Tony E. Wong

Convergent approaches to determine an ecosystem's transpiration fraction

The transpiration (T) fraction of total terrestrial evapotranspiration (ET), T/ET, can vary across ecosystems between 20–95% with a global average of ∼60%. The wide range may either reflect true heterogeneity between ecosystems and/or uncertainties in the techniques used to derive this property. Here we compared independent approaches to estimate T/ET at two needleleaf forested sites with a factor of 3 difference in leaf area index (LAI). The first method utilized water vapor isotope profiles and the second derived transpiration through its functional relationship with gross primary production. We found strong agreement between T/ET values from these two independent approaches although we noted a discrepancy at low vapor pressure deficits (VPD). We hypothesize that this divergence arises because stomatal conductance is independent of humidity at low VPD. Overall, we document significant synoptic-scale T/ET variability but minimal growing season-scale variability. This result indicates a high sensitivity of T/ET to passing weather but convergence toward a stable mean state, which is set by LAI. While changes in T/ET could emerge from a myriad of processes, including aboveground (LAI) or belowground (rooting depth) changes, there was only minimal interannual variability and no secular trend in our analysis of T/ET from the 15 year eddy covariance time series at Niwot Ridge. If the lack of trend observed here is apparent elsewhere, it suggests that the processes controlling the T and E fluxes are coupled in a way to maintain a stable ratio.

Evaluating hydrological processes in the Community Atmosphere Model Version 5 (CAM5) using stable isotope ratios of water

Water isotope-enabled climate and earth system models are able to directly simulate paleoclimate proxy records to aid in climate reconstruction. A less used major advantage is that water isotopologues provide an independent constraint on many atmospheric and hydrologic processes, allowing the model to be developed and tuned in a more physically accurate way. This paper describes the new isotope-enabled CAM5 model, including its isotopic physics routines, and its ability to simulate the modern distribution of water isotopologues in vapor and precipitation. It is found that the model has a negative isotopic bias in precipitation. This bias is partially attributed to model overestimates of deep convection, particularly over the midlatitude oceans during winter. This was determined by examining isotope ratios both in precipitation and vapor, instead of precipitation alone. This enhanced convective activity depletes the isotopic water vapor in the lower troposphere, where the majority of poleward moisture transport occurs, resulting in the insufficient transport of water isotopologue mass poleward and landward. This analysis also demonstrates that large-scale dynamical or moisture source changes can impact isotopologue values as much as local shifts in temperature or precipitation amount. The diagnosis of limitations in the large-scale transport characteristics has major implications if one is using isotope-enabled climate models to examine paleoclimate proxy records, as well as the modern global hydroclimate.

Evaluation of modeled land‐atmosphere exchanges with a comprehensive water isotope fractionation scheme in version 4 of the Community Land Model

All physical process models and field observations are inherently imperfect, so there is a need to both (1) obtain measurements capable of constraining quantities of interest and (2) develop frameworks for assessment in which the desired processes and their uncertainties may be characterized. Incorporation of stable water isotopes into land surface schemes offers a complimentary approach to constrain hydrological processes such as evapotranspiration, and yields acute insight into the hydrological and biogeochemical behaviors of the domain. Here, a stable water isotopic scheme in the National Center for Atmospheric Researchs version 4 of the Community Land Model (CLM4) is presented. An overview of the isotopic methods is given. Isotopic model results are compared to available datasets on site-level and global scales for validation. Comparisons of site-level soil moisture and isotope ratios reveal that surface water does not percolate as deeply into the soil as observed in field measurements. The broad success of the new model provides confidence in its use for a range of climate and hydrological studies, while the sensitivity of simulation results to kinetic processes stands as a reminder that new theoretical development and refinement of kinetic effect parameterizations is needed to achieve further improvements.

Assessing the impact of retreat mechanisms in a simple antarctic ice sheet model using Bayesian calibration

The response of the Antarctic ice sheet (AIS) to changing climate forcings is an important driver of sea-level changes. Anthropogenic climate change may drive a sizeable AIS tipping point response with subsequent increases in coastal flooding risks. Many studies analyzing flood risks use simple models to project the future responses of AIS and its sea-level contributions. These analyses have provided important new insights, but they are often silent on the effects of potentially important processes such as Marine Ice Sheet Instability (MISI) or Marine Ice Cliff Instability (MICI). These approximations can be well justified and result in more parsimonious and transparent model structures. This raises the question of how this approximation impacts hindcasts and projections. Here, we calibrate a previously published and relatively simple AIS model, which neglects the effects of MICI and regional characteristics, using a combination of observational constraints and a Bayesian inversion method. Specifically, we approximate the effects of missing MICI by comparing our results to those from expert assessments with more realistic models and quantify the bias during the last interglacial when MICI may have been triggered. Our results suggest that the model can approximate the process of MISI and reproduce the projected median melt from some previous expert assessments in the year 2100. Yet, our mean hindcast is roughly 3/4 of the observed data during the last interglacial period and our mean projection is roughly 1/6 and 1/10 of the mean from a model accounting for MICI in the year 2100. These results suggest that missing MICI and/or regional characteristics can lead to a low-bias during warming period AIS melting and hence a potential low-bias in projected sea levels and flood risks.

