Representation In Models Research Articles

Modulation of working memory duration by synaptic and astrocytic mechanisms.

Short-term synaptic plasticity and modulations of the presynaptic vesicle release rate are key components of many working memory models. At the same time, an increasing number of studies suggests a potential role of astrocytes in modulating higher cognitive function such as WM through their influence on synaptic transmission. Which influence astrocytic signaling could have on the stability and duration of WM representations, however, is still unclear. Here, we introduce a slow, activity-dependent astrocytic regulation of the presynaptic release probability in a synaptic attractor model of WM. We compare and analyze simulations of a simple WM protocol in firing rate and spiking networks with and without astrocytic regulation, and underpin our observations with analyses of the phase space dynamics in the rate network. We find that the duration and stability of working memory representations are altered by astrocytic signaling and by noise. We show that astrocytic signaling modulates the mean duration of WM representations. Moreover, if the astrocytic regulation is strong, a slow presynaptic timescale introduces a 'window of vulnerability', during which WM representations are easily disruptable by noise before being stabilized. We identify two mechanisms through which noise from different sources in the network can either stabilize or destabilize WM representations. Our findings suggest that (i) astrocytic regulation can act as a crucial determinant for the duration of WM representations in synaptic attractor models of WM, and (ii) that astrocytic signaling could facilitate different mechanisms for volitional top-down control of WM representations and their duration.

Apportionment of the Pre‐Industrial to Present‐Day Climate Forcing by Methane Using UKESM1: The Role of the Cloud Radiative Effect

AbstractThe Year 1850 to 2014 increase in methane from 808 to 1831 ppb leads to an effective radiative forcing (ERF) of 0.97 ± 0.04 W m−2 in the United Kingdom's Earth System Model, UKESM1. The direct methane contribution is 0.54 ± 0.04 W m−2. It is better represented in UKESM1 than in its predecessor model HadGEM2 due to shortwave and longwave absorption improvements and the absence of an anomalous dust response in the UKESM1 simulations. An indirect ozone ERF of 0.13–0.20 W m−2 is due to the tropospheric ozone increase outweighing that of the stratospheric decrease. The indirect water vapor ERF of 0.02–0.07 W m−2 is consistent with previous estimates. The methane increase also leads to a cloud radiative effect of 0.12 ± 0.02 W m−2 from thermodynamic adjustments and aerosol‐cloud interactions (aci). Shortwave and longwave contributions of 0.23 and −0.35 W m−2 to the cloud forcing arise from radiative heating and stabilization of the upper troposphere, reducing convection and global cloud cover. The aerosol‐mediated contribution (0.28–0.30 W m−2) is due to changes in oxidants reducing new particle formation (−8%), shifting the aerosol size distribution toward fewer but larger particles. Cloud droplet number concentration decreases and cloud droplet effective radius increases. This reduction in the Twomey effect switches the cloud forcing sign (−0.14 to 0.12 W m−2) and is due to chemistry‐aerosol‐cloud coupling in UKESM1. Despite uncertainties in rapid adjustments and process representation in models, these results highlight the potential importance of chemistry‐aerosol‐cloud interactions and dynamical adjustments in climate forcing.

Structured Sparse Coding With the Group Log-regularizer for Key Frame Extraction

Key frame extraction based on sparse coding can reduce the redundancy of continuous frames and concisely express the entire video. However, how to develop a key frame extraction algorithm that can automatically extract a few frames with a low reconstruction error remains a challenge. In this paper, we propose a novel model of structured sparse-coding-based key frame extraction, wherein a nonconvex group log-regularizer is used with strong sparsity and a low reconstruction error. To automatically extract key frames, a decomposition scheme is designed to separate the sparse coefficient matrix by rows. The rows enforced by the nonconvex group log-regularizer become zero or nonzero, leading to the learning of the structured sparse coefficient matrix. To solve the nonconvex problems due to the log-regularizer, the difference of convex algorithm (DCA) is employed to decompose the log-regularizer into the difference of two convex functions related to the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$I_{1}$</tex> norm, which can be directly obtained through the proximal operator. Therefore, an efficient structured sparse coding algorithm with the group log-regularizer for key frame extraction is developed, which can automatically extract a few frames directly from the video to represent the entire video with a low reconstruction error. Experimental results demonstrate that the proposed algorithm can extract more accurate key frames from most SumMe videos compared to the state-of-the-art methods. Furthermore, the proposed algorithm can obtain a higher compression with a nearly 18% increase compared to sparse modeling representation selection (SMRS) and an 8% increase compared to SC-det on the VSUMM dataset.

