The Common Land Model (CLM) has been validated by observation experiments over
different land surfaces in various climate zones throughout the world. These experiments
have shown that CLM simulates the characteristics of land-atmosphere interactions over
different land surfaces, except in the East Asian monsoon zone where complex land surface
conditions exist. China lies on this East Asian monsoon zone which consists of complex
terrain, various vegetation types, and specific land surface conditions, and experiences
frequent drought and flood disasters. It is important to study how varying land surfaces
affect the interaction of energy, mass, and momentum between land and atmosphere.
Owing to poor simulation of soil moisture by most land surface models, CLM has chosen
to simulate the distribution of soil moisture over China. Meanwhile, station-observed soil
moisture, drought monitoring data from a pole orbit meteorology satellite, and Advanced
Microwave Scanning Radiometer-EOS (AMSR-E) remote-sensed soil moisture are used to
verify the capability of CLM simulation, especially for surface and soil moisture at a 20 cm
depth. The results show that the surface soil moisture distribution and variation trend of
CLM simulation coincides with pole orbit meteorology satellite monitoring and AMSR-E,
and that soil moisture at a 20 cm depth coincides with station observation products from the
National Climate Center. It also illustrates that CLM can reasonably simulate the
distribution and variation of soil moisture over China. It is meaningful to study the climate
response of the lack of soil moisture on soil moisture data.
Key words: CLM, soil moisture, drought, AMSR-E
Taking Kenli County of the Yellow River delta as the study area and using digital satellite remote sensing
techniques, this paper explores the cultivated land changes and their correspondent driving forces. An interactive
interpretation and manual modification procedure was carried out to acquire cultivated land information. A classification
result overlay and the visual change detection method supported by land use map were employed to conduct the
cultivated land change detection. Based on the change detection results and a spatial analysis between the cultivated land
and the related natural and socio-economic factors, the driving force for the cultivated land changes of the study area
were determined. The results show that the cultivated land in Kenli County decreased by 5321.8 ha from 1987 to 1998,
i.e. 483.8 ha per year, which occurred mainly in the central paddy field region and the northeast dry land region. Adverse
human activities, soil salinization and water deficiency consist the driving force that caused the cultivated land changes.
In this paper, we explored the trends of the summer whole layer moisture budget and precipitation during
1961 to 2005 and possible correlations between them using linear regression method in the Yangtze River basin, China.
The results indicate that: 1) the summer moisture budget and precipitation are in significant increasing trend with p<0.05
in the middle and lower Yangtze River basin. The summer moisture budget and precipitation are also increasing, but are
not significant at >95% confidence level on the upper Yangtze basin; 2) The annual moisture budget show significant
decreasing trend in the Yangtze basin and this decreasing trend is not significant at >95% confidence level with p<0.05.
However, the annual precipitation is increased in mid-lower while decreased in the up Yangtze basin, but these trends are
not significant at >95% confidence level; 3) This research sheds light on the changing features of summer precipitation
and possible correlation with discharge and moisture budget in the Yangtze River basin since that flood hazards mostly
occurred to the summer, which will be of great scientific merits in further understanding the changes of the summer
precipitation, related impacts on discharge change and possible causes. The research results will be helpful for flood
control and fluvial management in the Yangtze River basin under the changing climate.
Diurnal variation of solar radiation at surface is of importance to data assimilation, weather and climate model
assessment. However, the shortage of solar radiation data has limited full use of other meteorological data. Solar
radiation at surface can not be simply calculated by interpolation in any time interval because it is heavily influenced by
solar hour angle, cloud, water vapor and aerosols etc., which brings great troubles to model applications. This paper
presents a method to compute mean solar radiation at surface in any time interval and develops a data set of hourly mean
solar radiation that can be used to assess models by use of NCEP 6-hourly mean of downward solar radiation flux at
surface. Also, while comparing to measured hourly mean of solar radiation, results show that the calculated hourly mean
solar radiation agrees closely with observation in numerical value and variation trend, which illuminates that the method
is efficient. The calculated hourly-mean solar radiation reflects the diurnal variation all over the world and it can be used
as land model forcing, It is helpful to simulation, validation and assessment of the weather and climate model and can
make up the shortage of measured solar radiation data.
In the first part of this paper, a 3DVar Land Data Assimilation Scheme (LDAS) is presented. With virtue of this land data assimilation system, this part of the paper demonstrates the results and error analysis of assimilating air temperature data observed at various meteorological stations in China into the output of ECMWF ERA-40. The air temperature distribution of sparse observation zones is obtained, which shows the validity of the assimilation procedure. The 3DVar LDAS can greatly improve the ECMWF background estimates with the high quality observations of air temperature from the Chinese meteorological stations. By comparing the assimilated air temperature field and the ECMWF background field to the observations, the assimilation outputs have better agreement with the air temperature variation trend than the ECMWF background. Another advantage of the assimilated result is that it can describe the extreme air temperature more accurately.
Land surface states have significant control to the water and energy exchanges between land surface and the atmosphere.
Thus land surface information is crucial to the global and regional weather and climate predictions. China has built
abundant meteorological stations that collect land surface data with good quality for many years. But applications of these data in their numerical weather and climate prediction models are quite low efficient. To take the advantages of land surface data in numerical weather and climate models, we have developed a three dimension variational (3DVar) Land Data Assimilation Scheme (LDAS). In Part 1 of this paper, we present the mathematical design of the 3DVar LDAS. By assimilating a single point observational datum into a background setup, the LDAS is tested to demonstrate its capability and usage. In the other part of this paper, we will demonstrate the results and error analysis of assimilating China's air temperature observational data of the meteorological stations into ECMWF's model background using the 3DVar Land Data Assimilation Scheme.