Reduced ENSO Variability at the LGM Revealed by an Isotope-Enabled Earth System Model

Studying the El Nino–Southern Oscillation (ENSO) in the past can help us better understand its dynamics and improve its future projections. However, both paleoclimate reconstructions and model simulations of ENSO strength at the Last Glacial Maximum (LGM, 21 ka BP) have led to contradicting results. Here, we perform model simulations using the recently developed water isotope-enabled Community Earth System Model (iCESM). For the first time, model simulated oxygen isotopes are directly compared with those from ENSO reconstructions using the Individual Foraminifera Analysis (IFA). We find that the LGM ENSO is most likely weaker comparing with the preindustrial. The iCESM suggests that total variance of the IFA records may only reflect changes in the annual cycle instead of ENSO variability as previously assumed. Furthermore, the interpretation of subsurface IFA records can be substantially complicated by the habitat depth of thermocline-dwelling foraminifera and their vertical migration with a temporally varying thermocline.

Sea-level projections representing the deeply uncertain contribution of the West Antarctic ice sheet

There is a growing awareness that uncertainties surrounding future sea-level projections may be much larger than typically perceived. Recently published projections appear widely divergent and highly sensitive to non-trivial model choices. Moreover, the West Antarctic ice sheet (WAIS) may be much less stable than previous believed, enabling a rapid disintegration. Here, we present a set of probabilistic sea-level projections that approximates the deeply uncertain WAIS contributions. The projections aim to inform robust decisions by clarifying the sensitivity to non-trivial or controversial assumptions. We show that the deeply uncertain WAIS contribution can dominate other uncertainties within decades. These deep uncertainties call for the development of robust adaptive strategies. These decision-making needs, in turn, require mission-oriented basic science, for example about potential signposts and the maximum rate of WAIS-induced sea-level changes.Future sea-level rise poses nontrivial risks for many coastal communities. Managing these risks often relies on consensus projections like those provided by the IPCC. Yet, there is a growing awareness that the surrounding uncertainties may be much larger than typically perceived. Recently published sea-level projections appear widely divergent and highly sensitive to non-trivial model choices and the West Antarctic Ice Sheet (WAIS) may be much less stable than previously believed, enabling a rapid disintegration. In response, some agencies have already announced to update their projections accordingly. Here, we present a set of probabilistic sea-level projections that approximate deeply uncertain WAIS contributions. The projections aim to inform robust decisions by clarifying the sensitivity to non-trivial or controversial assumptions. We show that the deeply uncertain WAIS contribution can dominate other uncertainties within decades. These deep uncertainties call for the development of robust adaptive strategies. These decision-making needs, in turn, require mission-oriented basic science, for example about potential signposts and the maximum rate of WAIS induced sea-level changes.

Deep Uncertainty Surrounding Coastal Flood Risk Projections: A Case Study for New Orleans

Future sea-level rise drives severe risks for many coastal communities. Strategies to manage these risks hinge on a sound characterization of the uncertainties. For example, recent studies suggest that large fractions of the Antarctic ice sheet (AIS) may rapidly disintegrate in response to rising global temperatures, leading to potentially several meters of sea-level rise during the next few centuries. It is deeply uncertain, for example, whether such an AIS disintegration will be triggered, how much this would increase sea-level rise, whether extreme storm surges intensify in a warming climate, or which emissions pathway future societies will choose. Here, we assess the impacts of these deep uncertainties on projected flooding probabilities for a levee ring in New Orleans, Louisiana. We use 18 scenarios, presenting probabilistic projections within each one, to sample key deeply uncertain future projections of sea-level rise, radiative forcing pathways, storm surge characterization, and contributions from rapid AIS mass loss. The implications of these deep uncertainties for projected flood risk are thus characterized by a set of 18 probability distribution functions. We use a global sensitivity analysis to assess which mechanisms contribute to uncertainty in projected flood risk over the course of a 50-year design life. In line with previous work, we find that the uncertain storm surge drives the most substantial risk, followed by general AIS dynamics, in our simple model for future flood risk for New Orleans.