Experimental evidence for modulation of slope effect on heterogeneous infiltrating surfaces by run-on

The influence of soil surface gradient on rainfall infiltration and related surface runoff generation are not well understood for representation in hydrologic modeling because of conflicting theoretical formulations and experimental results available in the scientific literature. The main objective of this study is to integrate and extend previous laboratory experiments using artificial rainfall over a laboratory setup with adjustable slope angle (α) and consisting of two adjacent natural bare soils that according to the USDA soil classification were of loam (Soil 1) and sandy loam (Soil 2) type, respectively. Soil 2, placed downstream, was subjected to the run-on process due to the overland flow produced over Soil 1. The slope effects are discussed on the basis of measurements of runoff surface (QS) and deep flow (Qd), both strictly linked to the infiltration rate, supported by a simplified rainfall-runoff model. Our results indicate that at a time close to the end of rainfall period (tre=8 h), for α increasing from 1° to 10°, Qd decreased by about 25% while QS exhibited a corresponding increase in magnitude. In the same slope range, at t → tre the total volume of water collected as deep flow decreased by about 40% for heavy rainfall and up to about 50% for light rainfall. On the other hand, a growth of the total surface water volume occurred such that the total outflow volume was nearly independent of α. The run-on effect is seen to modulate the differences in steady deep flows for different slopes. The above variations became of minor interest between 10° and 15°. The significant differences in the variations of surface and deep flow to changes of slope angle were analyzed and placed in context with those from earlier experiments. The differences are attributed to a different level of interaction between capillary and gravitational effects that should be represented in existing infiltration models, because appreciable values of α characterize most watershed areas and the decrease of infiltration with increasing α over bare soils can imply a greater contribution to the direct runoff and a significant reduction of the groundwater recharge with respect to a flat surface.

Mechanisms and effects of under-ice warming water in Ngoring Lake of Qinghai–Tibet Plateau

Abstract. The seasonal ice cover in lakes of the Qinghai–Tibet Plateau is a transient and vulnerable part of the cryosphere, whose characteristics depend on the regional climate: strong solar radiation in the context of the dry and cold environment because of the high altitude and relatively low latitude. We use the first under-ice temperature observations from the largest Tibetan freshwater lake, Ngoring Lake, and a one-dimensional lake model to quantify the mechanism of solar thermal accumulation under ice, which relies on the ice optical properties and weather conditions, as well as the effect of the accumulated heat on the land–atmosphere heat exchange after the ice breakup. The model was able to realistically simulate the feature of the Ngoring Lake thermal regime: the “summer-like” temperature stratification with temperatures exceeding the maximum density point of 3.98 ∘C across the bulk of the freshwater column. A series of sensitivity experiments revealed solar radiation was the major source of under-ice warming and demonstrated that the warming phenomenon was highly sensitive to the optical properties of ice. The heat accumulated under ice contributed to the heat release from the lake to the atmosphere for 1–2 months after ice-off, increasing the upward sensible and latent surface heat fluxes on average by ∼ 50 and ∼ 80 W m−2, respectively. Therefore, the delayed effect of heat release on the land–atmosphere interaction requires an adequate representation in regional climate modeling of the Qinghai–Tibet Plateau and other lake-rich alpine areas.

Five years calibrated observations from the University of Bonn X-band weather radar (BoXPol)

Polarimetric weather radars offer a wealth of new information compared to conventional technology, not only to enhance quantitative precipitation estimation, warnings, and short-term forecasts, but also to improve our understanding of precipitation generating processes and their representation in numerical weather prediction models. To support such research opportunities, this paper describes an open-access dataset between 2014–2019 collected by the polarimetric Doppler X-band weather radar in Bonn (BoXPol), western Germany. To complement this dataset, the technical radar characteristics, scanning strategy and the best-practice for radar data processing are detailed. In addition, an investigation of radar calibration is presented. Reflectivity measurements from the Dual-frequency Precipitation Radar operating on the core satellite of the Global Precipitation Mission are compared to those of BoXPol to provide absolute calibration offsets with the dataset. The Relative Calibration Adjustment technique is applied to identify stable calibration periods. The absolute calibration of differential reflectivity is determined using the vertical scan and provided with the BoxPol dataset.

