Search Results

  • Research Article

    Pedersen Ionic Contribution in Different Time Scales

    Abstract

    Pedersen conductivity is one of the main parameters in atmospheric electrodynamics, magnetosphere‐ionosphere‐thermosphere coupling and several other geophysical processes. It is determined by the collision frequency between charged and neutral components and the Earth's magnetic field intensity through the gyrofrequency. This work analyses the contribution of different ionic species to the variability of Pedersen conductance in time scales from hours to years within the period 1964‐2008 based on the results of various atmospheric and ionospheric models. The main results are: 1) there is a positive correlation between O+ density and Pedersen conductance; 2) Pedersen conductance of F‐layer has a larger increase with the solar activity than that of E layer; 3) Pedersen conductance has a long‐term trend that is determined by Earth's magnetic field intensity and electron density; 4) at mid‐latitudes trends are mainly governed by the Earth's magnetic field and modulated by the electron density, while at high latitudes both are important.

  • Research Article
    Open Access

    Ambiguity in the land‐use component of mitigation contributions towards the Paris Agreement goals

    Earth's FutureAccepted Articles

    First published: 28 June 2019

    Abstract

    Land‐use, land‐use change and forestry (LULUCF) activities, including deforestation and forest restoration, will play an important role in addressing climate change. Countries have stated their contributions to reducing emissions and enhancing sinks in their Nationally Determined Contributions (NDCs); in 2023, the Global Stocktake will assess the collective impact of these NDCs. Clarity in the contribution of LULUCF to NDC targets is necessary to prevent high LULUCF uncertainties from undermining the strength and clarity of mitigation in other sectors. We assess and categorize all 167 NDCs and find wide variation in how they incorporate LULUCF; many lack the clear information necessary to understand what land‐based mitigation is anticipated. The land sector is included in 121 NDCs, but only 11 provide a LULUCF target that can be fully quantified using information presented or referenced in the NDC. By developing alternative scenarios from a subset of 62 NDCs (89 countries) we estimate that ambiguity in LULUCF contributions causes an uncertainty range in the anticipated LULUCF sink in 2030 of magnitude 2.9 GtCO2eq/yr – larger in size than our best estimate for the LULUCF sink of ‐2 GtCO2eq/yr. Clearer communication of data sources and assumptions underlying the contribution of land‐use to mitigation efforts is therefore important for ensuring a robust Global Stocktake and ambitious emissions reductions. We find that guidance under the Paris Agreement may improve the clarity of future NDCs but is not sufficient to eliminate ambiguities. We therefore recommend that LULUCF targets should be presented and accounted for separately from other sectors.

  • Research Article

    The Impact of Waves and Tides on Residual Sand Transport on a Sediment‐poor, Energetic and Macrotidal Continental Shelf

    Abstract

    The energetic, macrotidal shelf off South West England was used to investigate the influence of different tide and wave conditions and their interactions on regional sand transport patterns using a coupled hydrodynamic, wave and sediment transport model. Residual currents and sediment transport patterns are important for the transport and distribution of littoral and shelf‐sea sediments, morphological evolution of the coastal and inner continental shelf zones and coastal planning.

    Waves heavily influence sand transport across this macrotidal environment. Median (50% exceedance) waves enhance transport in the tidal direction. Extreme (1% exceedance) waves can reverse the dominant transport path, shift the dominant transport phase from flood to ebb, and activate sand transport below 120m depth. Wave‐tide interactions (encompassing radiation stresses, Stoke's drift, enhanced bottom‐friction and bed shear stress, refraction, current‐induced Doppler shift and wave‐blocking) significantly and non‐linearly enhance sand transport, determined by differencing transport between coupled, wave‐only and tide‐only simulations.

    A new continental shelf classification scheme is presented based on sand transport magnitude due to wave‐forcing, tide‐forcing and non‐linear wave‐tide interactions. Classification changes between different wave/tide conditions have implications for sand transport direction and distribution across the shelf. Non‐linear interactions dominate sand transport during extreme waves at springs across most of this macrotidal shelf. At neaps, non‐linear interactions drive a significant proportion of sand transport under median and extreme waves despite negligible tide‐induced transport. This emphasises the critical need to consider wave‐tide interactions when considering sand transport in energetic environments globally, where previously tides alone or uncoupled waves have been considered.

