CMIP6 simulations with the compact Earth system model OSCAR v3.1

While Earth system models (ESMs) are process-based and can be run at high resolutions, they are only limited by computational costs. Reduced complexity models, also called simple climate models or compact models, provide a much cheaper alternative, although at a loss of spatial information. Their structure relies on the sciences of the Earth system, but with a calibration against the most complex models. Therefore it remains important to evaluate and validate reduced complexity models. Here, we diagnose such a model the newest version of OSCAR (v3.1) using observations and results from ESMs from the current Coupled Model Intercomparison Project 6. A total of 99 experiments are selected for simulation with OSCAR v3.1 in a probabilistic framework, reaching a total of 567,700,000 simulated years. A first highlight of this exercise that the ocean carbon cycle of the model may diverge under some parametrizations and for high-warming scenarios. The diverging runs caused by this unstability were discarded in the post-processing. Then, each physical parametrization is weighted based on its performance against a set of observations, providing us with constrained results. Overall, OSCAR v3.1 shows good agreement with observations, ESMs and emerging properties. It qualitively reproduces the responses of complex ESMs, for all aspects of the Earth system. We observe some quantitative differences with these models, most of them being due to the observational constraints. Some specific features of OSCAR also contribute to these differences, such as its fully interactive atmospheric chemistry and endogenous calculations of biomass burning, wetlands CH4 and permafrost CH4 and CO2 emissions. The main points of improvements are a low sensitivity of the land carbon cycle to climate change, an unstability of the ocean carbon cycle, the seemingly too simple climate module, and the too strong climate feedback involving short-lived species. Beyond providing a key diagnosis of the OSCAR model in the context of the reduced-complexity models intercomparison project (RCMIP), this work is also meant to help with the upcoming calibration of OSCAR on CMIP6 results, and to provide a large group of CMIP6 simulations run consistently within a probabilistic framework.

Forward Modelling for Structural Stratigraphic Analysis, Offshore Sureste Basin, Mexico

The stratal architecture of deep-water minibasins is dominantly controlled by the interplay of two factors, structure growth and sediment supply. In this paper we explore the utility of a reduced-complexity, fast computational method (Onlapse-2D) to simulate stratal geometry, using a process of iteration to match the model output to available subsurface control (well logs and 3D seismic data). This approach was used to model the Miocene sediments in two intersecting lines of section in a complex mini-basin in the deep-water Campeche Basin, offshore Mexico. A good first-pass match between model output and geological observations was obtained, allowing us to identify and separate the effects of two distinct phases of compressional folding and a longer-lasting episode of salt withdrawal/diapirism, and to determine the timing of these events. This modelling provides an indication of the relative contribution of background sedimentation (pelagic and hemipelagic) vs. sediment-gravity-flow deposition (e.g. turbidites) within each layer of the model. The inferred timing of the compressional events derived from the model is consistent with other geological observations within the basin. The process of iteration towards a best-fit model leaves significant but local residual mismatches at several levels in the stratigraphy; these correspond to surfaces with anomalous negative (erosional) or positive (constructive depositional) palaeotopography. We label these mismatch surfaces “informative discrepancies” because the magnitude of the mismatch allows us to estimate the geometry and magnitude of the local seafloor topography. Reduced-complexity simulation is shown to be a useful and effective approach, which, when combined with an existing seismic interpretation, provides insight into the geometry and timing of controlling processes, indicates the nature of the sediments (background vs. sediment-gravity-flow) and aids in the identification of key erosional or constructional surfaces within the stratigraphy.

Multiscale Entropy of Resting-State Functional Magnetic Resonance Imaging Differentiates Progressive Supranuclear Palsy and Multiple System Atrophy

Distinguishing progressive supranuclear palsy (PSP) from multiple system atrophy (MSA) in the early clinical stages is challenging; few sensitive and specific biomarkers are available for their differential diagnosis. Resting-state functional magnetic resonance imaging (rs-fMRI) is used to study the fluctuations in blood oxygen level-dependent (BOLD) signals at rest, which provides evidence for aberrant brain functional networks in neurodegenerative diseases. We aimed to examine whether rs-fMRI data could differentiate between PSP and MSA via a multiscale entropy (MSE) analysis of BOLD signals, which estimates the complexity of temporal fluctuations in brain activity. We recruited 14 and 18 patients with PSP and MSA, respectively, who underwent neuropsychological tests and rs-fMRI. PSP patients demonstrated greater cognitive function impairments, particularly in the frontal executive function. The bilateral prefrontal cortex revealed lower entropy BOLD signal values in multiple time scales for PSP, compared to the values observed in MSA patients; however, the functional connectivity of the representative brain networks was comparable between the diseases. The reduced complexity of BOLD signals in the prefrontal cortex was associated with frontal dysfunction. Thus, an MSE analysis of rs-fMRI could differentiate between PSP and MSA, and the reduced complexity of BOLD signals could be associated with cognitive impairment.

