Exceptions are models that are periodically corrected by assimilating atmospheric observations to provide a more meteorologically correct simulation of precipitation δ-values ( 13).
Comparisons with monthly GNIP observations are thus generally limited to multiyear averages of months or years ( 9, 11). However, whole atmosphere simulations are sensitive to initial conditions and stochasticity, so although they are useful for exploring dynamical mechanisms, they do not accurately replicate historic meteorology, and in at least one recent assessment from Europe also fail to capture long-term trends ( 12). By incorporating relevant isotopic processes for water, these models can calculate δ-values of precipitation or vapor. These complex and computationally intensive tools simulate three-dimensional atmospheric transport processes and may incorporate additional Earth system components ( 10, 11). Precipitation δ-values can be modeled using isotope-enabled Earth system models ( 9). Predictions from our modeling framework, Piso.AI, are available at. These predictions provide important isotope input variables for ecological and hydrological applications, as well as powerful targets for paleoclimate proxy calibration, and they can serve as resources for probing historic patterns in the isotopic composition of precipitation with a high level of meteorological accuracy.
#PRECIPITATE CALCULATOR SERIES#
This approach facilitates simple, user-friendly predictions of precipitation isotope time series that can be generated on demand and are accurate enough to be used for exploration of interannual and long-term variability in both hydrogen and oxygen isotopic systems. Predictions from this model are currently available for any location in Europe for the past 70 y (1950–2019), which is the period for which all climate data used as predictor variables are available. We developed a framework using machine learning to calculate isotope time series at monthly resolution using available climate and location data in order to improve precipitation isotope model predictions. This can limit applications that seek to use these values to identify the source history of water or to understand the hydrological or meteorological processes that determine these values.
However, direct measurements are not available at every location and time, and existing precipitation isotope models are often not sufficiently accurate for examining features such as long-term trends or interannual variability. Hydrogen and oxygen isotope values of precipitation are critically important quantities for applications in Earth, environmental, and biological sciences.
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