  • Technical Reports: Methods

    Can convolution and deconvolution be used as tools for modeling multi‐component, mixing‐limited reaction networks?

    Water Resources ResearchAccepted Articles

    First published: 27 June 2019

    Abstract

    Transfer functions (i.e. convolutions) have been powerful tools for both forward modeling and reconstructing past states of transport systems in a wide range of subsurface flow problems. The advantage of the transfer function is that it is a simple alternative to complicated, distributed parameter models of flow and transport, but the majority of applications of transfer functions in hydrology have been limited to relatively simple cases, like passive tracers or first‐order decay. The central question evaluated in this note is whether or not multi‐component mixing limited reactive transport can be represented within a transfer function framework. Our examples consider forward‐in‐time (FIT) predictions, and backward‐in‐time (BIT) reconstructions of a carbonate system that represents the intrusion of sea‐water into a freshwater aquifer. The main result is that accurate FIT and BIT models are developed by posing the problem in terms of conservative components. As with all convolution‐based methods, the results are sensitive to errors and/or noise in the input functions, but we show that smoothed approximations of the requisite functions provide good representations of transport. Given the vast unknowns in any subsurface transport problem, such generalized, reactive transfer function models may have yet unexplored advantages when the tradeoffs between overall computational cost, accuracy, and uncertainty are explored in more detail.

  • Research Article

    Using Machine Learning for Prediction of Saturated Hydraulic Conductivity and its Sensitivity to Soil Structural Perturbations

    Water Resources ResearchAccepted Articles

    First published: 27 June 2019

    Abstract

    Saturated hydraulic conductivity (Ks) is a fundamental soil property that regulates the fate of water in soils. Its measurement, however, is cumbersome and instead pedotransfer functions (PTFs) are routinely used to estimate it. Despite much progress over the years, the performance of current generic PTFs estimating Ks remains poor. Using machine learning, high‐performance computing, and a large database of over 18,000 soils, we developed new PTFs to predict Ks. We compared the performances of four machine learning algorithms and different predictor sets. We evaluated the relative importance of soil properties in explaining Ks. PTF models based on boosted regression tree algorithm produced the best models with root mean squared log‐transformed error in ranges of 0.4 to 0.3 (log10(cm/day)). The 10th percentile particle diameter (d10) was found to be the most important predictor followed by clay content, bulk density (ρb), and organic carbon content (C). The sensitivity of Ks to soil structure was investigated using ρb and C as proxies for soil structure. An inverse relationship was observed between ρb and Ks, with the highest sensitivity at around 1.8 g cm‐3 for most textural classes. Soil C showed a complex relationship with Ks with an overall positive relation for fine‐ and mid‐textured soils but an inverse relation for coarse‐textured soils. This study sought to maximize the extraction of information from a large database to develop generic machine learning‐based PTFs for estimating Ks. Models developed here have been made publicly available and can be readily used to predict Ks.

  • Research Letter
    Open Access

    Theoretical Predictability Limits of Spatially Anisotropic Multifractal Processes: Implications for Weather Prediction

    Earth and Space ScienceAccepted Articles

    First published: 26 June 2019

    Abstract

    A correlation‐spectra based approach is used to express the theoretical predictability limits of multifractal processes as an analytical function of their anisotropy parameters. This spatially‐anisotropic power‐law function is then used to investigate the general impact of anisotropy on the predictability of atmospheric fields in the weather regime. The investigation reveals that: (i) vertical stratification of a field increases and decreases its super and subsphero‐scale predictability limits respectively, (ii) trivial horizontal anisotropy slightly improves predictability at all scales, and (iii) horizontal anisotropy together with vertical stratification significantly enhances its predictability over almost the entire scale‐range. Applying these general results to the case of horizontal wind fields suggests that the interplay between spatial‐anisotropy and atmospheric predictability could account for improvements in forecast skill, commonly observed during the occurrence of rotating thunderstorms and breaks in the Indian summer monsoon.

