This paper describes the construction of a 0.5° × 0.5° daily temperature dataset for the period of 1961–2005 over mainland China for the purpose of climate model validation. The dataset is based on the interpolation from 751 observing stations in China and comprises 3 variables: daily mean, minimum, and maximum temperature. The “anomaly approach” is applied in the interpolation. The gridded climatology of 1971–2000 is first calculated and then a gridded daily anomaly for 1961–2005 is added to the climatology to obtain the final dataset. Comparison of the dataset with CRU (Climatic Research Unit) observations at the monthly scale shows general agreement between the two datasets. The differences found can be largely attributed to the introduction of observations at new stations. The dataset shows similar interannual variability as does CRU data over North China and eastern part of the Tibetan Plateau, but with a slightly larger linear trend. The dataset is employed to validate the simulation of three extreme indices based on daily mean, minimum, and maximum temperature by a high-resolution regional climate model. Results show that the model reproduces these indices well. The data are available at the National Climate Center of China Meteorological Administration, and a coarser resolution (1° × 1°) version can be accessed via the World Wide Web.

A daily temperature dataset over China and its application in validating a RCM simulation

https://openresearch-repository.anu.edu.au/bitstream/1885/33581/2/01_McVicar_Spatially_distributing_monthly_2007.pdf

Spatially distributing monthly reference evapotranspiration and pan evaporation considering topographic influences

Probabilistic assessment of remote sensing-based terrestrial vegetation vulnerability to drought stress of the Loess Plateau in China

Climate surfaces are digital representations of climatic variables from a region in the planet estimated via geographical interpolation techniques. Climate surfaces have multiple applications in research planning, experimental design, and technology transfer. Although high-resolution climatologies have been developed worldwide, Mexico is one of the few countries that have developed several climatic surfaces. Here, we present an updated high-resolution (30 arc sec) climatic surfaces for Mexico for the average monthly climate period 1910–2009, corresponding to monthly values of precipitation, daily maximum, and minimum temperature, as well as 19 bioclimatic variables derived from the monthly precipitation and temperature values. To produce these surfaces we applied the thin-plate smoothing spline interpolation algorithm implemented in the ANUSPLIN software to nearly 5000 climate weather stations countrywide. As an additional product and unlike the previous efforts, we generated monthly standard error surfaces for the three climate parameters, which can be used for error assessment when using these climate surfaces. Our climate surface predicted slightly drier and cooler conditions than the previous ones. ANUSPLIN diagnostic statistics indicated that model fit was adequate. We implemented a more recent error assessment, a set of withheld stations to perform an independent evaluation of the model surfaces. We estimate the mean absolute error and mean error, with the withheld data and all the available data. Average RTGCV for monthly temperatures was of 1.26–1.12 °C and 24.67% for monthly precipitation, and a RTMSE of 0.48–0.56 °C and 11.11%. The main advantage of the surfaces presented here regarding the other three developed for the country is that ours cover practically the entire 20th century and almost the entire first decade of the 21st century. It is the most up to date high-resolution climatology for the country.

An update of high‐resolution monthly climate surfaces for Mexico

Monitoring the vegetation activity in China using vegetation health indices

Spline interpolation techniques are used to develop a gridded climate database for China at a resolution of 0.01° in latitude and longitude. A digital elevation model (DEM) was developed at the same resolution to improve the accuracy of interpolation based upon the general spatial dependence of climate on topography. Climate data for the period 1971–2000 from meteorological stations in China were used to develop thin-plate smoothing spline surfaces for monthly mean temperature and precipitation. A regularly gridded climate database was produced by coupling the spline surfaces with the underlying DEM. The summary statistics show interpolation errors for monthly temperatures varying within 0.42–0.83 °C and 8–13% for monthly precipitation. These estimates are superior to results produced by methods commonly used in China. The fine-resolution spatial climate database has many potential applications in natural resource management. For example, it can be used as a baseline for climate change studies, in which potential distributions of flora and fauna can be predicted under the impact of climate change and priority areas for biodiversity conservation can be identified. Copyright  2005 Royal Meteorological Society.

Spatial interpolation of monthly mean climate data for China

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Spatial interpolation of monthly mean climate data for China