Propagation of measurement error in opinion dynamics models: The case of the Deffuant model

Opinion dynamics models have an enormous potential for studying current phenomena such as vaccine hesitancy or diffusion of fake news. Unfortunately, to date, most of the models have little to no empirical validation. One major problem in testing these models against real-world data relates to the difficulties in measuring opinions in ways that map directly to representations in models. Indeed, measuring opinions is a complex process and presents more types of measurement error than just classical random noise. Thus, it is crucial to know how these different error types may affect the model’s predictions. In this work, we analyze this relationship in the Deffuant model as an example. Starting from the psychometrics literature, we first discuss how opinion measurements are affected by three types of errors: random noise, binning, and distortions (i.e. uneven intervals between scale points). While the first two are known to most of the scientific community, the third one is mostly unknown outside psychometrics. Because of that, we highlight the nature and peculiarities of each of these measurement errors. By simulating these types of error, we show that the Deffuant model is robust to binning but not to noise and distortions. Indeed, if a scale has 4 or more points (like most self-report scales), binning has almost no effect on the final predictions. However, prediction error increases almost linearly with random noise, up to a maximum error of 40%. After reaching this value, increasing the amount of noise does not worsen the prediction. Distortions are most problematic, reaching a maximum prediction error of 80%. Error propagation is already established in other fields, such as statistics and engineering. We believe its application in opinion dynamics will contribute to the expansion and development of this field. Indeed, as we show here, it allows researchers to test models’ reliability and prediction quality even before testing the model against real world data.

Changes in Global Ocean Circulation due to Isopycnal Diffusion

Abstract We investigate changes in the ocean circulation due to the variation of isopycnal diffusivity (κiso) in a global non-eddy-resolving model. Although isopycnal diffusion is thought to have minor effects on interior density gradients, the model circulation shows a surprisingly large sensitivity to the changes: with increasing κiso, the strength of the Atlantic residual overturning circulation (AMOC) and the Antarctic Circumpolar Current (ACC) transport weaken. At high latitudes, the isopycnal diffusion diffuses temperature and salinity upward and poleward, and at low latitudes downward close to the surface. Increasing isopycnal diffusivity increases the meridional isopycnal fluxes whose meridional gradient is equatorward, hence leading to a negative contribution to the flux divergence in the tracer equations and predominant cooling and freshening equatorward of 40°. The effect on temperature overcompensates the countering effect of salinity diffusion, such that the meridional density differences decrease, along with which ACC and AMOC decrease. We diagnose the adjustment process to the new equilibrium with increased isopycnal diffusion to assess how the other terms in the tracer equations react to the increased κiso. It reveals that around ±40° latitude, the cooling induced by the increased isopycnal flux is only partly compensated by warming by advection, explaining the net cooling. Overall, the results emphasize the importance of isopycnal diffusion on ocean circulation and dynamics, and hence the necessity of its careful representation in models. Significance Statement The effect of mixing by mesoscale eddies, represented as diffusion along surfaces of constant density in models, on the ocean circulation is not well understood. Here, we show that an increase in the eddy diffusivity in different setups of a global ocean model leads to a surprisingly large change of the ocean circulation. The strength of the Atlantic overturning circulation and the Antarctic Circumpolar Current decrease. We find that the interior ocean becomes cooler and fresher and that the temperature effect on density dominates over salinity, resulting in a decrease in the density gradients. Our results point out the importance of eddy diffusion on ocean circulation, and hence the necessity of its correct representation in ocean and climate models.

Evaluating molecular representations in machine learning models for drug response prediction and interpretability.

Machine learning (ML) is increasingly being used to guide drug discovery processes. When applying ML approaches to chemical datasets, molecular descriptors and fingerprints are typically used to represent compounds as numerical vectors. However, in recent years, end-to-end deep learning (DL) methods that can learn feature representations directly from line notations or molecular graphs have been proposed as alternatives to using precomputed features. This study set out to investigate which compound representation methods are the most suitable for drug sensitivity prediction in cancer cell lines. Twelve different representations were benchmarked on 5 compound screening datasets, using DeepMol, a new chemoinformatics package developed by our research group, to perform these analyses. The results of this study show that the predictive performance of end-to-end DL models is comparable to, and at times surpasses, that of models trained on molecular fingerprints, even when less training data is available. This study also found that combining several compound representation methods into an ensemble can improve performance. Finally, we show that a post hoc feature attribution method can boost the explainability of the DL models.

