Interne Modellvariabilität von Ensemblesimulationen des gekoppelten Wellen- und Atmosphärenmodells GCOAST

<p>Ziel dieser Studie (Wiese et al., 2020) ist, die Signifikanz des Einflusses des Wellenmodells auf das regionale Atmosphärenmodell und die interne Modellvariabilität sowohl des Atmosphärenmodells, als auch des gekoppelten Systems bestehend aus Wellen- und Atmosphärenmodell zu bestimmen. In einer vorhergehenden Studie wurde gezeigt, dass die Rauigkeit, die im Wellenmodell berechnet wird, größer ist, als die Rauigkeit, die im Atmosphärenmodell approximiert wird, was zu Unterschieden im Atmosphärenmodell führt (Wiese et al. 2019). Hier soll nun untersucht werden, ob diese Unterschiede im Atmosphärenmodell signifikant sind.  Dazu werden Ensemblesimulation mit einem Referenz Setup (das Atmosphärenmodell sendet den Wind an das Wellenmodell) und dem gekoppelten Setup (zusätzlich zum Windaustausch, sendet das Wellenmodell die Rauigkeitslänge über dem Meer zurück an das Atmosphärenmodell) durchgeführt. Bei der Analyse der internen Modellvariabilität zwischen beiden Ensembles zeigt sich, dass die interne Modellvariabilität im gekoppelten Ensemble gegenüber dem Referenzensemble reduziert ist. Dieser Effekt tritt während Extremereignissen am stärksten auf, ist aber auch bei einer generellen Analyse der internen Modellvariabilität über den gesamten Zeitraum sichtbar. Außerdem können die Effekte der Kopplung von der internen Modellvariabilität unterschieden werden, da die Effekte der Kopplung größer sind, als die interne Modellvariabilität. Diese Studie zeigt daher das Potential sowohl in operationellen Systemen als auch Systemen für Klimastudien die Unsicherheit zu reduzieren, wenn das Wellenmodell mit dem Atmosphärenmodell gekoppelt wird. Hinzu kommt, dass die Effekte der Kopplung klar von der internen Modellvariabilität unterschieden werden können, wodurch außerdem eine verbesserte Übereinstimmung des gekoppelten Systems gegenüber dem Referenzensemble mit Beobachtungsdaten erzielt werden kann. In einem nächsten Schritt soll nun zusätzlich der Ozean gekoppelt und die Auswirkungen auf das gesamte System untersucht werden.</p> <p> </p> <p>Literatur:</p> <p>Wiese A, Stanev E, Koch W, Behrens A, Geyer B and Staneva J (2019) The Impact of the Two-Way Coupling between Wind Wave and Atmospheric Models on the Lower Atmosphere over the North Sea. Atmosphere. 10(7):386. doi: 10.3390/atmos10070386</p> <p>Wiese A, Staneva J, Ho-Hagemann HTM, Grayek S, Koch W and Schrum C (2020) Internal Model Variability of Ensemble Simulations With a Regional Coupled Wave-Atmosphere Model GCOAST. Front. Mar. Sci. 7:596843. doi: 10.3389/fmars.2020.596843</p>

Exploring Structural Uncertainty and Impact of Health State Utility Values on Lifetime Outcomes in Diabetes Economic Simulation Models: Findings from the Ninth Mount Hood Diabetes Quality-of-Life Challenge