  • Research Article

    Exchange of Water Between the Ross gyre and ACC Assessed by Lagrangian Particle Tracking

    Abstract

    To reach upwelling and downwelling zones deep within the Southern Ocean seasonal sea‐ice cover, water masses must move across the Antarctic Circumpolar Current and through current systems including the Ross Gyre, Weddell Gyre, and Antarctic Slope Current (ASC). In this study we focus our attention on the lagrangian exchange between the Ross Gyre and surrounding current systems. We conducted numerical experiments using 5‐day 3D velocity fields from the Southern Ocean State Estimate with a particle tracking package to identify pathways by which waters move from near the Antarctic (AA) coastal margins or Antarctic Circumpolar Current (ACC) into the interior of the Ross Gyre, and to identify the timescales of variability associated with these pathways. Waters from near the AA margins enter the Ross Gyre along the western and northern boundary of gyre until the gyre separates from the Pacific‐Antarctic Ridge (PAR) near fracture zones. At this juncture, ACC‐derived inflow dominates the across‐gyre transport up to the Antarctic margin. Transport and exchange associated with different time‐average components of flow are calculated to determine the relative contributions of high‐ and low‐frequency and time‐mean components.

  • Research Article

    A Nonlinear Dynamical Systems based Modeling Approach for Stochastic Simulation of Stream flow and Understanding Predictability

    Water Resources ResearchAccepted Articles

    First published: 26 June 2019

    Abstract

    We propose a time series modeling approach based on nonlinear dynamical systems to recover the underlying dynamics and predictability of streamflow and to produce projections with identifiable skill. First, a wavelet spectral analysis is performed on the time series to identify the dominant quasi‐periodic bands. The time series is then reconstructed across these bands and summed to obtain a signal time series. This signal is embedded in a D‐dimensional space with an appropriate lag τ to reconstruct the phase space in which the dynamics unfolds. Time varying predictability is assessed by quantifying the divergence of trajectories in the phase space with time, using Local Lyapunov Exponents (LLE). Ensembles of projections from a current time are generated by block resampling trajectories of desired projection length, from the K‐nearest neighbors of the current vector in the phase space. This modeling approach was applied to the naturalized historical and paleo reconstructed streamflow at Lees Ferry gauge on the Colorado River which offered three interesting insights. (i) The flows exhibited significant epochal variations in predictability. (ii) The predictability of the flow quantified by LLE is related to the variance of the flow signal and selected climate indices. (iii) Blind projections of flow during epochs identified as highly predictable showed good skill in capturing the distributional and threshold exceedance statistics and poor performance during low predictability epochs. The ability to assess the potential skill of these long lead projections opens opportunities to perceive hydrologic predictability and consequently water management in a new paradigm.

  • Research Article

    Restoring a Natural Fire Regime Alters the Water Balance of a Sierra Nevada Catchment

    Water Resources ResearchAccepted Articles

    First published: 26 June 2019

    Abstract

    Fire suppression in Western US mountains has caused dense forests with high water demands to grow. Restoring natural wildfire regimes to these forests could affect hydrology by changing vegetation composition and structure, but the specific effects on water balance are unknown. Mountain watersheds supply water to much of the Western USA, so understanding the relationship between fire regime and water yield is essential to inform management. We used a distributed hydrological model to quantify hydrologic response to a restored fire regime in the Illilouette Creek Basin (ICB) within Yosemite National Park, California. Over the past 45 years, as successive fires reduced the ICB's forest cover approximately 25%, model results show that annual streamflow, subsurface water storage, and peak snowpack increased relative to a fire‐suppressed control, while evapotranspiration and climatic water deficit decreased. A second model experiment compared the water balance in the ICB under two vegetation cover scenarios: 2012 vegetation, representing a frequent‐fire landscape, and 1969 vegetation, representing fire suppression. These two model landscapes were run with observed weather data from 1972 to 2017 in order to capture natural variations in precipitation and temperature. This experiment showed that wet years experienced greater fire‐related reductions in evapotranspiration and increases in streamflow, while reductions in climatic water deficit were greater in dry years. Spring snowmelt runoff was higher under burned conditions, while summer baseflow was relatively unaffected. Restoring wildfire to the fire‐suppressed ICB likely increased downstream water availability, shifted streamflows slightly earlier, and reduced water stress to forests.

