Geography Reference
In-Depth Information
Mongolia's grassland degraded seriously, which may lead to the increase of carbon
dioxide and other greenhouse gasses. Subsequently, it has driven the temperature
rise in Mongolia and redistributed the precipitation, destabilized the natural eco-
systems, and even threatened the human food supply and living environment.
7.4.1 Data and Methodology
7.4.1.1 Underlying Surface Data and Atmospheric Forcing Data
There are two types of input data in WRF model, the underlying land surface data
and atmospheric forcing data. An advanced very high resolution radiometer
(AVHRR) grid data with resolution of 1 km 9 1 km of the United States Geo-
logical Survey's (USGS) classification system spanning a 12-month period (April
1992-March 1993, henceforth, 1993) is used as the baseline underlying surface
data in this study. The predicted land conversion data with 0.5 9 0.5 resolution
from 2010 to 2050 are developed by the Asia-Pacific Integrated Model (AIM)
modeling team at the National Institute for Environmental Studies (NIES), Japan.
This data is used to project the land surface data in the future utilized in the WRF
model. To investigate the effects of future grassland degradation on climate, we
only focused the grassland conversion of this data, though change information of
all kinds of land use and land cover types are available. Supposing other types of
land use changes are not considered in this research, the newly grassland con-
version pixels derived from AIM (RCP 6.0) were overlaid to the land surface map
in the baseline. Thereafter, both of these two major underlying land surface data
were transformed to grid data with the resolution of 50 km 9 50 km by resam-
pling. Model outputs under RCP 6.0, such as air temperature, specific humidity,
sea level pressure, eastward wind, northward wind, and geo-potential height from
2010 to 2050, were used as the atmospheric forcing dataset in the WRF model.
7.4.1.2 Experimental Design
WRF model based on the Eulerian mass solver was used to investigate the tem-
perature and precipitation changes driven by future grassland degradation in this
study. Two sets of tests (control test and simulation test) were designed, the land
surface in the baseline and the predicted underlying land surface data were used in
these two tests from 2010 to 2050, respectively. The effects of future grassland
degradation on climate can be measured by the difference of these two series of
simulated results.
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