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The global-mean climate feedback quantifies how much the climate system will warm in response to a forcing such as increased CO2 concentration. Under a constant forcing, this feedback becomes less negative (increasing) over time in comprehensive climate models, which has been attributed to increases in cloud and lapse-rate feedbacks. However, out of eight Earth system models of intermediate complexity (EMICs) not featuring interactive clouds, two also simulate such a feedback increase: Bern3D-LPX and LOVECLIM. Using these two models, we investigate the causes of the global-mean feedback increase in the absence of cloud feedbacks. In both models, the increase is predominantly driven by processes in the Southern Ocean region. In LOVECLIM, the global-mean increase is mainly due to a local longwave feedback increase in that region, which can be attributed to lapse-rate changes. It is enhanced by the slow atmospheric warming above the Southern Ocean, which is delayed due to regional ocean heat uptake. In Bern3D-LPX, this delayed regional warming is the main driver of the global-mean feedback increase. It acts on a near-constant local feedback pattern mainly determined by the sea ice–albedo feedback. The global-mean feedback increase is limited by the availability of sea ice: faster Southern Ocean sea ice melting due to either stronger forcing or higher equilibrium climate sensitivity (ECS) reduces the increase of the global mean feedback in Bern3D-LPX. In the highest-ECS simulation with 4 × CO2 forcing, the feedback even becomes more negative (decreasing) over time. This reduced ice–albedo feedback due to sea ice depletion is a plausible mechanism for a decreasing feedback also in high-forcing simulations of other models.

An intermediate-complexity moist general circulation model is used to investigate the forcing of stationary waves in the Northern Hemisphere boreal winter by land–sea contrast, horizontal heat fluxes in the ocean, and topography. The additivity of the response to these building blocks is investigated. In the Pacific sector, the stationary wave pattern is not simply the linear additive sum of the response to each forcing. In fact, over the northeast Pacific and western North America, the sum of the responses to each forcing is actually opposite to that when all three are imposed simultaneously due to nonlinear interactions among the forcings. The source of the nonlinearity is diagnosed using the zonally anomalous steady-state thermodynamic balance, and it is shown that the background-state temperature field set up by each forcing dictates the stationary wave response to the other forcings. As all three forcings considered here strongly impact the temperature field and its zonal gradients, the nonlinearity and nonadditivity in our experiments can be explained, but only in a diagnostic sense. This nonadditivity extends up to the stratosphere, and also to surface temperature, where the sum of the responses to each forcing differs from the response if all forcings are included simultaneously. Only over western Eurasia is additivity a reasonable (though not perfect) assumption; in this sector land–sea contrast is most important over Europe, while topography is most important over western Asia. In other regions, where nonadditivity is pronounced, the question of which forcing is most important is ill-posed.

Consider a fundamental problem in microeconomics: selling a single item to a number of potential buyers, who independently draw their values from regular and publicly known distributions. There are four mechanisms widely studied in the literature and widely used in practice: Myerson Auction (OPT), Sequential Posted Pricing (SPM), Second-Price Auction with Anonymous Reserve (AR) , and Anonymous Pricing (AP). OPT is revenue-optimal but complicated, which also experiences several practical issues such as fairness. AP is the simplest mechanism, but also generates the lowest revenue among these four mechanisms. SPM and AR are of intermediate complexity and revenue. A quantitative approach to comparing the relative power of these mechanisms is to study their revenue gaps, each of which is defined as the largest ratio between the revenues from a pair of mechanisms. This letter surveys some recent developments on establishing tight revenue gaps, and highlights some open questions.

Decisions on how to manage future flood risks are frequently informed by both sophisticated and computationally expensive models. This complexity often limits the representation of uncertainties and the consideration of strategies. Here we use an intermediate complexity model framework that enables us to analyze a richer set of strategies, a wider range of objectives, and greater levels of uncertainty than are typically considered by more sophisticated and computationally expensive models. We find that allowing for more combinations of risk mitigation strategies can help expand the solution set, help explain synergies and trade‐offs, and point to strategies that can improve outcomes.

Marine ecosystems, biodiversity, and fisheries are under strain worldwide due to global changes including climate warming and demographic pressure. To address this issue, many scientists and stakeholders advocate the use of an ecosystem approach for fisheries that integrates the numerous ecological and economic complexities at play rather than focusing on the management of individual target species. However, the operationalization of such an ecosystem approach remains challenging, especially from a bio-economic standpoint. Here, to address this issue, we propose a model of intermediate complexity (MICE) relying on multi-species, multi-fleet, and resource-based dynamics. Climate change effects are incorporated through an envelope model for the biological growth of fish species as a function of sea surface temperature. The model is calibrated for the small-scale fishery in French Guiana using a time series of fish landings and fishing effort from 2006 to 2018. From the calibrated model, a predictive fishing effort projection and RCP climate scenarios derived from IPCC, we explore the ecosystem dynamics and the fishery production at the horizon 2100. Our results demonstrate the long-term detrimental impact of both climate change and ecological competition on fish biodiversity. The prognosis is particularly catastrophic under the most pessimistic climate scenario, with a potential collapse of both biomass targeted species and fishing activity by 2100. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

