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Abstract The mechanisms underlying observed global patterns of partitioning precipitation () to evapotranspiration () and runoff () are controversially debated. We test the hypothesis that asynchrony between climatic water supply and demand is sufficient to explain spatio‐temporal variability of water availability. We developed a simple analytical model forthat is determined by four dimensionless characteristics of intra‐annual water supply and demand asynchrony. The analytical model, populated with gridded climate data, accurately predicted global runoff patterns within 2%–4% of independent estimates from global climate models, with spatial patterns closely correlated to observations (). The supply‐demand asynchrony hypothesis provides a physically based explanation for variability of water availability using easily measurable characteristics of climate. The model revealed widespread responsiveness of water budgets to changes in climate asynchrony in almost every global region. Furthermore, the analytical model using global averages independently reproduced the Budyko curve () providing theoretical foundation for this widely used empirical relationship.
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Downscaling CESM2 in CLM5 to Hindcast Preindustrial Equilibrium Line Altitudes for Tropical Mountain Glaciers
Abstract Tropical mountain glaciers are an important water resource and highly impacted by recent climate change. Tropical mountain glaciation also occurred in the recent and deep past, which presents opportunities for better validating paleoclimate simulations in continental interiors and mountainous regions but requires bridging global model scales (hundreds of kilometers) with the1–10 km scale of glaciers when paleotopography is poorly known. Here, we hindcast tropical mountain glaciation in preindustrial time by using global climate model meteorology to force standalone simulations in its land component that use high‐resolution topography to resolve selected tropical mountain glaciers. These simulations underestimate observed equilibrium line altitudes (ELAs) by 249330 m, but the simulated ELA and snow lines capture observed intermountain ELA variability. Errors in large‐scale model precipitation and ELA reconstruction uncertainty are the main contributors to this bias.
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- Award ID(s):
- 1849754
- PAR ID:
- 10360561
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 48
- Issue:
- 17
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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