A long-term 0.01° surface air temperature dataset of Tibetan Plateau

Abstract

The surface air temperature (T_a) dataset of the Tibetan Plateau is obtained by downscaling the China regional surface meteorological feature dataset (CRSMFD). It contains the daily mean T_a and 3-hourly instantaneous T_a. This dataset has a spatial resolution of 0.01°. Its time range for surface air temperature dataset is from 2000 to 2015. Spatial dimension of data: 73°E–106°E, 40°N–23°N. The T_a with a 0.01° can serve as an important input for the modeling of land surface processes, such as surface evapotranspiration estimation, agricultural monitoring, and climate change analysis.

Specifications Table

Subject area	Earth and Planetary Sciences
More specific subject area	Atmospheric Science, Earth-Surface Processes.
Type of data	image
How data was acquired	Downscaling model
Data format	Raw and examples of analyzed data
Experimental factors
Experimental features
Data source location	School of Resources and Environment, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu, China
Data accessibility	This data has a high temporal resolution and a medium spatial resolution; thus, this dataset is huge. In order to maximize the sharing of this data, we can only provide a link to download this dataset.
	Resource link:https://pan.baidu.com/s/1SaD3gafyGJRYXjW8k8Cs7g
Related research article	L. Ding, J. Zhou, X. Zhang, S. Liu, and R. Cao, “Downscaling of surface air temperature over the Tibetan Plateau based on DEM,” Int. J. Appl. Earth Obs. Geoinformation, vol. 73, pp. 136–147, 2018.
	https://doi.org/10.1016/j.jag.2018.05.017

Value of the Data

• •It can contribute to better modeling the radiation balance and energy budget and water cycle over the Tibetan Plateau.
• •It can serve as an important input parameter for the modeling of land surface processes, such as surface evapotranspiration estimation.
• •It can provide long-term T_a dataset with acceptable accuracy and medium spatial resolution for climate change study.

1. Data

As the highest plateau in the world, the Tibetan Plateau has the largest glaciers except the Arctic and Antarctic. Due to complex natural environment, the Tibetan Plateau has significant impacts on climate change of the surrounding areas and even the whole world. Because of its special geographical location and topography, the radiation balance and energy budget and water cycle examinations of the Tibetan Plateau are particularly important. Thus, the scientific communities is requiring a long-term T_a dataset with acceptable accuracy and medium spatial resolution.

We use the China regional surface meteorological feature dataset (CRSMFD) [1], [2] as the basis dataset. We develop a practical method to downscale the CRSMFD from 0.1° to 0.01°. The temporal resolution of this dataset is consistent with CRSMFD. It have better consistency with the ground measured T_a than original CRSMFD in Tibetan Plateau. It has higher spatial resolution than most of the current long-term T_a dataset for the Tibetan Plateau. In addition, T_a with a 0.01° resolution can reflect more spatial details of T_a when compared with the original CRSMFD. The T_a at some time is shown as an example in Fig. 1, and the 0.01° T_a of local areas is shown as an example in Fig. 2 (area A and area B are shown in Fig. 1). Thus, this dataset is able to meet the ever-increasing demand for related studies and applications.

Fig. 1.

Examples of the 0.01° T_a data over the Tibetan Plate.

Fig. 2.

Subsets of the 0.01° T_a data.

2. Experimental design, materials, and methods

The linear relationship between T_a and its influencing factors, T_a can be expressed as:

$$T_{a,\text{daily}}=f_{\mathrm{daily}}\left(H,X_1\right)=\lambda H+a{X}_1+c$$ (1)

$$T_{a,\text{ins}}=f_{\mathrm{ins}}\left(H,X_1,X_2\right)=\lambda H+a{X}_1+b{X}_2+c$$ (2)

where T_a,daily and T_a,ins are the daily mean and instantaneous T_a in K, respectively; f_daily and f_ins are the statistical functions for the daily mean T_a and instantaneous T_a, respectively; H, X₁, X₂ are the elevation, latitude, and longitude, respectively; λ, a, and b are the corresponding coefficients; and c is the intercept. It is evident that λ is the lapse rate (LR) of T_a [3], [4]. Note that the longitude is not contained in Eq. (1) due to its ignorable ability in explaining daily mean T_a.