RESUMEN Objetivo: determinar la adherencia global y por componentes a la estrategia minuto de oro en médicos que atienden recién nacidos en una entidad de mediano nivel de complejidad. Materiales y métodos: estudio de corte transversal; se incluyeron recién nacidos vivos de partos vaginales espontáneos atendidos por médicos pediatras, ginecólogos o internos en sala de partos de un hospital universitario de la ciudad de Cali, Colombia, en el 2017. Se excluyeron fetos con mal- formaciones congénitas mayores, gemelares y con menos de 34 semanas de edad gestacional. Muestreo sistemático. Tamaño muestral: 150 recién nacidos. Se evaluaron las características basales de los recién nacidos y sus madres, y la adherencia a la estrategia minuto de oro y sus componentes. Se hizo análisis descriptivo. Resultados: la adherencia al minuto de oro en médicos pediatras fue del 65,6 % (IC 95 %: 53,8-78,4), en ginecobstetras, de 33,33 % (IC 95 %: 4,3-77,7), y en médicos internos, de 75,3 % (IC 95 %: 64,8- 85,1). La menor frecuencia se dio en la colocación del gorro al recién nacido, 64,90 % (IC 95 %: 56,7- 72,4), y poner al bebé piel a piel sobre la madre, 65 % (IC 95 %: 55,9-74,4); la mayor frecuencia se presentó en cubrir al recién nacido con paños calientes, 98,6 % (IC 95 %: 95,3-99,8), y la ventilación con presión positiva en los casos en los que no había respuesta a la estimulación inicial, 100 % (IC 95 %: 30-100). Conclusiones: los resultados obtenidos sobre el grado de adherencia de los profesionales sugieren la necesidad de realizar procesos continuos de educación y evaluación sobre la aplicación de esta estrategia de reconocida efectividad en las institu- ciones que ofrecen el servicio de atención de partos. ABSTRACT Objective: To determine adherence, overall and by components, to the Helping Babies Breathe strategy by physicians caring for neonates in an intermediate complexity institution. Materials and Methods: Cross-sectional study that included live neonates born by spontaneous vaginal delivery and who received care from pediatricians, gynecologists or interns in the delivery room of a university hospital in the city of Cali, Colombia, in 2017. Fetuses with major congenital malformations, twins, and neonates with less than 34 weeks of gestational age were excluded. Sampling was systematic and the sample size was of 150 neonates. Baseline neonatal and maternal characteristics were assessed, as well as adherence to the Helping Babies Breathe strategy and its components. A descriptive analysis was performed. Results: Adherence to the Helping Babies Breathe was 65.6% (95% CI 53.8-78.4) for pediatricians, 33.33% (95% CI: 4,3-77,7) for obstetricians and gynecologists, and 75.3% (95% CI: 64,8-85,1) for interns. The lowest frequency was found for cap placement on the neonate’s head, 64.90% (95% CI: 56.7-72.4), and placement of the baby in contact with the mother’s skin, 65% (95% CI: 55.9-74.4); the highest frequency was found for covering the baby with warm blankets, 98,6% (95% CI: 95.3-99.8), and positive pressure ventilation in those cases of absent response to initial stimulation, 100% (95% CI 30-100). Conclusions: Results pertaining to the degree of adherence on the part of the practitioners suggest the need to implement continuous education and evaluation processes focused on the application of this strategy which has been shown to be effective in institutions offering childbirth care.

The National Quality Control Program in Mycology (PNCCM) of Argentina was establishedin 1996 to improve the quality of the mycological diagnosis, to help establish and to setup standardized procedures and continuous training of laboratory staff. The aim of this studywas to assess the effectiveness of the PNCCM in the 1996---2018 period. Data from the NationalMycology Laboratory Network (NMLN) and PNCCM database was used to estimate the increasein the number of controlled laboratories and jurisdictions, the percentage of participation, theimprovement in the quality of results and the adherence to the program. Satisfaction surveyswere performed to assess user satisfaction. The number of controlled laboratories increasedfrom 29 to 146; participation increased from 49% to 93% and general adherence was 72% inthe evaluated period (1996---2018). Improvement in the quality of the results was 15% for lowcomplexity samples; 7% for intermediate complexity samples and 14% for the identification ofhigh complexity strains. Up to 84% of the users consider the PNCCM to be ‘‘very good’’ and 16%‘‘satisfactory’’. These results show the importance of the PNCCM, which is widely accepted bymycological diagnostic laboratories from Argentina. Resumen En 1996 se creó el Programa Nacional de Control de Calidad en Micología (PNCCM)de Argentina con el objetivo de mejorar la calidad del diagnóstico micológico, colaborar enel establecimiento de procedimientos estandarizados en aquellos laboratorios que carecen deellos y contribuir a la capacitación continua del personal.El objetivo de este estudio fue evaluar la efectividad del PNCCM en el período 1996-2018.Se utilizaron los datos de la base de la Red Nacional de Laboratorios de Micología (RNLM) ydel PNCCM para estimar el aumento en el número de laboratorios controlados y el porcentajede participación, la mejora de la calidad de los resultados y la adhesión al programa. Paraevaluar el grado de satisfacción de los usuarios, se analizaron las encuestas de satisfacción delos participantes. En el período evaluado, el número de laboratorios controlados aumentó de 29a 146, la participación aumentó de 49% a 93% y la adherencia general de los participantes fue del72%. La mejora de la calidad de los resultados de los laboratorios fue del 15% para muestras debaja complejidad, 7% para muestras de complejidad intermedia y 14% para la identificación decepas de alta complejidad. El 84% de los usuarios considera que el PNCCM es muy bueno y el 16%que es satisfactorio. Estos resultados evidencian la importancia del PNCCM, que es ampliamenteaceptado por los laboratorios que realizan diagnóstico micológico en nuestro país.

