Enhancing Understanding of the Hydrological Cycle via Pairing of Process‐Oriented and Isotope Ratio Tracers

Fiorella, Richard P.; Siler, Nicholas; Nusbaumer, Jesse; Noone, David

doi:10.1029/2021ms002648

Cited by 9 publications

(9 citation statements)

References 182 publications

(211 reference statements)

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“…Community Earth System Model (CESM) version 2 (Danabasoglu et al, 2020). The Community Atmosphere Model version 6 (CAM6) was setup to incorporate water isotope ratio tracers (Fiorella et al, 2021;Nusbaumer et al, 2017).…”

Section: Isocam Simulationmentioning

confidence: 99%

“…Cloud microphysics are parameterized by the Morrison-Gettleman scheme, version 2 (Gettelman & Morrison, 2015;Morrison & Gettelman, 2008). Details of the water isotope physics in the atmospheric model are given in Nusbaumer et al (2017), while parameterization of land-atmosphere isotope ratio exchanges are given in the appendix to Fiorella et al (2021).…”

Section: Isocam Simulationmentioning

confidence: 99%

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Vertical structure of turbulence and fluxes across cloud mesoscale organizations from the WP-3D Orion aircraft during ATOMIC

Henze

Noone²,

Szoeke³

et al. 2022

Preprint

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In-situ measurements of the trade cumulus boundary layer turbulence structure are compared across large-scale circulation conditions and cloud horizontal organizations during the EUREC4A-ATOMIC campaign. The vertical structure of turbulent (e.g. vertical velocity variance, total kinetic energy) and flux (e.g. sensible, latent, and buoyancy) quantities are derived and investigated using the WP-3D aircraft stacked level legs (cloud modules).The 16 cloud modules aboard the P-3 were split into three groups according to cloud top height and column-integrated TKE and vertical velocity variance. These groups map onto qualitative cloud features related to object size and clustering over a scale of 100 km. This grouping also correlates to the large scale forcings of surface windspeed and low-level divergence on the scale of a few hundred km. The ratio cloud top to trade inversion base height is consistent across the groups at around 1.18. The altitude of maximum turbulence is 0.75-0.85 of cloud top height. The consistency of these ratios across the groups may point to the underlying coupling between convection, dissipation, and boundary layer thermodynamic structure. The following picture of turbulence and cloud organization is proposed: (1) light surface winds and turbulence which decreases from the sub-cloud mixed layer (ML) with height generates clouds with generally uniform spacing and smaller features, then (2) as the surface winds increase, convective aggregation occurs, and finally (3), if surface convergence occurs, convection and turbulence reach higher altitudes, producing higher clouds which may precipitate and create colds pools. Observations are compared to a CAM simulation is run over the study period, nudged by ERA5 winds and surface pressure. CAM produces higher column integrated turbulent kinetic energy and larger maximum values on the days where higher cloud tops are observed from the aircraft, which is likely a factor that influences the development of deeper clouds in the model. However, CAM places the peak turbulence 500 m lower than observed, suggesting there may be a bias in CAM representation of turbulence and moisture transport. CAM also does not capture the large LHFs seen for two of the days in which lower cloud tops are observed, which could result in insufficient lower free tropospheric moistening in the model during this type of cloud organization. A large and consistent bias between the model and observations for all cloud groups is the negative SHFs produced in CAM near 1500 m. This is not observed in the measurements. This leads to a net negative buoyancy flux not observed and provides evidence of a specific shortcoming that can be addressed as part of the needed improvement in the representation of clouds by large-scale models.

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Section: Isocam Simulationmentioning

confidence: 99%

Section: Isocam Simulationmentioning

confidence: 99%

Vertical structure of turbulence and fluxes across cloud mesoscale organizations from the WP-3D Orion aircraft during ATOMIC

Henze

Noone²,

Szoeke³

et al. 2022

Preprint

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“…Their study assessed whether and to what extent potential climate effects on the isotopic composition can be verified in data simulated by an isotope-enabled climate model. There is an important difference between their study and our work: while our ML methods use standard output variables of climate models as inputs, Fiorella et al (2021) relied on tracers being implemented for the inputs to the random forest regression. While this is a suitable choice for their study design and research question, it limits the utility of their random forest model as an emulator.…”

Section: Introductionmentioning

confidence: 99%

“…We thus call this approach “offline-emulation.” Conducting the emulation “offline,” that is, not coupled to the climate simulation, is possible because isotopes are passive tracers of the hydrological cycle that reflect climatic variations, but have no feedback onto the climate system. Exploratory work in this direction has been conducted by Fiorella et al (2021), who used random forest regression to infer isotope ratios in precipitation. Their study assessed whether and to what extent potential climate effects on the isotopic composition can be verified in data simulated by an isotope-enabled climate model.…”