  • Research Article

    What is controlling our control rules? Opening the black box of multi‐reservoir operating policies using time‐varying sensitivity analysis

    Water Resources ResearchAccepted Articles

    First published: 26 June 2019

    Abstract

    Multi‐reservoir systems are designed to serve multiple conflicting demands over varying time scales that may be out of phase with the system's hydroclimatic inputs. Adaptive, nonlinear reservoir control policies are often best suited to serve these needs. However, nonlinear operating policies are often difficult to interpret, and so water managers tend to favor simple, static rules that may not effectively manage conflicts between the system's multisectoral demands. In this study, we introduce an analytical framework for opening the black‐box of optimized nonlinear operating policies, decomposing their time‐varying information sensitivities to show how their adaptive and coordinated release prescriptions better manage hydrologic variability. Interestingly, these information sensitivities vary significantly across policies depending on how they negotiate tradeoffs between conflicting objectives. We illustrate this analysis in the Red River basin of Vietnam, where four major reservoirs serve to protect the capital of Hanoi from flooding while also providing the surrounding region with electric power and meeting multi‐sectoral water demands for the agricultural and urban economies. Utilizing Evolutionary Multi‐Objective Direct Policy Search (EMODPS), we are able to design policies that, using the same information as sequential if/then/else‐based operating guidelines developed by the government, outperform these traditional rules with respect to every objective. Policy diagnostics using time‐varying sensitivity analysis illustrate how the EMODPS operations better adapt and coordinate information use to reduce food‐energy‐water conflicts in the basin. These findings accentuate the benefits of transitioning to dynamic operating policies in order to manage evolving hydroclimatic variability and socioeconomic demands in multi‐purpose reservoir networks.

  • Research Article

    Bayesian Dynamic Finite‐Fault Inversion: 1. Method and Synthetic Test

    Abstract

    Dynamic earthquake source inversions aim to determine the spatial distribution of initial stress and friction parameters leading to dynamic rupture models that reproduce observed ground motion data. Such inversions are challenging, particularly due to their high computational burden, thus so far only few attempts have been made. Using a highly efficient rupture simulation code, we introduce a novel method to generate a representative sample of acceptable dynamic models from which dynamic source parameters and their uncertainties can be assessed. The method assumes a linear slip‐weakening friction law and spatially variable prestress, strength and characteristic slip weakening distance along the fault. The inverse problem is formulated in a Bayesian framework and the posterior probability density function is sampled using the Parallel Tempering Monte Carlo algorithm. The forward solver combines a 3D finite difference code for dynamic rupture simulation on a simplified geometry to compute slip rates, and pre‐calculated Green's functions to compute ground motions. We demonstrate the performance of the proposed method on a community benchmark test for source inversion. We find that the dynamic parameters are resolved well within the uncertainty, especially in areas of large slip. The overall relative uncertainty of the dynamic parameters is rather large, reaching ~50% of the averaged values. In contrast, the kinematic rupture parameters (rupture times, rise times, slip values), also well‐resolved, have relatively lower uncertainties of ~10%. We conclude that incorporating physics‐based constraints, such as an adequate friction law, may serve also as an effective constraint on the rupture kinematics in finite‐fault inversions.