Snow deposition patterns in complex terrain are heavily dependent on the underlying topography. This topography affects precipitating clouds at the kilometer-scale and causes changes to the wind field at the sub-kilometer scale, resulting in altered advection of falling hydrometeors. Snow particles are particularly sensitive to changes in the near-surface flow field due to their low density. Atmospheric models which run at the kilometer scale cannot resolve the actual heterogeneity of the underlying terrain, resulting in precipitation maps which do not capture terrain-affected precipitation patterns. Thus, snow-atmosphere interactions such as preferential deposition are often not resolved in precipitation data used as input to snow models. To bridge this spatial gap and resolve snow-atmosphere interactions at the sub-kilometer scale, we couple an intermediate complexity atmospheric model (ICAR) to the COSMO NWP model. Applying this model to sub-kilometer terrain (horizontal resolution of 50 and 250 m) required changes to ICAR’s computational grid, atmospheric dynamics, and boundary layer flow. As a result, the near-surface flow now accounts for surface roughness and topographically induced speed up. This has been achieved by using terrain descriptors calculated once at initialization which consider a point’s exposure or sheltering relative to surrounding terrain. In particular, the use of a 3-dimensional Sx parameter allows us to simulate areas of stagnation and recirculation on the lee of terrain features. Our approach maintains the accurate large-scale precipitation patterns from COSMO but resolves the dynamics induced by terrain at the sub-kilometer scale without adding additional computational burden. We find that solid precipitation patterns at the ridge scale, such as preferential deposition of snow, are better resolved in the high-resolution version of ICAR than the current ICAR or COSMO models. This updated version of ICAR presents a new tool to dynamically downscale NWP output for snow models and enables future studies of snow-atmosphere interactions at domain scales of 100’s of kilometers.

The three-dimensional (3D) multicellular tumor spheroids (MCTs) model is becoming an essential tool in cancer research as it expresses an intermediate complexity between 2D monolayer models and in vivo solid tumors. MCTs closely resemble in vivo solid tumors in many aspects, such as the heterogeneous architecture, internal gradients of signaling factors, nutrients, and oxygenation. MCTs have growth kinetics similar to those of in vivo tumors, and the cells in spheroid mimic the physical interaction of the tumors, such as cell-to-cell and cell-to-extracellular matrix interactions. These similarities provide great potential for studying the biological properties of tumors and a promising platform for drug screening and therapeutic efficacy evaluation. However, MCTs are not well adopted as preclinical tools for studying tumor behavior and therapeutic efficacy up to now. In this review, we addressed the challenges with MCTs application and discussed various efforts to overcome the challenges.

In mountainous terrain, wind-driven transport of deposited snow affects the overall distribution of snow, and can have a significant effect on snowmelt patterns even at coarser resolution. In an operational modelling perspective, a compromise must be found to represent this complex small-scale process with enough accuracy while mitigating the computational costs of snow cover simulations over large domains. To achieve this compromise, we implemented the SNOWTRAN-3D snow transport module within the FSM intermediate complexity snow cover model. We included a new layering scheme and a historical variable of past snow wetting, but without resolving the snow microstructure. Simulations are run and evaluated over a small mountain range in the Swiss Alps at 25 to 100 m resolution. Being implemented in the model framework of the SLF operational snow hydrology service (OSHD), simulations further benefit from snow data assimilation techniques to provide improved estimates of solid precipitation fields. As complex wind patterns in mountains are the key processes driving snow transport, we tested statistical and dynamical methods to downscale 1 km resolution COSMO winds to better reflect topographically-induced flow patterns. These simulations are a first step working towards the integration of wind transport processes over large domains in an intermediate-complexity and -resolution operational modelling framework.