Based on Eqs. (1), (2), the flowchart of the proposed method for T_a downscaling is shown in Fig. 3. The first stage for T_a downscaling is to calculate LR. The DEM data at 90-m is aggregated to 0.01°. The mean elevation of the 10 × 10 pixels is calculated and used as the elevation of the pixel at 0.1° that containing these 10 × 10 pixels. The spatial distribution of LR can be divided into eight regions, i.e. Region 1: 73–90°E, 35–40°N; Region 2: 90–100°E, 35–40°N; Region 3: 100–105°E, 35–40°N; Region 4: 78–95°E, 27–35°N; Region 5: 95–100°E, 27–35°N; Region 6: 100–107.5°E, 30–35°N; Region 7: 100–105°E, 25–30°N; Region 8: 100–105°E, 23–25°N [5]. In this division scheme, each region has similar regional climatic characteristics and a range of elevation changes. This division scheme is utilized by this method. To better address the intra-annual variations of LR, the LR values of instantaneous T_a at every 3 h and the daily mean T_a on every day are calculated.

Fig. 3.

Flowchart of the method for T_a downscaling.

The second stage is to determine and optimize the initial value of T_a at the target resolution. The T_a value at the native resolution (i.e. 0.1°) is taken as the initial value of the pixel at the target resolution (i.e. 0.01°). At the target resolution, a moving window approach is employed to refine the initial T_a of the central pixel. For each pixel at the target resolution, the window size is set to 11 × 11 pixels and the current pixel under consideration is the center of the window. If the current pixel is on the edge of the image, the window is not complete and the existing pixels are selected. Pixels with valid T_a and elevation in the moving window are selected as valid pixels [6]. Then the mean T_a of the valid pixels in the moving window is calculated as the optimized T_a of the central pixel as follows:

$${T\prime }_{\mathrm{a}}=\frac{\sum _{i=1}^m{T}_{a-\text{initial}}\left(i\right)}{m}$$ (3)

where T_a′ is optimized initial value of the T_a; T_a-initial(i) is the initial T_a the i-th pixel at the target resolution within the window; and m is the number of valid pixels in the window.

The third stage is to determine the final value of T_a at the target resolution. According Eqs. (1), (2), the T_a difference between the central pixel and the mean T_a of moving window can be expressed as:

$$\Delta T_{a,\text{daily}}=\lambda (H-H_{\mathrm{win}})+a(X_{1-i}-X_{1-\mathrm{win}})$$ (4)

$$\Delta T_{a,\text{ins}}=\lambda (H-H_{\mathrm{win}})+a(X_{1-i}-X_{1-\mathrm{win}})+b(X_{2-i}-X_{2-\mathrm{win}})$$ (5)

where ΔT_a,daily and ΔT_a,ins are daily mean T_a difference and instantaneous T_a difference in K; H, X_1−i and X_2−i are the elevation, latitude, and longitude of the central pixel of the moving window, respectively; H_win, X_1−win and X_2−win are the mean elevation, latitude, and longitude of the moving window, respectively. X_1−i and X_1−win, X_2−i, and X_2−win can be considered to be approximately equal. Thus, Eqs. (4), (5) can be simplified as:

$$\Delta T=\lambda (H-H_{\mathrm{win}})$$ (6)

where ΔT is the T_a difference in K.

Then the final T_a of the central pixel is:

$$T_{\mathrm{a}}=T_{\mathrm{a}}^{\prime }+\Delta T$$ (7)

Finally, the 0.01° T_a data of Tibetan Plateau was obtained by this downscaling method.

Transparency document. Supporting information

Supplementary material

.