  • Research Article

    On the origin of Water Masses in the Beaufort Gyre

    Abstract

    The Beaufort Gyre is a key feature of the Arctic Ocean, acting as a reservoir for fresh water in the region. Depending on whether the prevailing atmospheric circulation in the Arctic is anticyclonic or cyclonic, either a net accumulation or release of fresh water occurs. The sources of fresh water to the Arctic Ocean are well established and include contributions from the North American and Eurasian rivers, the Bering Strait Pacific water inflow, sea ice meltwater and precipitation, but their contribution to the Beaufort Gyre fresh water accumulation varies with changes with the atmospheric circulation. Here, we use a Lagrangian backward tracking technique in conjunction with the 1/12° resolution NEMO model to investigate how sources of fresh water to the Beaufort Gyre have changed in recent decades, focusing on increase in the Pacific water content in the gyre between the late 1980s and early 2000s. Using empirical orthogonal functions (EOF) we analyse the change in the Arctic oceanic circulation that occurred between the 1980s and 2000s. We highlight a “waiting room” advective pathway that was present in the 1980s and provide evidence that this pathway was caused by a shift in the center of Ekman transport convergence in the Arctic. We discuss the role of these changes as a contributing factor to changes in the stratification, and hence potentially the biology, of the Beaufort Gyre region.

  • Research Letter

    Geographical distribution of thermometers gives the appearance of lower historical global warming

    Geophysical Research LettersAccepted Articles

    First published: 25 June 2019

    Abstract

    Gaps with missing data in the observational temperature record are responsible for an underestimation of the global warming between 1881‐1910 and 1986‐2015 by 0.1°C. We found that missing data in the historical observations introduce a warm bias in the early part of the record and a cold bias towards the end. The effect of the non‐uniform sampling was explored by comparing the global mean temperature estimated from gridded observations, climate model simulations, and reanalysis. Output from global simulations was sub‐sampled by masking the grid‐boxes corresponding to those with missing data in the observations to mimic the geographical availability of temperature measurements. A combination of variance depending on region and a varying geographical data sampling over time explains the bias in the global mean. We propose a methodology for estimating the global mean temperature that reduces the effect of the non‐uniform variance.

  • Research Article

    Near‐Earth solar wind forecasting using corotation from L5: The error introduced by heliographic latitude offset

    Space WeatherAccepted Articles

    First published: 25 June 2019

    Abstract

    Routine in‐situ solar wind observations from L5, located 60° behind Earth in its orbit, would provide a valuable input to space‐weather forecasting. One way to ulitise such observations is to assume that the solar wind is in perfect steady state over the 4.5 days it takes the Sun to rotate 60° and thus near‐Earth solar wind in 4.5‐days time would be identical to that at L5 today. This corotation approximation is most valid at solar minimum when the solar wind is slowly evolving. Using STEREO data, it has been possible to test L5‐corotation forecasting for a few months at solar minimum, but the various contributions to forecast error cannot be disentangled. This study uses 40+ years of magnetogram‐constrained solar wind simulations to isolate the effect of latitudinal offset between L5 and Earth due to the inclination of the ecliptic plane to the solar rotational equator. Latitudinal offset error is found to be largest at solar minimum, due to the latitudinal ordering of solar wind structure. It is also a strong function of time of year; maximum at the solstices and very low at equinoxes. At solstice, the latitudinal offset alone means L5‐corotation forecasting is expected to be less accurate than numerical solar wind models, even before accounting for time‐dependent solar wind structures. Thus, a combination of L5‐corotation and numerical solar wind modelling may provide the best forecast. These results also highlight that three‐dimensional solar wind structure must be accounted for when performing solar wind data assimilation.

  • Research Article

    Prediction and inference of flow‐duration curves using multi‐output neural networks

    Water Resources ResearchAccepted Articles

    First published: 24 June 2019

    Abstract

    We develop multi‐output neural network models (MNNs) to predict flow‐duration curves (FDCs) in 9,203 ungaged locations in the Southeastern United States for six decades between 1950‐2009. The model architecture contains multiple response variables in the output layer that correspond to individual quantiles along the FDC. During training, predictions are made for each quantile, and a combined loss function is used for back propagation and parameter updating. The loss function accounts for the covariance between the quantiles and generates physically consistent outputs (i.e., monotonically increasing quantiles with increasing nonexceedance probabilities). We use neural‐network dropout to generate posterior‐predictive distributions for FDCs, and test model performance under cross validation. Finally, we demonstrate how local surrgotate models, via the Local Interpretable Model‐agnostic Explanations (LIME) method, can be used to infer the relation between basin characteristics and the predicted FDCs. Results suggest that MNNs can learn the monotonic relations between adjacent quantiles on an FDC, they result in better predictions than single‐output neural‐network models that predict each quantile independently, and basin characteristics are most useful for predicting smaller quantiles, whereas bias terms from neighboring quantiles are most informative for predicting higher quantiles.