Light absorption and albedo reduction by pigmented microalgae on snow and ice

AbstractPigmented microalgae inhabiting snow and ice environments lower the albedo of glacier and ice-sheet surfaces, significantly enhancing surface melt. Our ability to accurately predict their role in glacier and ice-sheet surface mass balance is limited by the current lack of empirical data to constrain their representation in predictive models. Here we present new empirical optical properties for snow and ice algae and incorporate them in a radiative transfer model to investigate their impact on snow and ice surface albedo. We found ice algal cells to be more efficient absorbers than snow algal cells, but their blooms had comparable impact on surface albedo due to the different photic conditions of their habitats. We then used the model to reconstruct the effect of ice algae on bare ice albedo spectra collected at our field site in southern Greenland, where blooms dropped the albedo locally by between 3 and 43%, equivalent to 1–10 L m$^{-2}$ d$^{-1}$ of melted ice. Using the newly parametrized model, future studies could investigate biological albedo reduction and algal quantification from remote hyperspectral and multispectral imagery.

Improving the representation of sugarcane crop in the Joint UK Land Environment Simulator (JULES) model for climate impact assessment

AbstractBioenergy from sugarcane production is considered a key mitigation strategy for global warming. Improving its representation in land surface models is important to further understand the interactions between climate and bioenergy production, supporting accurate climate projections and decision‐making. This study aimed to calibrate and evaluate the Joint UK Land Environment Simulator (JULES) for climate impact assessments in sugarcane. A dataset composed of soil moisture, water and carbon fluxes and biomass measurements from field experiments across Brazil was used to calibrate and evaluate JULES‐crop and JULES‐BE parametrizations. The ability to predict the spatiotemporal variability of sugarcane yields and the impact of climate change was also tested at five Brazilian microregions. Parameters related to sugarcane allometry, crop growth and development were derived and tested for JULES‐crop and JULES‐BE, together with the response to atmospheric carbon dioxide, temperature and low‐water availability (CTW‐response). Both parametrizations showed comparable performance to other sugarcane dynamic models, with a root mean squared error of 6.75 and 6.05 t ha−1 for stalk dry matter for JULES‐crop and JULES‐BE, respectively. The parametrizations were also able to replicate the average yield patterns observed in the five microregions over 30 years when the yield gap factors were taken into account, with the correlation (r) between simulated and observed interannual variability of yields ranging from 0.33 to 0.56. An overall small positive trend was found in future yield projections of sugarcane using the new calibrations, with exception of the Jataí mesoregion which showed a clear negative trend for the SSP5 scenario from the years 2070 to 2100. Our simulations showed that an abrupt negative impact on sugarcane yields is expected if daytime temperatures above 35°C become more frequent. The newly calibrated version of JULES can be applied regionally and globally to help understand the interactions between climate and bioenergy production.

Cross-correlation corrected friction in generalized Langevin models: Application to the continuous Asakura-Oosawa model.

The development of dynamically consistent coarse-grained models for molecular simulations is often based on generalized Langevin equations, motivated by the application of the projection operator formalism (Mori-Zwanzig theory). While Mori's projection operator yields linear generalized Langevin equations that can be computationally efficiently implemented in numerical simulations, the downside is that Mori's generalized Langevin equation does not encompass the multi-body potential of mean force required to correctly encode structural and thermodynamic properties in coarse-grained many-body systems. Zwanzig's projection operator yields nonlinear generalized Langevin equations including the multi-body potential of mean force, while the remaining force contributions are not as cheap to implement in molecular simulation without making it formally hard to justify approximations. For many-particle coarse-grained models, due to computational and conceptual simplicity, an often used approach is to combine nonlinear conservative interactions with linear expressions to model dissipation. In a previous study [V. Klippenstein and N. F. A. van der Vegt, J. Chem. Phys. 154, 191102 (2021)], we proposed a method to parameterize such models to achieve dynamic consistency in coarse-grained models, allowing us to reconcile Mori's and Zwanzig's approach for practical purposes. In the current study, by applying the same strategy, we develop coarse-grained implicit solvent models for the continuous Asakura-Oosawa model, which under certain conditions allows us to develop very accurate coarse-grained potentials. By developing coarse-grained models for different reference systems with varying parameters, we test the broader applicability of the proposed procedure and demonstrate the relevance of accurate coarse-grained potentials in bottom-up derived dissipative models. We study how different system parameters affect the dynamic representability of the coarse-grained models. In particular, we find that the quality of the coarse-grained potential is crucial to correctly model the backscattering effect due to collisions on the coarse-grained scale. As hydrodynamic interactions are not explicitly modeled in the presented coarse-graining approach, deviations are observed in the long-time dynamics. The Asakura-Oosawa model allows for the tuning of system parameters to gain an improved understanding of this limitation. We also propose three new iterative optimization schemes to fine-tune the generalized Langevin thermostat to exactly match the reference velocity-autocorrelation function.

