Search Results (3)

The use of a compact set of climate change indexes enhances our understanding of the combined impacts of extreme climatic conditions. In this study, we developed the modified Climate Extremes Index (mCEI) to obtain unified information about different types of extremes. For this purpose, we calculated 10 different climate change indexes considering the temperature extremes, extreme precipitation, and moisture surplus and drought over the Europe–Mediterranean (EURO–MED) region for the 1979–2016 period. As a holistic approach, mCEI provides spatiotemporal information, and the high-resolution grid-based data allow us to accomplish detailed country-based and city-based analyses. The analyses indicate that warm temperature extremes rise significantly over the EURO–MED region at a rate of 1.9% decade⁻¹, whereas the cold temperature extremes decrease. Extreme drought has a significant increasing trend of 3.8% decade⁻¹. Although there are regional differences, extreme precipitation indexes have a significant increasing tendency. According to the mCEI, the major hotspots for the combined extremes are the Mediterranean coasts, the Balkan countries, Eastern Europe, Iceland, western Russia, western Turkey, and western Iraq. The decadal changes of mCEI for these regions are in the range of 3–5% decade⁻¹. The city-scale analysis based on urbanized locations reveals that Fes (Morocco), Izmir (Turkey), Marseille and Aix-en-Provence (France), and Tel Aviv (Israel) have the highest increasing trend of mCEI, which is greater than 3.5% decade⁻¹. Full article

Changes in climate extremes have a profound impact on vegetation growth. In this study, we employed the Moderate Resolution Imaging Spectroradiometer (MODIS) and a recently published climate extremes dataset (HadEX3) to study the temporal and spatial evolution of vegetation cover, and its responses to climate extremes in the arid region of northwest China (ARNC). Mann-Kendall test, Anomaly analysis, Pearson correlation analysis, Time lag cross-correlation method, and Least absolute shrinkage and selection operator logistic regression (Lasso) were conducted to quantitatively analyze the response characteristics between Normalized Difference Vegetation Index (NDVI) and climate extremes from 2000 to 2018. The results showed that: (1) The vegetation in the ARNC had a fluctuating upward trend, with vegetation significantly increasing in Xinjiang Tianshan, Altai Mountain, and Tarim Basin, and decreasing in the central inland desert. (2) Temperature extremes showed an increasing trend, with extremely high-temperature events increasing and extremely low-temperature events decreasing. Precipitation extremes events also exhibited a slightly increasing trend. (3) NDVI was overall positively correlated with the climate extremes indices (CEIs), although both positive and negative correlations spatially coexisted. (4) The responses of NDVI and climate extremes showed time lag effects and spatial differences in the growing period. (5) Precipitation extremes were closely related to NDVI than temperature extremes according to Lasso modeling results. This study provides a reference for understanding vegetation variations and their response to climate extremes in arid regions. Full article

Climate extreme indices (CEIs) are important metrics that not only assist in the analysis of regional and global extremes in meteorological events, but also aid climate modellers and policymakers in the assessment of sectoral impacts. Global high-spatial-resolution CEI datasets derived from quality-controlled historical observations, or reanalysis data products are scarce. This study introduces a new high-resolution global gridded dataset of CEIs based on sub-daily temperature and precipitation data from the Global Land Data Assimilation System (GLDAS). The dataset called “CEI_0p25_1970_2016” includes 71 annual (and in some cases monthly) CEIs at 0.25 ${}^{\circ }$ × 0.25 ${}^{\circ }$ gridded resolution, covering 47 years over the period 1970–2016. The data of individual indices are publicly available for download in the commonly used Network Common Data Form 4 (NetCDF4) format. Potential applications of CEI_0p25_1970_2016 presented here include the assessment of sectoral impacts (e.g., Agriculture, Health, Energy, and Hydrology), as well as the identification of hot spots (clusters) showing similar historical spatial patterns of high/low temperature and precipitation extremes. CEI_0p25_1970_2016 fills gaps in existing CEI datasets by encompassing not only more indices, but also by being the only comprehensive global gridded CEI data available at high spatial resolution. Full article