  • Research Letter

    Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault

    Geophysical Research LettersAccepted Articles

    First published: 24 June 2019

    Abstract

    Seismogenic plate boundaries are posited to behave in a similar manner to a densely packed granular medium, where fault and blocks systems rapidly rearrange the distribution of forces within themselves, as particles do in slowly sheared granular systems. We use machine learning to show that statistical features of velocity signals from individual particles in a simulated sheared granular fault contain information regarding the instantaneous global state of intermittent frictional stick‐slip dynamics. We demonstrate that combining features built from the signals of more particles can improve the accuracy of the global model, and discuss the physical basis behind decrease in error. We show that the statistical features such as median and higher moments of the signals that represent the particle displacement in the direction of shearing are among the best predictive features. Our work provides novel insights into the applications of machine learning in studying frictional processes occurring in geophysical systems.

  • Research Article

    Endless Forams: >34,000 modern planktonic foraminiferal images for taxonomic training and automated species recognition using convolutional neural networks

    ABSTRACT

    Planktonic foraminiferal species identification is central to many paleoceanographic studies, from selecting species for geochemical research to elucidating the biotic dynamics of microfossil communities relevant to physical oceanographic processes and interconnected phenomena such as climate change. However, few resources exist to train students in the difficult task of discerning amongst closely related species, resulting in diverging taxonomic schools that differ in species concepts and boundaries. This problem is exacerbated by the limited number of taxonomic experts. Here, we document our initial progress towards removing these confounding and/or rate‐limiting factors by generating the first extensive image library of modern planktonic foraminifera, providing digital taxonomic training tools and resources, and automating species‐level taxonomic identification of planktonic foraminifera via machine learning using convolution neural networks. Experts identified 34,640 images of modern (extant) planktonic foraminifera to the species level. These images are served as species exemplars through the online portal Endless Forams (endlessforams.org) and a taxonomic training portal hosted on the citizen science platform Zooniverse (zooniverse.org/projects/ahsiang/endless‐forams/). A supervised machine learning classifier was then trained with ~27,000 images of these identified planktonic foraminifera. The best‐performing model provided the correct species name for an image in the validation set 87.4% of the time, and included the correct name in its top three guesses 97.7% of the time. Together, these resources provide a rigorous set of training tools in modern planktonic foraminiferal taxonomy and a means of rapidly generating assemblage data via machine learning in future studies for applications such as paleotemperature reconstruction and salinity indicator counting.

  • Research Article

    Modeling Photoprotection at Global Scale: the Relative Role of Non‐Photosynthetic Pigments, Physiological State and Species Composition

    Global Biogeochemical CyclesAccepted Articles

    First published: 22 June 2019

    Abstract

    Microalgae are capable of acclimating to dynamic light environments as they have developed mechanisms to optimize light harvesting and photosynthetic electron transport. When absorption of light exceeds photosynthetic capacity, various physiological protective mechanisms prevent damage of the photosynthetic apparatus. Xanthophyll pigments provide one of the most important photoprotective mechanisms to dissipate the excess light energy and prevent photoinhibition. In this study, we coupled a mechanistic model for phytoplankton photoinhibition with the global biogeochemical model Regulated Ecosystem Model version 2 (REcoM2). The assumption that photoinhibition is small in phytoplankton communities acclimated to ambient light allowed us to predict the photoprotective needs of phytoplankton. When comparing the predicted photoprotective needs to observations of pigment content determined by high‐performance liquid chromatography, our results showed that photoprotective response seems to be mediated in most parts of the ocean by a variable ratio of xanthophyll pigments to chlorophyll. The variability in the ratio appeared to be mainly driven by changes in phytoplankton community composition. Exceptions appeared at high latitudes where other energy dissipating mechanisms seem to play a role in photoprotection and both taxonomic changes and physiological acclimation determine community pigment signature. Understanding the variability of community pigment signature is crucial for modeling the coupling of light absorption to carbon fixation in the ocean. Insights about how much of this variability is attributable to changes in community composition may allow us to improve the match between remotely‐sensed optical data and the underlying phytoplankton community.