Chemistry Learning Using Multiple Representations: A Systematic Literature Review

The abstractness of the chemistry concept can be understood easily through chemistry learning using multiple representations. This article used the Systematic Literature Review (SLR) method to review eleven articles published from 2012 to 2021 and focused on chemistry learning using various representations. The articles are systematically obtained from the online article database ERIC, Scopus, and SINTA. The purpose of a review is to give information to teachers and researchers in chemistry education about the definition of multiple representations, the influence of multiple representations on chemistry learning outcomes, and how to implement various representations in chemistry learning models or strategies. The review results showed that the definition of numerous representations referred to both three levels of chemical representation and the tetrahedral representation of chemistry. Also, it referred to the use of various media. The influence of multiple representations on chemistry learning outcomes included improving concept understanding, improving performance, reducing mental effort, improving self-efficacy, making better cognitive structures, improving mental models, and reducing misconceptions. Multiple representations have also been implemented in several learning models or strategies such as Inquiry, Inquiry 5E, Guided Inquiry, Problem Solving, Thinking, Aloud Pair Problem Solving (TAPPS), Problem Posing (PP), Cognitive Dissonance, and Multiple Representation Based Learning (MRL).

Cloud radar perspective on tropical warm clouds associated with summer monsoon rainfall and warm rain onset process

The role of warm clouds in monsoon rainfall and the warm rain onset process are investigated using high-resolution cloud-sensitive radar measurements. Quality controlled data from Ka-band radar consisting of 1.8 million profiles during July–August 2015, have been explored. A method is devised using the Ze profiles, for identifying warm clouds whose cloud tops are confined below 5.3 km. Warm rain microphysical parameters are examined utilizing the complemented measurements from Disdrometer, rain gauge, and CloudSat data. The strong temperature inversion of 2 to 4 K km−1 at 3 km explained the generation of shallow warm clouds. Strong low-level updraft and subsidence above the warm clouds are also evident. Contoured frequency by altitude diagram infers that 75% of Ze (velocity) dominates at −20 dBZe (−1 m s−1). Simultaneous Disdrometer measurements revealed 78% of cases with zero-drop count and zero rain rates. The dominating non-precipitating characteristics of warm clouds are also confirmed from the CloudSat inferred cloud droplet effective radius below 10 μm. During the rainy episodes, peak at 0.91 mm in the drop size distribution suggests prominence of drizzle-sized drops. The precipitation caused by warm clouds accounts for a small fraction (28%) of the total rainfall. The warm rain onset process involving the transformation of smaller cloud droplets into big raindrops is investigated using the radar's three lower-order moments. Before cloud droplet to raindrop transition an intense spike in the spectral width (SW) is observed for a brief period in 98% of the cases. Therefore, SW, which infers turbulence, triggers the collision-coalescence of cloud droplets to form raindrops. This study provides a multi-observation-based perspective on the macro- and micro-physical characteristics of monsoon warm clouds and the onset of warm rain for a better representation in the global climate models.