The global hydrological cycle is of crucial importance for life on Earth. Hence, it is a focus of both future climate projections and paleoclimate modeling. The latter typically requires long integrations or large ensembles of simulations, and therefore models of reduced complexity are needed to reduce the computational cost. Here, we study the hydrological cycle of the the Planet Simulator (PlaSim) [1], a general circulation model (GCM) of intermediate complexity, which includes evaporation, precipitation, soil hydrology, and river advection.Using published parameter configurations for T21 resolution [2, 3], PlaSim strongly underestimates precipitation in the mid-latitudes as well as global atmospheric water compared to ERA5 reanalysis data [4]. However, the tuning of PlaSim has been limited to optimizing atmospheric temperatures and net radiative fluxes so far [3].Here, we present a different approach by tuning the model’s atmospheric energy balance and water budget simultaneously. We argue for the use of the globally averaged mean absolute error (MAE) for 2 m temperature, net radiation, and evaporation in the objective function. To select relevant model parameters, especially with respect to radiation and the hydrological cycle, we perform a sensitivity analysis and evaluate the feature importance using a Random Forest regressor. An optimal set of parameters is obtained via Bayesian optimization.Using the optimized set of parameters, the mean absolute error of temperature and cloud cover is reduced on most model levels, and mid-latitude precipitation patterns are improved. In addition to annual zonal-mean patterns, we examine the agreement with the seasonal cycle and discuss regions in which the bias remains considerable, such as the monsoon region over the Pacific.We discuss the robustness of this tuning with regards to resolution (T21, T31, and T42), and compare the atmosphere-only results to simulations with a mixed-layer ocean. Finally, we provide an outlook on the applicability of our parametrization to climate states other than present-day conditions.[1] K. Fraedrich et al., Meteorol. Z. 14, 299–304 (2005)[2] F. Lunkeit et al., Planet Simulator User’s Guide Version 16.0 (University of Hamburg, 2016)[3] G. Lyu et al., J. Adv. Model. Earth Syst. 10, 207–222 (2018)[4] H. Hersbach et al., Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020)

Hysteresis diagrams of the Atlantic meridional overturning circulation (AMOC) under freshwater forcing from climate models of intermediate complexity are fitted to a simple model based on the Langevin equation. A total of six parameters are sufficient to quantitatively describe the collapses seen in these simulations. Reversing the freshwater forcing results in asymmetric behaviour that is less well captured and appears to require a more complicated model. The Langevin model allows for comparison between models that display an AMOC collapse. Differences between the climate models studied here are mainly due to the strength of the stable AMOC and the strength of the response to a freshwater forcing.

We apply two independent data analysis methodologies to locate stable climate states in an intermediate complexity climate model and analyze their interplay. First, drawing from the theory of quasipotentials, and viewing the state space as an energy landscape with valleys and mountain ridges, we infer the relative likelihood of the identified multistable climate states, and investigate the most likely transition trajectories as well as the expected transition times between them. Second, harnessing techniques from data science, specifically manifold learning, we characterize the data landscape of the simulation output to find climate states and basin boundaries within a fully agnostic and unsupervised framework. Both approaches show remarkable agreement, and reveal, apart from the well known warm and snowball earth states, a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production via the hydrological cycle drastically change the topography of the dynamical landscape of Earth's climate., 28 pages, 12 figures plus Supplementary Material. Revised version

Using a machine learning technique, collapse trajectories of the Atlantic Meridional Overturning Circulation from climate models of intermediate complexity are fitted to a simple model based on the Langevin equation. A total of six parameters are sufficient to quantitatively describe the collapses seen in these simulations under a freshwater forcing. Reversing the freshwater forcing results in asymmetric behaviour that is less well captured and would require a more complicated model.

We present estimates of carbon budgets for different levels of surface air temperature (SAT) increase from multiple 400-member ensembles of simulations with the MIT Earth System Model of intermediate complexity (MESM). Ensembles were carried out using distributions of climate parameters affecting climate system response to external forcing obtained by comparison of historical simulations with available observations.First, to evaluate MESM performance, we ran two ensembles: one with MESM forced by 1% per year increase in CO2 concentrations (and with non-CO2 greenhouse gases (GHG) at pre-industrial level) and the other with GHG concentrations from the RCP 8.5 scenario. Distributions of climate characteristics describing model response to increasing CO2 concentrations (e.g. TRC and TCRE) as well as values of carbon budgets of exceeding different SAT levels agree well with published estimates.Then we ran a number of ensembles with MESM driven by emissions produced by the MIT Economic Projection and Policy Analysis (EPPA) Model. Our results show that under stringent mitigation policy concerning non-CO2 GHGs, the SAT increase can be kept below 2°C relative to pre-industrial with 66% probability through the end of the 21st century without negative CO2 emissions. The SAT increase can also be restricted to 1.5°C with 50% probability if such policy is implemented immediately. If GHG emissions follow the path implied by the Paris Agreement pledges through 2030, then it would require either an unrealistically sharp drop in non-CO2 GHG emissions or negative CO2 emissions to stay below 1.5°C. Keeping the temperature increase below a chosen value (1.5°C or 2°C) beyond 2100 will most likely require negative CO2 emissions in part due to difficulties in restricting agricultural methane emissions.Further analysis shows that temperature change during the 21st century is also significantly affected by the assumption concerning decrease of SO2 emissions from energy-intensive industries. Implementation of technologies resulting in reduction of those emissions will decrease the probability of SAT staying below a given limit by about 7-12%. This will affect the time when negative CO2 emissions will become necessary to prevent temperature increase.

