Climate-related changes have already been observed at various spatial and temporal scales, along with frequent extreme climate events and emerging issues in great complexity. Meanwhile, the impacts of climate change are expected to become increasingly more severe for more people and places as the amount of warming increases. However, great challenges and uncertainties exist in understanding climate change and its complex impacts, largely due to the limited availability and compatibility of large-scale data. Climate change research is based on multiple parameters, using global datasets that cover long time periods and that have increasingly finer temporal and spatial resolutions. As data volumes continuously expand and more parameters are considered in climate modeling and analysis, traditional spatial data management and analysis is reaching a bottleneck. There is, therefore, an urgent need to make use of big data and cloud computing to allow more integrated investigations and a synthesis of the complex changes in the climate in the context of Earth systems. Big Earth data empowered by Earth observation technology has great potential for use in the integrated analysis of climate change at global and regional scales and for supporting an informed response by human society to common challenges such as climate warming, extreme climate and weather, and climate-related disaster risks.
Figure 1. Reliable and integrated spatial datasets are essential for understanding complex changes in the climate and their interaction with terrestrial ecosystems | Photo: Gensuo Jia.
This special issue includes six original research and technical papers that cover a wide range of applications of big Earth data related to the understanding of various aspects of climate change, including dataset development and the application of these data to extreme high temperatures, drought, dust storms, and ecosystem respiration and evapotranspiration at global, regional, and national scales.
As the mean land surface air temperature has increased at almost twice the rate of the mean global warming rate, extreme heat events have become more frequent and more intense over major land areas (Jia et al., Citation2019), especially the vast Eurasian and African continental areas. Long-term and reliable spatial datasets are key to effectively capturing the details of large-scale heatwaves on which to base informed actions. This special collection includes a dataset of high-temperature extremes over Eurasia and Africa for the period 1979–2018 at a spatial resolution of 0.5°, produced by Yang and Zhang (Citation2020). Three main indices – hot days, hot nights, and combined hot extremes – are included in the data products. These data are freely available and users can select high-temperature extremes with appropriate thresholds according to their specific research and application purposes.
Drought is another major extreme climate event that compromises the economy and also livelihoods in many developing countries in Eurasia and Africa. Also included in this special issue is a paper by Zhong, Hua, and Yan (Citation2020), who extracted a basic gridded dataset of the drought events that occurred between 1950 and 2015; a second-level dataset (a drought risk dataset) that describes the drought hazard, exposure, and vulnerability for the period 2000–2015 is also presented. Data for analysing drought events and their impacts at various scales are available for analysis.
More specific datasets related to the impacts and consequences of extreme climate and weather events have been developed and presented at national level. One of the papers in this issue (Gao et al., Citation2020) focuses on risk assessment and an integrated analysis of extreme events and impacts across China under different future climate change scenarios that assume warming of 1.5°C or 2.0°C. Another paper (Kamal, Chenglai, & Lin, Citation2020) examines interannual variations of dust activity in western Iran and the possible mechanism behind dust activity under climate change, and highlights the key roles of wind speed, precipitation, and soil moisture.
The terrestrial biosphere and atmosphere interact through a series of feedback loops (). Vegetation and land-cover types are largely determined by the dominant climate regime and are sensitive to climate change and variability. Variability in terrestrial vegetation growth and phenology can modulate fluxes of water and energy to the atmosphere, and thus affect the climatic conditions that in turn regulate vegetation dynamics (Jia et al., Citation2019). Major spatial heterogeneity exists in interactions between climate change and ecosystem dynamics, and, therefore, more reliable high-resolution datasets are essential for capturing the spatio-temporal patterns of interactive changes in the climate and terrestrial ecosystems. In this issue, a global terrestrial ecosystem respiration dataset (2001–2010) estimated using MODIS land-surface temperature and vegetation indices (Ai et al., Citation2020) is presented. This paper demonstrates the distribution and variability of ecosystem respiration, a key parameter in carbon budget and greenhouse gas (GHG) assessment and also describes the development of a global land evapotranspiration dataset that combines field observations, modeling and satellite remote sensing data (Zhang et al., Citation2020) together with details of estimates derived from this dataset. This dataset was developed with the aim of investigating the water and energy fluxes between the vegetation canopy and the atmosphere, which are key parameters in the biophysical climate effects of land surface processes.
Disclosure statement
No potential conflict of interest was reported by the author.
References
- Ai, J., Xiao, S., Feng, H., Wang, H., Jia, G., & Hu, Y. (2020). A global terrestrial ecosystem respiration dataset (2001-2010) estimated with MODIS land surface temperature and vegetation indices. Big Earth Data, 4(2), 142–152.
- Gao, J., Liu, L., & Wu, S. (2020). Hazards of extreme events in china under different global warming targets. Big Earth Data, 4(2), 153–174.
- Jia, G., Shevliakova, E., Artaxo, P., De Noblet-Ducoudré, N., Houghton, R., House, J., … Verchot, L. (2019). Land–climate interactions. In IPCC Special report on climate change and land (SRCCL). Geneva: Intergovernmental Panel on Climate Change.
- Kamal, A., Chenglai, W., & Lin, Z. (2020). Interannual variations of dust activity in western Iran and their possible mechanisms. Big Earth Data, 4(2), 175–190.
- Yang, Z., & Zhang, J. (2020). Dataset of high temperature extremes over the major land areas of the Belt and Road for 1979-2018. Big Earth Data, 4(2), 128–141.
- Zhang, J., Bai, Y., Yan, H., Guo, H., Yang, S., & Wang, J. (2020). Linking observation, modelling and satellite-based estimation of global land evapotranspiration. Big Earth Data, 4(2), 94–127.
- Zhong, L., Hua, L., & Yan, Z. (2020). Datasets of meteorological drought events and risks for the developing countries in Eurasia. Big Earth Data, 4(2), 191–223.