An Analysis of the Aerosol Lifecycle Over India: COALESCE Intercomparison of Three General Circulation Models

AbstractAtmospheric aerosols or atmospheric particulate matter affects climate variables like temperature and rainfall, agricultural productivity, soil, and human health. We evaluated aerosol lifecycle over India via simulations (2005–2014) from three general circulation models under the COALESCE project (carbonaceous aerosol emissions, source apportionment, and climate impacts; Venkataraman et al., 2020, 10.1175/bams‐d‐19‐0030.1). The ECHAM6.3‐HAM2.3, CAM5.3, and NICAM‐SPRINTARS simulations use identical regional emissions (from the Speciated Multi‐pollutant generator, SMoG‐India‐v1). Satisfactory model simulations of meteorological variable magnitudes and seasonal cycle have been achieved partly from the adoption of nudging. Estimations of anthropogenic aerosol, aerosol optical depth (AOD), and particulate matter surface concentrations are significantly improved from (a) dust tuning (b) use of satellite‐derived organic aerosol to carbon ratio, and (c) nudged meteorology to capture variables influencing the production of secondary sulfate. Larger wintertime under prediction (−30% to −60%) results from over prediction of seasonal planetary boundary layer height and the absence of secondary ammonium nitrate and organic aerosols. Vertical dispersion to higher altitudes than in observations calls for improved modeling of vertical mass flux representation. Carbonaceous aerosol residence time and AOD fraction larger than global mean values in India, with a seasonal predominance in the autumn and winter seasons can be explained by enhanced regional emissions from residential biofuel cooking, agricultural stubble burning, and traditional informal industries like brick production.

Neural Distributed Representations for Artificial Intelligence and Modeling of Thinking

Neural Distributed Representations for Artificial Intelligence and Modeling of Thinking

Impact of downward longwave radiative deficits on Antarctic sea-ice extent predictability during the sea ice growth period

Forecasting Antarctic atmospheric, oceanic, and sea ice conditions on subseasonal to seasonal scales remains a major challenge. During both the freezing and melting seasons current operational ensemble forecasting systems show a systematic overestimation of the Antarctic sea-ice edge location. The skill of sea ice cover prediction is closely related to the accuracy of cloud representation in models, as the two are strongly coupled by cloud radiative forcing. In particular, surface downward longwave radiation (DLW) deficits appear to be a common shortcoming in atmospheric models over the Southern Ocean. For example, a recent comparison of ECMWF reanalysis 5th generation (ERA5) global reanalysis with the observations from McMurdo Station revealed a year-round deficit in DLW of approximately 50 Wm−2 in marine air masses due to model shortages in supercooled cloud liquid water. A comparison with the surface DLW radiation observations from the Ocean Observatories Initiative mooring in the South Pacific at 54.08° S, 89.67° W, for the time period January 2016–November 2018, confirms approximately 20 Wm−2 deficit in DLW in ERA5 well north of the sea-ice edge. Using a regional ocean model, we show that when DLW is artificially increased by 50 Wm−2 in the simulation driven by ERA5 atmospheric forcing, the predicted sea ice growth agrees much better with the observations. A wide variety of sensitivity tests show that the anomalously large, predicted sea-ice extent is not due to limitations in the ocean model and that by implication the cause resides with the atmospheric forcing.

Investigating stable oxygen and carbon isotopic variability in speleothem records over the last millennium using multiple isotope-enabled climate models

Abstract. The incorporation of water isotopologues into the hydrology of general circulation models (GCMs) facilitates the comparison between modeled and measured proxy data in paleoclimate archives. However, the variability and drivers of measured and modeled water isotopologues, as well as the diversity of their representation in different models, are not well constrained. Improving our understanding of this variability in past and present climates will help to better constrain future climate change projections and decrease their range of uncertainty. Speleothems are a precisely datable terrestrial paleoclimate archives and provide well-preserved (semi-)continuous multivariate isotope time series in the lower latitudes and mid-latitudes and are therefore well suited to assess climate and isotope variability on decadal and longer timescales. However, the relationships of speleothem oxygen and carbon isotopes to climate variables are influenced by site-specific parameters, and their comparison to GCMs is not always straightforward. Here we compare speleothem oxygen and carbon isotopic signatures from the Speleothem Isotopes Synthesis and Analysis database version 2 (SISALv2) to the output of five different water-isotope-enabled GCMs (ECHAM5-wiso, GISS-E2-R, iCESM, iHadCM3, and isoGSM) over the last millennium (850–1850 CE). We systematically evaluate differences and commonalities between the standardized model simulation outputs. The goal is to distinguish climatic drivers of variability for modeled isotopes and compare them to those of measured isotopes. We find strong regional differences in the oxygen isotope signatures between models that can partly be attributed to differences in modeled surface temperature. At low latitudes, precipitation amount is the dominant driver for stable water isotope variability; however, at cave locations the agreement between modeled temperature variability is higher than for precipitation variability. While modeled isotopic signatures at cave locations exhibited extreme events coinciding with changes in volcanic and solar forcing, such fingerprints are not apparent in the speleothem isotopes. This may be attributed to the lower temporal resolution of speleothem records compared to the events that are to be detected. Using spectral analysis, we can show that all models underestimate decadal and longer variability compared to speleothems (albeit to varying extents). We found that no model excels in all analyzed comparisons, although some perform better than the others in either mean or variability. Therefore, we advise a multi-model approach whenever comparing proxy data to modeled data. Considering karst and cave internal processes, e.g., through isotope-enabled karst models, may alter the variability in speleothem isotopes and play an important role in determining the most appropriate model. By exploring new ways of analyzing the relationship between the oxygen and carbon isotopes, their variability, and co-variability across timescales, we provide methods that may serve as a baseline for future studies with different models using, e.g., different isotopes, different climate archives, or different time periods.