When is good enough, good enough? The spatio-temporal variability of precipitation makes measurements extremely challenging, particularly in the mountains. Simultaneously, the improvements in physical realism of atmospheric models makes them increasingly valuable for fields such as hydrology, particularly in the mountains. However, the computational cost of such models renders them impractical for many applications, in or out of the mountains. Here we describe an intermediate complexity atmospheric model (ICAR) capable of capturing around 90% of the variability in orographic precipitation for 1% of the computational cost of a state of the science non-hydrostatic atmospheric simulation. ICAR uses an analytical solution for flow perturbations created by topography and simulates the core processes responsible for orographic precipitation (e.g. orographic lifting, advection, cloud microphysical processes). We show that key aspects of orographic precipitation spatial patterns are well simulated in ICAR, including some that gridded observation based products are missing. We then show some early results when using ICAR to simulate regional climate changes forced by global models at higher spatial resolutions than it is currently practical to run traditional regional climate models. These simulations quantify plausible shifts in precipitation resulting in the transition from snow to rain, as well as elevation dependent warming caused by the snow albedo feedback. Further, the computational efficiency of ICAR permits us to run these simulations with many different physics configurations to better explore the sensitivity of these changes to assumptions in the microphysics and land surface model components.

The output of general circulation models is too coarse to adequately capture the features influencing local climate and weather, particularly in complex topography. To asses the long-term impact of a changing global climate in mountainous regions, regional climate models need to be run on a fine spatial and temporal grid. Here the Intermediate Complexity Atmospheric Research (ICAR) model is a computationally frugal and physics based alternative to full physics regional climate models such as the Weather Research and Forecasting (WRF) model. A sizable portion of the computational efficiency of ICAR stems from its application of linear mountain wave theory to determine the wind field in the domain, thereby avoiding a numerical solution of the Navier-Stokes equations of motion. Heat, moisture and other atmospheric quantities are then advected in this wind field. Microphysical conversion processes between water vapor and various hydrometeor species are handled by a complex microphysics scheme. Altogether ICAR does not require measurements and enables computationally cheap downscaling, particularly in mountainous regions with complex topography, yielding a physically consistent set of atmospheric variables. However, in a real-world application and evaluation of ICAR we observed a strong sensitivity of the model performance to the elevation of the model top (Horak et al., 2019).We present three recommendations, derived from idealized simulations, that improve different aspects of ICAR simulations. The simulations constitute an idealized ridge experiment with a non-dimensional mountain height of 0.5. The ridge is specified by a witch of Agnesi function and the sounding characterized by a saturated, horizontally and vertically homogeneous atmosphere with constant and stable stratification. The wind field calculated by ICAR is compared to the exact analytical solution. Furthermore, the water vapor, suspended hydrometeor and precipitating hydrometeor fields are used as proxies to identify inconsistencies in the model output, such as the dependence of the results on the elevation of the model top. To highlight the deviations of ICAR results from a full physics model, resulting from non-linearities in the wind field, the ICAR output was additionally compared to that of a WRF simulation. The results of our investigation strongly suggest that ICAR simulations can be significantly improved by (i) calculating the Brunt-Väisälä frequency from the forcing data set instead of the perturbed state of the atmosphere, (ii) setting the model top to an elevation of at least 11.4 km and, (iii) by applying a zero value boundary condition to the water vapor and hydrometeor species at the model top. To our knowledge none of the preceding studies employing ICAR satisfied these three conditions. Overall our investigation deepens the understanding of the ICAR model sensitivity to crucial model components, thereby increasing the potential of the model as a tool for long-term impact studies in data-sparse regions with complex topography.ReferencesHorak, J., Hofer, M., Maussion, F., Gutmann, E., Gohm, A., and Rotach, M. W. (2019), Assessing the added value of the Intermediate Complexity Atmospheric Research (ICAR) model for precipitation in complex topography. Hydrology and Earth System Sciences, 23(6), 2715-2734.

