Gridded Climate Datasets Research Articles

Trends in vegetation activity and their climatic correlates: China 1982 to 1998

We combined a satellite-derived Leaf Area Index (LAI) dataset and a gridded climate dataset to analyse trends in vegetation activity and their correlation with climate variability in China between 1982 and 1998. Vegetation activity over the growing season increased 11.03% in China during the 17-year period, which is broadly consistent with the greening trend in the northern high latitudes in Eurasia and North America shown in previous studies. Approximately 99×106 ha of croplands and 35×106 ha of forest exhibited significant upward trends in growing season LAI, and accounted for 53% and 19% of the total vegetated area with greening trends, respectively. Temperature was the leading climatic factor controlling greening patterns. However, trends in agricultural practices, such as increased use of high-yield crops and application of chemical fertilizers, along with land-use changes such as afforestation and reforestation probably made a greater contribution to the greening trend than temperature. Increased vegetation activity in forests suggests an increasing carbon stock in forest biomass in China, which supports previous studies based on satellite sensor data and forest inventory data.

Spatial modeling of winter temperature and precipitation in Arizona and New Mexico, USA

The development of a statistical modeling technique suitable for producing mean and interannual gridded climate datasets for a topographically varying domain is undertaken. Stepwise regression models at 1 × 1 km resolution are generated to estimate mean winter temperature and precipitation for the Southwest United States for the years 1961–1990. Topographic predictor variables are used to explain spatial variance in the datasets. Kriging and inverse distance weighting interpolation algorithms are utilized to account for model residuals. The final regression models show a high degree of explained variance for temperature (R2 = 0.98, mean bias error [MBE] = –0.15°C, rootmean-squared error [RMSE] = 0.74°C) and a moderate degree of explained variance for precipitation (R2 = 0.63, MBE = –1.4 mm, RMSE = 27.0 mm). Several smaller-scale precipitation regression models are developed for comparison to the domain-wide model, but do not show marked accuracy improvements. Observed values of winter temperature and precipitation from the years 1961–1999 are compared to the 30 yr modeled means, and the differences are interpolated using kriging (temperature) and inverse distance weighting (precipitation). The result is a 39 yr time series of maps and datasets of winter temperature and precipitation at 1 × 1 km resolution for the Southwest United States.