Background Structural uncertainty can affect model-based economic simulation estimates and study conclusions. Unfortunately, unlike parameter uncertainty, relatively little is known about its magnitude of impact on life-years (LYs) and quality-adjusted life-years (QALYs) in modeling of diabetes. We leveraged the Mount Hood Diabetes Challenge Network, a biennial conference attended by international diabetes modeling groups, to assess structural uncertainty in simulating QALYs in type 2 diabetes simulation models. Methods Eleven type 2 diabetes simulation modeling groups participated in the 9th Mount Hood Diabetes Challenge. Modeling groups simulated 5 diabetes-related intervention profiles using predefined baseline characteristics and a standard utility value set for diabetes-related complications. LYs and QALYs were reported. Simulations were repeated using lower and upper limits of the 95% confidence intervals of utility inputs. Changes in LYs and QALYs from tested interventions were compared across models. Additional analyses were conducted postchallenge to investigate drivers of cross-model differences. Results Substantial cross-model variability in incremental LYs and QALYs was observed, particularly for HbA1c and body mass index (BMI) intervention profiles. For a 0.5%-point permanent HbA1c reduction, LY gains ranged from 0.050 to 0.750. For a 1-unit permanent BMI reduction, incremental QALYs varied from a small decrease in QALYs (−0.024) to an increase of 0.203. Changes in utility values of health states had a much smaller impact (to the hundredth of a decimal place) on incremental QALYs. Microsimulation models were found to generate a mean of 3.41 more LYs than cohort simulation models ( P = 0.049). Conclusions Variations in utility values contribute to a lesser extent than uncertainty captured as structural uncertainty. These findings reinforce the importance of assessing structural uncertainty thoroughly because the choice of model (or models) can influence study results, which can serve as evidence for resource allocation decisions. Highlights The findings indicate substantial cross-model variability in QALY predictions for a standardized set of simulation scenarios and is considerably larger than within model variability to alternative health state utility values (e.g., lower and upper limits of the 95% confidence intervals of utility inputs). There is a need to understand and assess structural uncertainty, as the choice of model to inform resource allocation decisions can matter more than the choice of health state utility values.

Persistent Uncertainties in Ocean Net Primary Production Climate Change Projections at Regional Scales Raise Challenges for Assessing Impacts on Ecosystem Services

Ocean net primary production (NPP) results from CO2 fixation by marine phytoplankton, catalysing the transfer of organic matter and energy to marine ecosystems, supporting most marine food webs, and fisheries production as well as stimulating ocean carbon sequestration. Thus, alterations to ocean NPP in response to climate change, as quantified by Earth system model experiments conducted as part of the 5th and 6th Coupled Model Intercomparison Project (CMIP5 and CMIP6) efforts, are expected to alter key ecosystem services. Despite reductions in inter-model variability since CMIP5, the ocean components of CMIP6 models disagree roughly 2-fold in the magnitude and spatial distribution of NPP in the contemporary era, due to incomplete understanding and insufficient observational constraints. Projections of NPP change in absolute terms show large uncertainty in CMIP6, most notably in the North Atlantic and the Indo-Pacific regions, with the latter explaining over two-thirds of the total inter-model uncertainty. While the Indo-Pacific has previously been identified as a hotspot for climate impacts on biodiversity and fisheries, the increased inter-model variability of NPP projections further exacerbates the uncertainties of climate risks on ocean-dependent human communities. Drivers of uncertainty in NPP changes at regional scales integrate different physical and biogeochemical factors that require more targeted mechanistic assessment in future studies. Globally, inter-model uncertainty in the projected changes in NPP has increased since CMIP5, which amplifies the challenges associated with the management of associated ecosystem services. Notably, this increased regional uncertainty in the projected NPP change in CMIP6 has occurred despite reduced uncertainty in the regional rates of NPP for historical period. Improved constraints on the magnitude of ocean NPP and the mechanistic drivers of its spatial variability would improve confidence in future changes. It is unlikely that the CMIP6 model ensemble samples the complete uncertainty in NPP, with the inclusion of additional mechanistic realism likely to widen projections further in the future, especially at regional scales. This has important consequences for assessing ecosystem impacts. Ultimately, we need an integrated mechanistic framework that considers how NPP and marine ecosystems respond to impacts of not only climate change, but also the additional non-climate drivers.