Excessive river erosion and sedimentation threatens critical infrastructure, degrades aquatic habitat, and impairs water quality. Tools for predicting the magnitude of erosion, sedimentation, and channel evolution processes are needed for effective mitigation and management. We present a new numerical model that simulates coupled river bed and bank erosion at the watershed scale. The model uses modified versions of Bagnold’s sediment transport equation to simulate bed erosion and aggradation, as well as a simplified Bank Stability and Toe Erosion Model (BSTEM) to simulate bank erosion processes. The model is mechanistic and intermediate complexity, accounting for the dominant channel evolution processes while limiting data requirements. We apply the model to a generic test case of channel network response following a disturbance and the results match physical understanding of channel evolution. The model was also tested on two field data sets: below Parker Dam on the lower Colorado River and the North Fork Toutle River (NFTR) which responded dramatically to the 1980 eruption of Mount St. Helens. It accurately predicts observed channel incision and bed material coarsening on the Colorado River, as well as observations for the upstream 18 km of the NFTR watershed. The model does not include algorithms for extensive lateral migration and avulsions and therefore did not perform well in the lower NFTR where the channel migrated across a wide valley bottom. REM is parsimonious and useful for simulating network scale channel change in single thread systems responding to disturbance.

The Massachusetts Institute of Technology Integrated Global System Model (IGSM) is designed for analyzing the global environmental changes that may result from anthropogenic causes, quantifying the uncertainties associated with the projected changes, and assessing the costs and environmental effectiveness of proposed policies to mitigate climate risk. The IGSM consists of the Massachusetts Institute of Technology Earth System Model (MESM) of intermediate complexity and the Economic Projections and Policy Analysis model. This paper documents the current version of the MESM, which includes a two-dimensional (zonally averaged) atmospheric model with interactive chemistry coupled to the zonally averaged version of Global Land System model and an anomaly-diffusing ocean model., United States. Department of Energy. Office of Science (DE-FG02-94ER61937)

An intermediate-complexity model for four-dimensional variational data assimilation including moist processes

A pilot coupled climate sensitivity study is presented based on the newly developed adjoint coupled climate model, Centrum für Erdsystemforschung und Nachhaltigkeit (CEN) Earth System Assimilation Model (CESAM). To this end the components of the coupled forward model are summarized, and the generation of the adjoint code out of the model forward code through the application of the Transformation of Algorithms in FORTRAN (TAF) adjoint compiler is discussed. It is shown that simulations of the intermediate-complexity CESAM are comparable in quality to CMIP-type coupled climate models, justifying the usage of the model to compute adjoint sensitivities of the northern Europe near-surface temperature to anomalies in surface temperature, sea surface salinity, and sea ice over the North Atlantic and the Arctic on time scales of up to one month. Results confirm that on a time scale of up to a few days surface temperatures over northern Europe are influenced by Atlantic temperature anomalies just upstream of the target location. With increasingly longer time lapse, however, it is the influence of SSTs over the central and western North Atlantic on the overlying atmosphere and the associated changes in storm-track pattern that dominate the evolution of the surface European temperature. Influences of surface salinity and sea ice on the northern European temperature appear to have similar sensitivity mechanisms, invoked indirectly through their influence on near-surface temperature anomalies. The adjoint study thus confirms that the SST’s impact on the atmospheric dynamics, notably storm tracks, is the primary cause for the influence of northern European temperature changes.

This work evaluated the National Research Council (NRC) dairy model (2001) predictions of rumen undegradable (RUP) and degradable (RDP) protein compared with measured postruminal non-ammonia, nonmicrobial (NANMN) and microbial N flows. Models were evaluated using the root mean squared prediction error (RMSPE) as a percent of the observed mean; mean and slope biases as percentages of mean squared prediction error (MSPE), and concordance correlation coefficient, (CCC). The NRC (2001) over-estimated NANMN by 18% and under-estimated microbial N by 14%. Both responses had large mean biases (19% and 20% of MSPE, respectively); and NANMN had a slope Dias (22% of MSPE). The NRC NANMN estimate had high RMSPE (46% of observed mean) and low CCC (0.37); updating feed library A, B, and C protein fractions and degradation rate (K-d) estimates with newer literature only marginally improved fit. The re-fit NRC models for NANMN and microbial N had CCC of 0.89 and 0.94, respectively. When compared with a prediction of NANMN as a static mean fraction of N intake; the re-derived NRC approach did not have improved fit. A protein system of intermediate complexity was derived in an attempt to estimate NANMN with improved fit compared with the static mean NANMN model. In this system, postruminal appearance of A, B, and C protein fractions were predicted in a feed-type specific mariner rather than from estimated passage and degradation rates. In a comparison to independent data. achieved through cross-validation; the new protein system improved RMSPE (34 vs. 36% of observed mean) and CCC (0.42 vs. 0.30) compared with the static mean NANMN model. When the NRC microbial N equation was re-derived, the RDP term dropped from the model. Consequently, 2 new microbial protein equations were formulated, both used a saturating (increasing at a decreasing rate) form: one saturated with respect to TDN and the other saturated over increasing intakes of rumen degraded starch and NDF. Both equations expressed maximal microbial N production as a linear function of RDP intake. The function relating microbial N to intake of rumen degradable carbohydrate improved RMSPE (24 vs. 28% of the observed mean) and CCC (0.63 vs 0.30) compared with the re-derived NRC model. The newly derived equations showed modest improvements in model fit and improved capacity to account for known biological effects; however, substantial variability in NANMN and microbial N estimates remained unexplained. Agricultural and Food Research Initiative Competitive Grant [2011-68004-30340]; USDA National Institute of Food and Agriculture (Washington, DC) [2015-03656]; Papillon (Easton, MD) In addition to the funding sources listed in the title page footnote, funding for this project was provided by Agricultural and Food Research Initiative Competitive Grant no. 2011-68004-30340 and no. 2015-03656 from the USDA National Institute of Food and Agriculture (Washington, DC) and by Papillon (Easton, MD). The authors acknowledge the contributions of the late L. F. Reutzel (Laud O'Lakes/Purina Mills) to portions of the initial code used in the project and the late Gale Bateman (Akey, Lewisburg, OH) for help in assembling and collating the dietary ingredient data.

