No abstract

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids thus fail to reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions are controlled and most terrestrial species reside. Here we provide global maps of soil temperature and bioclimatic variables at a 1-km² resolution for 0-5 and 5-15 cm depth. These maps were created by calculating the difference (i.e., offset) between in-situ soil temperature measurements, based on time series from over 1200 1-km² pixels (summarized from 8500 unique temperature sensors) across all of the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding 2 m gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (3.6 ± 2.3°C warmer than gridded air temperature), whereas soils in warm and humid environments are on average slightly cooler (0.7 ± 2.3°C cooler). The observed substantial and biome-specific offsets underpin that the projected impacts of climate and climate change on biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining global gaps by collecting more in-situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.

A radar-based automated technique for the identification of tropical precipitation was developed to improve quantitative precipitation estimation during extreme rainfall events. The technique uses vertical profiles of reflectivity to identify the potential presence of warm rain (i.e., tropical rainfall) microphysics and delineates the tropical rainfall region to which the tropical Z-R relationship is applied. The performance of the algorithm is examined based on case studies of five storms that produced extreme precipitation in the United States. Results demonstrate relative improvements in radar-based quantitative precipitation estimation through the automated identification of tropical rainfall and the subsequent adaptation of the tropical Z-R relation to account for the potential warm rain processes.

Abstract. The planetary boundary layer (PBL) governs the vertical transport of mass, momentum, and moisture between the surface and the free atmosphere, and thus the determination of PBL height (BLH) is recognized as crucial for air quality, weather, and climate analysis. Although reanalysis products can provide important insight into the global view of BLH in a seamless way, the BLH observed in situ on a global scale remains poorly understood due to the lack of high-resolution (1 or 2 s) radiosonde measurements. The present study attempts to establish a near-global BLH climatology at synoptic times (00:00 and 12:00 UTC) and in the daytime using high-resolution radiosonde measurements over 300 radiosonde sites worldwide for the period 2012 to 2019, which is then compared against the BLHs obtained from four reanalysis datasets, including ERA5, MERRA-2, JRA-55, and NCEP-2. The variations in daytime BLH exhibit large spatial and temporal dependence, and as a result the BLH maxima are generally discerned over the regions such as the western United States and western China, in which the balloon launch times mostly correspond to the afternoon. The diurnal variations in BLH are revealed with a peak at 17:00 local solar time (LST). The most promising reanalysis product is ERA5, which underestimates BLH by around 130 m as compared to radiosondes released during daytime. In addition, MERRA-2 is a well-established product and has an underestimation of around 160 m. JRA-55 and NCEP-2 might produce considerable additional uncertainties, with a much larger underestimation of up to 400 m. The largest bias in the reanalysis data appears over the western United States and western China, and it might be attributed to the maximal BLH in the afternoon when the PBL has risen. Statistical analyses further indicate that the biases of reanalysis BLH products are positively associated with orographic complexity, as well as the occurrence of static instability. To our best knowledge, this study presents the first near-global view of high-resolution radiosonde-derived boundary layer height and provides a quantitative assessment of the four frequently used reanalysis products.