Clinical evaluation of non-contact infrared thermometers

Non-contact infrared thermometers (NCITs) are being widely used during the COVID-19 pandemic as a temperature-measurement tool for screening and isolating patients in healthcare settings, travelers at ports of entry, and the general public. To understand the accuracy of NCITs, a clinical study was conducted with 1113 adult subjects using six different commercially available NCIT models. A total of 60 NCITs were tested with 10 units for each model. The NCIT-measured temperature was compared with the oral temperature obtained using a reference oral thermometer. The mean difference between the reference thermometer and NCIT measurement (clinical bias) was different for each NCIT model. The clinical bias ranged from just under − 0.9 °C (under-reporting) to just over 0.2 °C (over-reporting). The individual differences ranged from − 3 to + 2 °C in extreme cases, with the majority of the differences between − 2 and + 1 °C. Depending upon the NCIT model, 48% to 88% of the individual temperature measurements were outside the labeled accuracy stated by the manufacturers. The sensitivity of the NCIT models for detecting subject’s temperature above 38 °C ranged from 0 to 0.69. Overall, our results indicate that some NCIT devices may not be consistently accurate enough to determine if subject’s temperature exceeds a specific threshold of 38 °C. Model-to-model variability and individual model accuracy in the displayed temperature were found to be outside of acceptable limits. Accuracy and credibility of the NCITs should be thoroughly evaluated before using them as an effective screening tool.

Modelling migration in birds: competition's role in maintaining individual variation

Animals exhibit extensive intraspecific variation in behaviour. Causes of such variation are less well understood. Here, we ask when competition leads to the maintenance of multiple behavioural strategies. We model variability using the timing of bird migration as an example. Birds often vary in when they return from non-breeding grounds to establish breeding territories. We assume that early-arriving birds (counting permanent residents as ‘earliest’) select the best territories. But arriving before the optimal (frequency-independent) breeding date incurs a fitness penalty. Using simulations, we find stable sets of return dates. When year-round residency is viable, the greatest between-individual variation occurs when a small proportion of permanent residents is favoured, and the rest of the population varies in their return times. However, when fitness losses due to year-round residency exceed the benefits of breeding in the worst territory, all individuals migrate, although their return dates often vary continuously. In that case, individual variation is inversely related to fitness risks and positively related to territory inequality. This result is applicable across many systems: when there is more to gain through competition, or when its risks are small, a diversity of individual strategies prevails. Additionally, stability can depend upon the distribution of resources.

Demistify: an LES and SCM intercomparison of radiation fog

An intercomparison between 10 single-column (SCM) and 5 large-eddy simulation (LES) models is presented for a radiation fog case study inspired by the LANFEX field campaign. 7 of the SCMs represent single-column equivalents of operational numerical weather prediction (NWP) models, whilst 3 are research-grade SCMs designed for fog simulation, and the LES are designed to reproduce in the best manner currently possible the underlying physical processes governing fog formation. The LES model results are of variable quality, and do not provide a consistent baseline against which to compare the NWP models, particularly under high aerosol or cloud droplet number (CDNC) conditions. The main SCM bias appears to be toward over-development of fog, i.e. fog which is too thick, although the inter-model variability is large. In reality there is a subtle balance between water lost to the surface and water condensed into fog, and the ability of a model to accurately simulate this process strongly determines the quality of its forecast. Some NWP-SCMs do not represent fundamental components of this process (e.g. cloud droplet sedimentation) and therefore are naturally hampered in their ability to deliver accurate simulations. Finally, we show that modelled fog development is as sensitive to the shape of the cloud droplet size distribution, a rarely studied or modified part of the microphysical parametrization, as it is to the underlying aerosol or CDNC.