We use the Bern3D model of intermediate complexity to examine the marine cycle of isotopes 231Pa and 230Th and the relationship between the particle-bound ratio Pa p / Th p and changes in the formation of the North Atlantic Deep Water (NADW). Model parameters describing reversible scavenging of isotopes by organic particles, opal, calcite and resuspended sediments were systematically varied and alternative sink parametrisations explored. It proves difficult to simultaneously achieve a good agreement with observations of dissolved and particle-associated concentrations of 231Pa and 230Th ( Pa d , Th d , Pa p , Th p ) as well as the particle-bound ratio Pa p / Th p within the classical concept of reversible scavenging alone. Agreement between simulated and observed Pa d , Th d and estimates of mean ocean residence times is improved by taking into account simplified representations of additional sinks at the sea floor (bottom scavenging) and at continental boundaries (boundary scavenging). We also find improved agreement between model and data by increasing lateral advection, in particular for Pa d . These results point to the importance of sink processes that act in addition to reversible scavenging to shape the steady state distribution of 231Pa and, to a lesser degree, of 230Th. In transient experiments in which the strength of the Atlantic meridional overturning circulation (AMOC) is periodically turned on and off, we find a strong statistical relationship between variations in AMOC strength and Pa p / Th p at great depths in the Northwest Atlantic region. These conclusions are robust across the range of sink parametrisations, that are consistent with estimates in the mean ocean residence time of 231Pa and 230Th. Our results indicate that the relationship between Pa p / Th p and AMOC-strength may not be fundamentally affected by uncertainties in sink processes, at least on the large spatial and temporal scale considered here, and support the idea that changes in Pa p / Th p in sediments of the Northwest Atlantic are indicative of changes in AMOC strength. Taking into account our simplified approach, our results indicate that the relationship between Pa p / Th p and AMOC-strength in the deep Northwest Atlantic is not affected by boundary scavenging or bottom scavenging. Our results thus support the idea that changes in Pa p / Th p in sediments of the Northwest Atlantic are indicative of changes in AMOC strength.

Although past decades have witnessed significant reductions in mortality of heart failure together with advances in our understanding of its cellular, molecular, and whole-heart features, a lot of basic cardiac research still fails to translate into clinical practice. In this review we examine myocardial slices, a novel model in the translational arena. Myocardial slices are living ultra-thin sections of heart tissue. Slices maintain the myocardium’s native function (contractility, electrophysiology) and structure (multicellularity, extracellular matrix) and can be prepared from animal and human tissue. The discussion begins with the history and current advances in the model, the different interlaboratory methods of preparation and their potential impact on results. We then contextualize slices’ advantages and limitations by comparing it with other cardiac models. Recently, sophisticated methods have enabled slices to be cultured chronically in vitro while preserving the functional and structural phenotype. This is more timely now than ever where chronic physiologically relevant in vitro platforms for assessment of therapeutic strategies are urgently needed. We interrogate the technological developments that have permitted this, their limitations, and future directions. Finally, we look into the general obstacles faced by the translational field, and how implementation of research systems utilizing slices could help in resolving these., Graphical Abstract Graphical Abstract

Myocardial slices, also known as “cardiac tissue slices” or “organotypic heart slices,” are ultrathin (100–400 μm) slices of living adult ventricular myocardium prepared using a high-precision vibratome. They are a model of intermediate complexity as they retain the native multicellularity, architecture, and physiology of the heart, while their thinness ensures adequate oxygen and metabolic substrate diffusion in vitro. Myocardial slices can be produced from a variety of animal models and human biopsies, thus providing a representative human in vitro platform for translational cardiovascular research. In this review, we compare myocardial slices to other in vitro models and highlight some of the unique advantages provided by this platform. Additionally, we discuss the work performed in our laboratory to optimize myocardial slice preparation methodology, which resulted in highly viable myocardial slices from both large and small mammalian hearts with only 2–3% cardiomyocyte damage and preserved structure and function. Applications of myocardial slices span both basic and translational cardiovascular science. Our laboratory has utilized myocardial slices for the investigation of cardiac multicellularity, visualizing 3D collagen distribution and micro/macrovascular networks using tissue clearing protocols and investigating the effects of novel conductive biomaterials on cardiac physiology. Myocardial slices have been widely used for pharmacological testing. Finally, the current challenges and future directions for the technology are discussed.

