CHELSA-BIOCLIM+ A novel set of global climate-related predictors at kilometre-resolution

A multitude of physical and biological processes on which ecosystems and human societies depend are governed by climatic conditions. Understanding how these processes are altered by climate change is central to mitigation efforts. Based on mechanistically downscaled climate data, we developed a set of climate-related variables at yet unprecedented spatiotemporal detail as a basis for environmental and ecological analyses. We created gridded data for near-surface relative humidity (hurs), cloud area fraction (clt), near-surface wind speed (sfcWind), vapour pressure deficit (vpd), surface downwelling shortwave radiation (rsds), potential evapotranspiration (pet), climate moisture index (cmi), and site water balance (swb), at a monthly temporal and 30 arcsec spatial resolution globally starting 1980 until 2018. At the same spatial resolution, we further estimated climatological normals of frost change frequency (fcf), snow cover days (scd), potential net primary productivity (npp), growing degree days (gdd), and growing season characteristics for the periods 1981-2010, 2011-2040, 2041-2070, and 2071-2100, considering three shared socioeconomic pathways (SSP126, SSP370, SSP585) and five Earth system models. Time-series variables showed high accuracy when validated against observations from meteorological stations. Climatological normals were also highly correlated to observations although some variables showed notable biases, e.g., snow cover days (scd). Together, the data sets presented here allow improving our understanding of patterns and processes that are governed by climate, including the impact of recent and future climate changes on the world’s ecosystems and associated services to societies.生态系统与人类社会赖以存续的众多物理及生物过程，均受气候条件调控。明晰此类过程如何受气候变化影响，是气候减缓工作的核心要义。本研究基于动力降尺度（mechanistically downscaled）气候数据，构建了一套时空分辨率前所未有的气候相关变量集，可为环境与生态分析提供支撑。本数据集涵盖近地表相对湿度（near-surface relative humidity, hurs）、云量分数（cloud area fraction, clt）、近地表风速（near-surface wind speed, sfcWind）、水汽压亏缺（vapour pressure deficit, vpd）、地表下行短波辐射（surface downwelling shortwave radiation, rsds）、潜在蒸散量（potential evapotranspiration, pet）、气候湿度指数（climate moisture index, cmi）以及站点水分平衡（site water balance, swb），以月为时间分辨率、30角秒为空间分辨率，覆盖全球1980年至2018年的格点数据。基于相同的空间分辨率，本研究还针对1981-2010年、2011-2040年、2041-2070年及2071-2100年四个时段，结合三种共享社会经济路径（SSP126、SSP370、SSP585）与五个地球系统模型，估算了霜冻变化频率（frost change frequency, fcf）、积雪日数（snow cover days, scd）、潜在净初级生产力（potential net primary productivity, npp）、生长度日（growing degree days, gdd）以及生长季特征的气候基准值。经气象站观测数据验证，本数据集的时序变量具备较高精度。尽管部分变量存在显著偏差（如积雪日数scd），但气候基准值仍与观测数据高度相关。综上，本数据集可助力深化对气候调控的格局与过程的认知，包括近期及未来气候变化对全球生态系统及其为人类社会提供的相关服务的影响。