The Study of Growth and Performance in Local Chicken Breeds and Varieties: A Review of Methods and Scientific Transference

A review of the scientific advances in the study of the growth and performance in native chicken breeds and varieties over the past 20 years was performed. Understanding the growth patterns of native breeds can only be achieved if the constraints characterizing these populations are considered and treated accordingly. Contextually, the determination of researchers to use the same research methods and study designs applied in international commercial poultry populations conditions the accuracy of the model, variability capturing ability, and the observational or predictive performance when the data of the local population are fitted. Highly skewed sex ratios favouring females, an inappropriate census imbalance compensation and a lack of population structure render models that are regularly deemed effective as invalid to issue solid and sound conclusions. The wider the breed diversity is in a country, the higher the scientific attention paid to these populations. A detailed discussion of the most appropriate models and underlying reasons for their suitability and the reasons preventing the use of others in these populations is provided. Furthermore, the factors conditioning the scientific reception and impact of related publications used to transfer these results to the broad scientific public were evaluated to serve as guidance for the maximization of the success and dissemination of local breed information.

Clinical Evaluation of Non-Contact Infrared Thermometers

Background:Non-contact infrared thermometers (NCITs) are being widely used during the COVID-19 pandemic as a temperature-measurement tool for screening and isolating patients in healthcare settings, travelers at ports of entry, and the general public. Methods:To understand the accuracy of NCITs, a clinical study was conducted with 1113 adult subjects using six different commercially available NCIT models. A total of 60 NCITs were tested with 10 units for each model. The NCIT-measured temperature was compared with the oral temperature obtained using a reference oral thermometer. Results:The mean difference between the reference thermometer and NCIT measurement (clinical bias) was different for each NCIT model. The clinical bias ranged from just under -0.9 °C (under-reporting) to just over 0.2 °C (over-reporting). The individual differences ranged from -3 °C to +2 °C in extreme cases, with the majority of the differences between -2 °C and +1 °C. Depending upon the NCIT model, 48% to 88% of the individual temperature measurements were outside the labeled accuracy stated by the manufacturers. The sensitivity of the NCIT models for detecting subject’s temperature above 38 °C ranged from 0 to 0.69. Conclusions: Overall, our results indicate that some NCIT devices may not be consistently accurate enough to determine if subject’s temperature exceeds a specific threshold of 38 °C. Model-to-model variability and individual model accuracy in the displayed temperature were found to be outside of acceptable limits. Accuracy and credibility of the NCITs should be thoroughly evaluated before using them as an effective screening tool.

Large Differences in Diffuse Solar Radiation Among Current-Generation Reanalysis and Satellite-Derived Products

Though the partitioning of shortwave radiation (K↓) at the surface into its diffuse (K↓,d) and direct beam (K↓,b) components is relevant for, among other things, the terrestrial energy and carbon budgets, there is a dearth of large-scale comparisons of this partitioning across reanalysis and satellite-derived products. Here we evaluate K↓, K↓,d, and K↓,b, as well as the diffuse fraction (kd) of solar radiation in four current-generation reanalysis (NOAA-CIRES-DOE, NCEP/NCAR, MERRA-2, ERA5) datasets and one satellite-derived product (CERES) using ≈1400 site years of observations. Although the systematic positive biases in K↓ is consistent with previous studies, the biases in gridded K↓,d and K↓,b vary in direction and magnitude, both annually and across seasons. The inter-model variability in cloud cover strongly explains the biases in both K↓,d and K↓,b. Over Europe and China, the long-term (10-year plus) trends in K↓,d in the gridded products are noticeably differ from corresponding observations and the grid-averaged 35-year trends show an order of magnitude variability. In the MERRA-2 reanalysis, which includes both clouds and assimilated aerosols, the reduction in both clouds and aerosols reinforce each other to establish brightening trends over Europe, while the effect of increasing aerosols overwhelm the effect of decreasing cloud cover over China. The inter-model variability in kd seen here (0.27 to 0.50 from CERES to MERRA-2) suggests substantial differences in shortwave parameterization schemes and their inputs in climate models and can contribute to inter-model variability in coupled simulations. Based on these results, we call for systematic evaluations of K↓,d and K↓,b in CMIP6 models.