Source and transport controls on nutrient delivery to tile drains

Apportioning sources, assessing flow pathways, and quantifying the interaction between source and transport factors are critical for decreasing nutrient loss from agricultural catchments. In this study, water, nutrient, and tracer (δ18O, electrical conductivity) fluxes were measured over two years from a tile-drained field in Indiana, USA to quantify linkages among water flow pathways, management practices, and nutrient delivery to tile drains. Three-component hydrograph separation showed that tile discharge was, on average, sourced from groundwater (65 ± 31%), shallow soil water (10–20-cm deep; 29 ± 28%), and precipitation (6 ± 8%). Daily nitrate-N (NO3-N) and dissolved reactive phosphorus (DRP) load ranged from 0.0 to 1.8 kg ha−1 and 0–101 g ha−1, respectively, while cumulative loads were 30.7 kg ha−1 and 1,040 g ha−1. Nutrient management practices (fertilizer rate, placement, timing) directly influenced the magnitude of incidental nutrient loss and proportion of load that was derived from a fertilizer source. It was estimated that 52% and 46% of cumulative NO3-N and DRP load, respectively, was incidental loss that occurred within 30 d of fertilizer application. Continuous re-distribution of NO3-N throughout the soil profile following fertilizer application resulted in a broad range of concentration across flow pathways and antecedent conditions. In contrast, dry conditions with tile water sourced from shallow soil water or precipitation resulted in greater, but more variable, DRP concentration compared to wetter conditions when tile water was largely comprised of groundwater. Findings suggest that groundwater table dynamics exerted a strong control over DRP concentration, as similar surface-tile hydrologic connectivity occurred during both dry and wet conditions but produced different water quality outcomes. Groundwater may therefore serve both as a chemical and hydrologic buffer of DRP concentration. Combined measurements of water, nutrient, and tracer fluxes revealed novel insights into processes controlling nutrient delivery to tile drains, with direct applicability for conservation practice implementation and improving process representation in models.

Congestus Mode Invigoration by Convective Aggregation in Simulations of Radiative-Convective Equilibrium.

This study examines how the congestus mode of tropical convection is expressed in numerical simulations of radiative‐convective equilibrium (RCE). We draw insights from the ensemble of cloud‐resolving models participating in the RCE Model Intercomparison Project (RCEMIP) and from a new ensemble of two‐dimensional RCE simulations. About half of the RCEMIP models produce a congestus circulation that is distinct from the deep and shallow modes. In both ensembles, the congestus circulation strengthens with large‐scale convective aggregation, and in the 2D ensemble this comes at the expense of the shallow circulation centered at the top of the boundary layer. Congestus invigoration occurs because aggregation dries out the upper troposphere, which allows moist congestus outflow to undergo strong radiative cooling. The cooling generates divergence that promotes continued congestus overturning (a positive feedback). This mechanism is fundamentally similar to the driving of shallow circulations by radiative cooling at the top of the surface boundary layer. Aggregation and congestus invigoration are also associated with enhanced static stability throughout the troposphere, but a modeling experiment shows that enhanced stability is not necessary for congestus invigoration; rather, invigoration itself contributes to the stability increase via its impact on the vertical profile of radiative cooling. Changes in entrainment cooling are also found to play an important role in stability enhancement, as has been suggested previously. When present, congestus circulations have a large impact on the mean RCE atmospheric state; for this reason, their inconsistent representation in models and their impact on the real tropical atmosphere warrant further scrutiny.