A multi-objective decision-making approach to the journal submission problem

When researchers complete a manuscript, they need to choose a journal to which they will submit the study. This decision requires to navigate trade-offs between multiple objectives. One objective is to share the new knowledge as widely as possible. Citation counts can serve as a proxy to quantify this objective. A second objective is to minimize the time commitment put into sharing the research, which may be estimated by the total time from initial submission to final decision. A third objective is to minimize the number of rejections and resubmissions. Thus, researchers often consider the trade-offs between the objectives of (i) maximizing citations, (ii) minimizing time-to-decision, and (iii) minimizing the number of resubmissions. To complicate matters further, this is a decision with multiple, potentially conflicting, decision-maker rationalities. Co-authors might have different preferences, for example about publishing fast versus maximizing citations. These diverging preferences can lead to conflicting trade-offs between objectives. Here, we apply a multi-objective decision analytical framework to identify the Pareto-front between these objectives and determine the set of journal submission pathways that balance these objectives for three stages of a researcher’s career. We find multiple strategies that researchers might pursue, depending on how they value minimizing risk and effort relative to maximizing citations. The sequences that maximize expected citations within each strategy are generally similar, regardless of time horizon. We find that the “conditional impact factor”—impact factor times acceptance rate—is a suitable heuristic method for ranking journals, to strike a balance between minimizing effort objectives and maximizing citation count. Finally, we examine potential co-author tension resulting from differing rationalities by mapping out each researcher’s preferred Pareto front and identifying compromise submission strategies. The explicit representation of trade-offs, especially when multiple decision-makers (co-authors) have different preferences, facilitates negotiations and can support the decision process.

Interpreting Precession‐Driven δ18O Variability in the South Asian Monsoon Region

Speleothem records from the South Asian summer monsoon (SASM) region display variability in the ratio of O and O (δO) in calcium carbonate at orbital frequencies. The dominant mode of variability in many of these records reflects cycles of precession. There are several potential explanations for why SASM speleothem records show a strong precession signal, including changes in temperature, precipitation, and circulation. Here we use an Earth system model with water isotope tracers and water-tagging capability to deconstruct the precession signal found in SASM speleothem records. Our results show that cycles of precession-eccentricity produce changes in SASM intensity that correlate with local temperature, precipitation, and δO. However, neither the amount effect nor temperature differences are responsible for the majority of the SASM δO variability. Instead, changes in the relative moisture contributions from different source regions drive much of the SASM δO signal, with more nearby moisture sources during Northern Hemisphere summer at aphelion and more distant moisture sources during Northern Hemisphere summer at perihelion. Further, we find that evaporation amplifies the δO signal of soil water relative to that of precipitation, providing a better match with the SASM speleothem records. This work helps explain a significant portion of the long-term variability found in SASM speleothem records. Plain Language Summary Cave records suggest that there has been significant long-term climate variability in India related to changes in Earth’s orbit. However, these records are difficult to interpret because the signals can represent several different climate responses. Here we use a climate model that directly simulates the isotopic data captured in the cave records to better interpret their physical meaning. From these model simulations, we show that a large portion of the orbital signals found in the cave records are due to changes in the amount of water vapor coming from different sources. Changes in the amount of local evaporation compared to precipitation also have a large effect on the signals found in the cave records.

Neglecting model structural uncertainty underestimates upper tails of flood hazard

Coastal flooding drives considerable risks to many communities, but projections of future flood risks are deeply uncertain. The paucity of observations of extreme events often motivates the use of statistical approaches to model the distribution of extreme storm surge events. A key deep uncertainty that is often overlooked is model structural uncertainty. There is currently no strong consensus among experts regarding which class of statistical model to use as a best practice. Robust management of coastal flooding risks requires coastal managers to consider the distinct possibility of non-stationarity in storm surges. This increases the complexity of the potential models to use, which tends to increase the data required to constrain the model. Here, we use a Bayesian model averaging approach to analyze the balance between model complexity sufficient to capture decision-relevant risks and data availability to constrain complex model structures. We characterize deep model structural uncertainty through a set of calibration experiments. Specifically, we calibrate a set of models ranging in complexity using long-term tide gauge observations from the Netherlands and the United States. We find that in both cases, roughly half the model weight is associated with non-stationary models. Our approach provides a formal framework to integrate information across model structures, in light of the potentially sizable modeling uncertainties. By combining information from multiple models, our inference sharpens for the projected storm surge 100-year return levels, and estimated return levels increase by several centimeters. We assess the impacts of data availability through a set of experiments with temporal subsets and model comparison metrics. Our analysis suggests about 70 years of data are required to stabilize estimates of the 100-year return level, for the locations and methods considered here.