Search

The data set represents the Regional Meteoric Water Line (RMWL) for the Yucatan Peninsula (Mexico), a region with karstic aquifers but little knowledge of the isotopic composition of meteoric water. This data paper comprises the isotopic composition of meteoric water collected from November 2018 to November 2019 as monthly composite samples expressed as weighted monthly mean precipitation from 16 locations across the Yucatan Peninsula. The RMWL was δ2H = 7.803 δ18O + 12.075, with a slope and intercept suggesting precipitation from air that has undergone condensation, air masses with variable moisture or recycled moisture. The average δ18O is −2.57‰ (−12.23 to 1.2‰) and δ2H is −7.94‰ (−83.39 to 18.32‰). This data set was collected by the implementation of an isotopic monitoring network, gathering information useful as a tool for better understanding of the hydrology and hydrogeochemistry of karstic aquifers, and to develop proxies for paleohydrology, ecohydrology, climate change and paleoclimate studies in the Gulf of Mexico and the Great Caribbean Area. [ABSTRACT FROM AUTHOR]

Global mean precipitation changes due to climate change were previously shown to be relatively small and well constrained by the energy budget. However, local precipitation changes can be much more significant. In this paper we propose that for large enough scales, for which the water budget is closed (precipitation [P] roughly equals evaporation [E]), changes in P approach the small global mean value. However, for smaller scales, for which P and E are not necessarily equal and convergence of water vapor still plays a role, changes in P could be much larger due to dynamical contributions. Using 40 years of two reanalysis data sets, 39 Coupled Model Intercomparison Project Phase 5 (CMIP5) models and additional numerical simulations, we identify the scale of transition in the importance of the different terms in the water budget to precipitation to be ~3,500–4,000 km and demonstrate its relation to the spatial scale of precipitation changes under climate change. Plain Language Summary: Predicting precipitation changes due to climate change is of great importance for society. We propose that the present‐day characteristic scale of the hydrological cycle (for which precipitation roughly equals evaporation) predicts the spatial scale of future precipitation changes under global warming. For smaller scales than the characteristic scale of the hydrological cycle, changes in precipitation could be much larger than the global mean change due to water vapor convergence contributions. However, above this scale the precipitation changes approach the relatively small global mean change. Using reanalysis data sets, Coupled Model Intercomparison Project Phase 5 (CMIP5) models, and additional numerical simulations, we identify the characteristic scale of the hydrological cycle to be ~3,500–4,000 km and demonstrate its relation to the spatial scale of precipitation changes under climate change. These results suggest that changes in precipitation on the regional‐continental scale could be much larger than the global mean change. Key Points: The present‐day characteristic scale of the hydrological cycle predicts the spatial scale of precipitation changes under climate changeUsing reanalysis data sets and CMIP5 models, we identify the characteristic scale of the hydrological cycle to be ~3,500–4,000 kmThese results suggest that changes in precipitation on the continental scale could be much larger than the global mean change [ABSTRACT FROM AUTHOR]

Few studies conduct a quantitative assessment between the urban extent and local precipitation characteristics. In this paper, high‐resolution precipitation data and urban extent data are used to investigate the correlation between precipitation frequency, intensity and extremes and urban extent under different climate conditions over the Pearl River Delta, China. Analysis reveals that extreme precipitation amount (R = 0.45 for 1‐hr maximum) and mean intensity (R = 0.56) are positively correlated with urban extent and that wet hours (R = −0.58) are negatively correlated with urban extent. The correlation coefficients between urban extent and extreme indices increase with the decrease in temporal scale of indices. The high coefficients of precipitation intensity and urban extent are mainly attributed to the precipitation under the tropical convection, and the negative coefficient of total wet hours is dominated by precipitation frequency under the Southwest monsoon. By contrast, the precipitation characteristics are less correlated with urban extent during typhoon events. Urban environments probably play a positive role in generating the short‐duration heavy precipitation through enhancing deep convections induced by higher surface temperature and higher aerosol concentration. [ABSTRACT FROM AUTHOR]

Interannual land cover change plays a significant role in food security, ecosystem processes, and regional and global climate modelling. Measuring the magnitude and location and understanding the driving factors of interannual land cover change are therefore of utmost importance to improve our understanding and prediction of these impacts and to better differentiate between natural and human causes of land cover change. Despite advances in quantifying the magnitude of land cover change, the interpretation of the observed land cover change in terms of climatic, ecological and anthropogenic processes still remains a complex issue. In this paper, we map land cover change across sub-Saharan Africa and examine the influences of rainfall fluctuations on interannual change.The analysis was applied to sub-Saharan Africa.Ten-day rainfall estimates (RFE) obtained from National Oceanic and Atmospheric Administration's (NOAA) Climate Prediction Center (CPC) were used to extract information on inter and intra-annual rainfall fluctuations. The magnitude of land cover change was quantified based on the multitemporal change vector method measuring year-to-year differences in bidirectional reflectance distribution function (BRDF) corrected 16-day enhanced vegetation index (EVI) data from the Moderate Resolution Imaging Spectro-radiometer (MODIS). Statistical models were used to estimate the relationship between short-term rainfall variability and the magnitude of land cover change. The analysis was stratified first by physiognomic vegetation type and second by chorological data on species distribution to gain insights into spatial variations in response to short-term rainfall fluctuations.The magnitude of land cover change was significantly related to rainfall variability at the 5% level. Stratification considerably strengthened the relationship between the magnitude of change and rainfall variability. Explanatory power of the models ranged fromR2 = 0.22 for the unstratified model to 0.40–0.96 for the individual models stratified by patterns of species distribution. The total variability explained by the combined models including the influence of rainfall and differences in vegetation response ranged from 22% for the model not stratified by vegetation to 76% when stratified by chorological data.Using this methodology, we were able to measure the contribution of natural variation in precipitation to land cover change. Several ecosystems across sub-Saharan Africa are highly sensitive to short-term rainfall variability. [ABSTRACT FROM AUTHOR]

Featured paper: See also the Editorial by McDowell et al [ABSTRACT FROM AUTHOR]