Section: Introductionmentioning

confidence: 99%

Towards learned emulation of interannual water isotopologue variations in General Circulation Models

Wider,

Kruse,

Weitzel

et al. 2023

Environ. Data Science

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Simulating abundances of stable water isotopologues, that is, molecules differing in their isotopic composition, within climate models allows for comparisons with proxy data and, thus, for testing hypotheses about past climate and validating climate models under varying climatic conditions. However, many models are run without explicitly simulating water isotopologues. We investigate the possibility of replacing the explicit physics-based simulation of oxygen isotopic composition in precipitation using machine learning methods. These methods estimate isotopic composition at each time step for given fields of surface temperature and precipitation amount. We implement convolutional neural networks (CNNs) based on the successful UNet architecture and test whether a spherical network architecture outperforms the naive approach of treating Earth’s latitude-longitude grid as a flat image. Conducting a case study on a last millennium run with the iHadCM3 climate model, we find that roughly 40% of the temporal variance in the isotopic composition is explained by the emulations on interannual and monthly timescale, with spatially varying emulation quality. The tested CNNs outperform simple baseline models such as random forest and pixel-wise linear regression substantially. A modified version of the standard UNet architecture for flat images yields results that are as good as the predictions by the spherical CNN. Variations in the implementation of isotopes between climate models likely contribute to an observed deterioration of emulation results when testing on data obtained from different climate models than the one used for training. Future work toward stable water-isotope emulation might focus on achieving robust climate–oxygen isotope relationships or exploring the set of possible predictor variables.

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“…Stable water isotope data (δ 2 H and δ 18 O) provide a powerful tool to study hydrologic connectivity and transit times that are reflective of integrated transport processes within a landscape (Brooks et al, 2014;Capell et al, 2012;Fiorella et al, 2021;Jasechko et al, 2016;Kirchner, 2016aKirchner, , 2016bLutz et al, 2018;McGuire et al, 2005;McGuire & McDonnell, 2006;Stockinger et al, 2016). Isotopic tracers are able to carry a signature of partial evaporation of soil waters and connectivity between soil and deeper subsurface processes (i.e., infiltration mechanisms), thereby linking the response of precipitation becoming discharge (Brooks et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Relationship between isotope ratios in precipitation and stream water across watersheds of the National Ecological Observation Network

Butler,

Good,

Haagsma

et al. 2023

Hydrological Processes

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The timescales associated with precipitation moving through watersheds reveal processes that are critical to understanding many hydrologic systems. Measurements of environmental stable water isotope ratios (δ2H and δ18O) have been used as tracers to study hydrologic timescales by examining how long it takes for incoming precipitation tracers become stream discharge, yet limited measurements both spatially and temporally have bounded macroscale evaluations so far. In this observation driven study across North American biomes within the National Ecological Observation Network (NEON), we examined δ18O and δ2H stable water isotope in precipitation (δP) and stream water (δQ) at 26 co‐located sites. With an average 54 precipitation samples and 139 stream water samples per site collected over 2014–2022, assessment of local meteoric water lines and local stream water lines showed geographic variation across North America. Taking the ratio of estimated seasonal amplitudes of δP and δQ to calculate young water fractions (Fyw), showed a Fyw range from 1% to 93% with most sites having Fyw below 20%. Calculated mean transit times (MTT) based on a gamma convolution model showed a MTT range from 0.10 to 13.2 years, with half of the sites having MTT estimates lower than 2 years. Significant correlations were found between the Fyw and watershed area, longest flow length, and the longest flow length/slope. Significant correlations were found between MTT and site latitude, longitude, slope, clay fraction, temperature, precipitation magnitude, and precipitation frequency. The significant correlations between water timescale metrics and the environmental characteristics we report share some similarities with those reported in prior studies, demonstrating that these quantities are primarily driven by site or area specific factors. The analysis of isotope data presented here provides important constraints on isotope variation in North American biomes and the timescales of water movement through NEON study sites.

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Enhancing Understanding of the Hydrological Cycle via Pairing of Process‐Oriented and Isotope Ratio Tracers

Cited by 9 publications

References 182 publications

Vertical structure of turbulence and fluxes across cloud mesoscale organizations from the WP-3D Orion aircraft during ATOMIC

Vertical structure of turbulence and fluxes across cloud mesoscale organizations from the WP-3D Orion aircraft during ATOMIC

Towards learned emulation of interannual water isotopologue variations in General Circulation Models

Relationship between isotope ratios in precipitation and stream water across watersheds of the National Ecological Observation Network

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