  • Research Article

    Bumpy topographic effects on the transbasin evolution of large‐amplitude internal solitary wave in the northern South China Sea

    Abstract

    The bumpy continental slope/shelf topography is a quite common feature in the northern South China Sea (SCS), yet its effect on the shoaling internal solitary waves (ISWs) remains poorly understood. Therefore, numerical simulations by a fully nonlinear, nonhydrostatic model are carried out to explore the bumpy continental slope/shelf topographic effects on the transbasin evolution of large‐amplitude ISW in the northern SCS. It is found that the prominent bumps over both continental slope and shelf regions play significant roles in modulating the evolution of transbasin ISW in the northern SCS. The bump over the continental slope is capable of triggering a solitary‐like mode‐2 internal wave packet, while the bump over the continental shelf can result in three wave groups, including a leading group of rank‐ordered mode‐1 ISW packet, two following groups of non‐rank‐ordered mode‐1 ISW packet and mode‐2 internal waves. The bumps can cause a peak‐to‐peak difference of the energy decay rate of ISW up to 10–20 KW/m over continental slope region and 3–5 KW/m over continental shelf region. The wave kinetic energy (KE) is found to exceed the available potential energy (APE) by as much as 50% over the continental shelf break region. Over the shelf region, however, the bumps can first make the KE drop to as low as only 80% of the APE, but later the KE might bounce back to approximately 1.1–1.2 times of the APE. Both onshore‐ and offshore‐propagating beam‐like disturbances are found to be excited by the bumps. Except for the onshore‐propagating mode‐2 ISW packet, the reflected offshore‐propagating waves in different internal modes are also formed. These onshore‐ and offshore‐propagating multimodal internal waves can be clarified by the beam scattering and local generation mechanism.

  • Research Article
    Open Access

    Representing nitrogen, phosphorus, and carbon interactions in the E3SM Land Model: Development and global benchmarking

    Abstract

    Over the past several decades, the land modeling community has recognized the importance of nutrient regulation of the global terrestrial carbon cycle. Implementations of nutrient limitation in land models are diverse, varying from applying simple empirical down‐regulation of potential Gross Primary Productivity (GPP) under nutrient deficit conditions to more mechanistic treatments. In this study, we introduce a new approach to model multi‐nutrient (nitrogen (N), phosphorus (P)) limitations in the Energy Exascale Earth System Model (E3SM) Land Model version 1 (ELMv1‐ECA). The development is grounded on (1) advances in representing multiple‐consumer, multiple‐nutrient competition; (2) a generic dynamic allocation scheme based on water, N, P, and light availability; (3) flexible plant CNP stoichiometry; (4) prognostic treatment of N and P constraints on several carbon cycle processes; and (5) global datasets of plant physiological traits. Through benchmarking the model against best knowledge of global plant and soil carbon pools and fluxes, we show that our implementation of nutrient constraints on the present‐day carbon cycle is robust at the global scale. Compared with predecessor versions, ELMv1‐ECA better predicts global‐scale gross primary productivity, ecosystem respiration, leaf area index, vegetation biomass, soil carbon stocks, evapotranspiration, N2O emissions, and NO3 leaching. Factorial experiments indicate that representing the phosphorus cycle improves modeled carbon fluxes, while considering dynamic allocation improves modeled carbon stock density. We also highlight the value of using the International Land Model Benchmarking package (ILAMB) to evaluate and document performance during model development.