The uncertainty in a climate data records (CDRs) derived from Earth observations in part derives from the propagated uncertainty in the radiance record (the fundamental climate data record, FCDR) from which the geophysical estimates in the CDR are derived. A common barrier to providing uncertainty-quantified CDRs is the inaccessibility to CDR creators of appropriate radiance uncertainty information in the FCDR. Here, we propose radiance uncertainty information designed directly to facilitate estimation of propagated uncertainty in derived CDRs at full resolution and in gridded products. Errors in Earth observations are typically highly structured and complex, and the uncertainty information we propose is of intermediate complexity, sufficient to capture the main variability in propagated uncertainty in a CDR, while avoiding unfeasible complexity or data volume. The uncertainty and error correlation characteristics of uncertainty are quantified for three classes of error with different propagation properties: independent, structured and common radiance errors. The meaning, mathematical derivations, practical evaluation and example applications of this set of uncertainty information are presented.

This paper investigates the role played by cooperation for the sustainable harvesting of an ecosystem. To achieve this, a bio-economic model based on a multi-species dynamics with interspecific relationships and multi-agent catches is considered. A comparison between the non-cooperative and cooperative optimal strategies is carried out. Revisiting the Tragedy of Open Access and over-exploitation issues, it is first proved analytically how harvesting pressure is larger in the non-cooperative case for every species. Then it is examined to what extent gains from cooperation can also be derived for the state of the ecosystem. It turns out that cooperation clearly promotes the conservation of every species when the number of agents is high. When the number of agents remains limited, results are more complicated, especially if a species-by-species viewpoint is adopted. However, we identify two metrics involving the state of every species and accounting for their ecological interactions which exhibit gains from cooperation at the ecosystem scale in the general case. Numerical examples illustrate the mathematical findings.

The next generation of chemical toxicity testing will use organs-on-chips (OoCs)—3D cultures of heterotypic cells with appropriate extracellular matrices to better approximate the in vivo cellular microenvironment. Researchers are already working to validate whether OoCs are predictive of toxicity in humans. Here, we review two other key aspects of how OoCs may advance predictive toxicology—each taking advantage of OoCs as systems of intermediate complexity that remain experimentally accessible. First, the intermediate complexity of OoCs will help elucidate the scale(s) of organismal complexity that currently confound computational predictions of in vivo toxicity from in vitro data sets. Identifying the strongest confounding factors will help researchers improve the computational models underlying such predictions. Second, the experimental accessibility of OoCs will allow researchers to analyze chemical-exposure responses in OoCs using an array of high-content readouts—from fluorescent biosensors...

With limited computational resources, there is a need for computationally frugal models. This is particularly the case for atmospheric sciences, which have long relied on either simplistic analytical solutions or computationally expensive numerical models. The simpler solutions are inadequate for many problems, while the cost of numerical models makes their use impossible for many problems, most notably high-resolution climate downscaling applications spanning large areas, long time periods, and many global climate projections. Here the Intermediate Complexity Atmospheric Research model (ICAR) is presented to provide a new step along the modeling complexity continuum. ICAR leverages an analytical solution for high-resolution perturbations to wind velocities, in conjunction with numerical physics schemes, that is, advection and cloud microphysics, to simulate the atmosphere. The focus of the initial development of ICAR is for predictions of precipitation, and eventually temperature, humidity, and radiation at the land surface. Comparisons between ICAR and the Weather Research and Forecasting (WRF) Model for simulations over an idealized mountain are presented, as well as among ICAR, WRF, and the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) observation-based product for a year-long simulation over the Colorado Rockies. In the ideal simulations, ICAR matches WRF precipitation predictions across a range of environmental conditions with a coefficient of determination r2 of 0.92. In the Colorado Rockies, ICAR, WRF, and PRISM show very good agreement, with differences between ICAR and WRF comparable to the differences between WRF and PRISM in the cool season. For these simulations, WRF required 140–800 times more computational resources than ICAR.

The local ensemble transform Kalman filter (LETKF) is used to develop a strongly coupled data assimilation (DA) system for an intermediate complexity ocean-atmosphere coupled model. Strongly coupled DA uses the cross-domain error covariance from a coupled-model background ensemble to allow observations in one domain to directly impact the state of the other domain during the analysis update. This method is compared to weakly coupled DA in which the coupled model is used for the background, but the cross-domain error covariance is not utilized. We perform an observing system simulation experiment with atmospheric observations only. Strongly coupled DA reduces the ocean analysis errors compared to weakly coupled DA, and the higher accuracy of the ocean also improves the atmosphere. The LETKF system design presented should allow for easy implementation of strongly coupled DA with other types of coupled models.