climate conditions. We intend to further explore this direction and provide regional climate forecasts using the Community Atmospheric Model (CAM) global atmospheric model to force the WRF model simulations. The current study is a preliminary step towards understanding the long term effects on temperature and changes in the rainfall pattern due to climate change in the Borneo rainforests.

We have also investigated the effect of nudging on the regional climate simulations. Nudging has an important role to play as regional climate model (RCM) simulations tend to drift away from the driving fields. Nudging addresses this issue by retaining both the large-scale features (from the large-scale fields) and the small-scale features (from mesoscale models). Two nudging options (grid and spectral) available in WRF are used to downscale the reanalysis data. Our preliminary results indicate that with the appropriate choice of wave numbers, spectral nudging can outperform grid nudging in terms of balancing the simulation performance at varying scales.