Reduced-complexity air quality intervention modeling over China: the development of InMAPv1.6.1-China and a comparison with CMAQv5.2

This paper presents the first development and evaluation of a reduced-complexity air quality model for China. In this study, the reduced-complexity Intervention Model for Air Pollution over China (InMAP-China) is developed by linking a regional air quality model, a reduced-complexity air quality model, an emission inventory database for China, and a health impact assessment model to rapidly estimate the air quality and health impacts of emission sources in China. The modeling system is applied over mainland China for 2017 under various emission scenarios. A comprehensive model evaluation is conducted by comparison against conventional Community Multiscale Air Quality (CMAQ) modeling system simulations and ground-based observations. We found that InMAP-China satisfactorily predicted total PM2.5 concentrations in terms of statistical performance. Compared with the observed PM2.5 concentrations, the mean bias (MB), normalized mean bias (NMB) and correlations of the total PM2.5 concentrations are −8.1 µg m−3, −18 % and 0.6, respectively. The statistical performance is considered to be satisfactory for a reduced-complexity air quality model and remains consistent with that evaluated in the USA. The underestimation of total PM2.5 concentrations was mainly caused by its composition, primary PM2.5. In terms of the ability to quantify source contributions of PM2.5 concentrations, InMAP-China presents similar results to those based on the CMAQ model, with variation mainly caused by the different treatment of secondary inorganic aerosols in the two models. Focusing on the health impacts, the annual PM2.5-related premature mortality estimated using InMAP-China in 2017 was 1.92 million, which was 250 000 deaths lower than estimated based on CMAQ simulations as a result of the underestimation of PM2.5 concentrations. This work presents a version of the reduced-complexity air quality model over China that provides a powerful tool to rapidly assess the air quality and health impacts associated with control policy and to quantify the source contribution attributable to many emission sources.

Photonic RF and Microwave Photonic Channelizers based on Kerr Micro-combs

We review recent work on broadband RF channelizers based on integrated optical frequency Kerr micro-combs combined with passive micro-ring resonator filters, with microcombs having channel spacings of 200GHz and 49GHz. This approach to realizing RF channelizers offers reduced complexity, size, and potential cost for a wide range of applications to microwave signal detection.

sandbox – Creating and Analysing Synthetic Sediment Sections with R

The majority of palaeoenvironmental information is inferred from proxy data contained in accretionary sediments, called geo-archives. The validity of proxy data and analysis workflows are usually assumed implicitly, with systematic tests and uncertainty estimates restricted to modern analogue studies or reduced-complexity case studies. However, a more generic and consistent approach to exploring the validity and variability of proxy functions would be to translate a given geo-archive into a model scenario: a "virtual twin". Here, we introduce a conceptual framework and numerical toolset that allows the definition and analysis of synthetic sediment sections. The R package sandbox describes arbitrary stratigraphically consistent deposits by depth-dependent rules and grain-specific parameters, allowing full scalability and flexibility. Virtual samples can be taken, resulting in discrete grain-mixtures with well-defined parameters. These samples can then be virtually prepared and analysed, for example to test hypotheses. We illustrate the concept of sandbox, explain how a sediment section can be mapped into the model and, by focusing on an exemplary field of application, we explore universal geochronological research questions related to the effects of sample geometry and grain-size specific age inheritance. We summarise further application scenarios of the model framework, relevant for but not restricted to the broader geochronological community.

GBS and a newly developed mRNA-GBS approach to link population genetic and transcriptome analyses reveal pattern differences between sites and treatments in red clover (Trifolium pratense L.)

The important worldwide forage crop red clover (Trifolium pratense L.) is widely cultivated as cattle feed and for soil improvement. Wild populations and landraces have great natural diversity that could be used to improve cultivated red clover. However, to date, there is still insufficient knowledge about the natural genetic and phenotypic diversity of the species. Here, we developed a low-cost transcriptome analysis (mRNA-GBS) with reduced complexity and compared the results with population genetic (GBS) and previously published mRNA-Seq data, to assess whether analysis of intraspecific variation within and between populations and transcriptome responses is possible simultaneously. The mRNA-GBS approach was successful. SNP analyses from the mRNA-GBS approach revealed comparable patterns to the GBS results, but it was not possible to link transcriptome analyses with reduced complexity and sequencing depth to previously published greenhouse and field expression studies. The use of short sequences upstream of the poly(A) tail of mRNA to reduce complexity are promising approaches that combine population genetics and expression profiling to analyze many individuals with trait differences simultaneously and cost-effectively, even in non-model species. Our mRNA-GBS approach revealed too many additional short mRNA sequences, hampering sequence alignment depth and SNP recovery. Optimizations are being discussed. Nevertheless, our study design across different regions in Germany was also challenging as the use of differential expression analyses with reduced complexity, in which mRNA is fragmented at specific sites rather than randomly, is most likely counteracted under natural conditions by highly complex plant reactions at low sequencing depth.