ARCLIM: bioclimatic atlas of the terrestrial Arctic

crossref(2023)

引用 0|浏览1
暂无评分
摘要
<p>The warming of the Arctic and its consequences for the global climate system have become one of the strongest manifestations of human-induced climate change. Over the four decades, the Arctic has warmed three to four times faster than globally. In addition to the long-term trend in average temperatures, extreme weather events are becoming increasingly frequent causing disturbances to the Arctic terrestrial ecosystems.&#160;</p> <p>Many existing datasets primarily concentrate on seasonal precipitation and temperature at coarse spatial (10-100 km) and temporal (30-year average climatologies) resolutions forming the basis of current understanding of how Arctic ecosystems will respond to climate change. For this reason, the conventional datasets likely leave out many ecologically significant aspects of the Arctic climate relevant for biological or biogeochemical processes. For instance, snow cover duration, rain-on-snow events, or extreme wind events are known to be important variables for Arctic ecology that may not be adequately represented by the more widely used climate statistics.</p> <p>Here, we introduce a new dataset of bioclimatic indices relevant for investigating the changes of Arctic terrestrial ecosystems. The dataset, called ARCLIM, consists of several climate and event-type indices for the northern high-latitude land areas. The indices are calculated from the hourly ERA5-Land reanalysis data for 1950-2021 in a spatial grid of 0.1 degree (~9 km) resolution. We provide the indices in three subsets: (1) the annual values during 1950-2021; (2) the average conditions for the 1991-2020 climatology; and (3) temporal trends over 1951-2021.&#160;</p> <p>The 72-year time series of various climate and event-type indices draws a comprehensive picture of the Arctic bioclimate variability. We hope that the ARCLIM dataset opens new research opportunities aiming to better understand the impacts of climate change in Arctic terrestrial ecosystems.</p>
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要