The European intercomparison of in-vivo monitoring laboratories: the EIVIC-2020 project.

The EIVIC project was launched in 2020, and the main goal was the organisation of a European intercomparison of in-vivo monitoring laboratories dealing with direct measurements of gamma-emitting radionuclides incorporated into the body of exposed workers. This project was organised jointly by members of EURADOS Working Group 7 on internal dosimetry (WG7), the Federal Office for Radiation Protection (BfS, Germany) and the Radioprotection and Nuclear Safety Institute (IRSN, France). The objective was to assess the implementation of individual-monitoring requirements in EU Member States on the basis of in-vivo measurements and to gain insight into the performance of in-vivo measurements using whole-body counters. In this context, a total of 41 in-vivo monitoring laboratories from 21 countries, together with JRC (EC) and IAEA participated. The results were submitted in terms of activity (Bq) of the radionuclides identified inside phantoms that were circulated to all participants. The measured data were compared with reference activity values to evaluate the corresponding bias according to the standards ISO 28218 and ISO 13528. In general, the results of the different exercises are good, and most facilities are in conformity with the criteria for the bias and z-scores in the ISO standards. Furthermore, information about technical and organisational characteristics of the participating laboratories was collected to test if they had a significant influence on the reported results.

Investigation of the Performance of Various Low-Cost Radon Monitors under Variable Environmental Conditions.

A comparison of low-cost radon monitors was conducted at the Laboratory of Natural Radiation (LNR). The monitors we evaluated were EcoQube, RadonEye, RadonEye Plus2, Spirit, ViewPlus, ViewRadon and WavePlus. An AlphaGUARD monitor calibrated at the Laboratory of Environmental Radioactivity of the University of Cantabria (LaRUC), accredited for testing and calibration according to ISO/IEC 17025, provided the reference value of radon concentration. The temporal stability of the monitors was studied, obtaining a percentage of missing records ranged from 1% to 19% of the data. The main technical characteristics studied were temporal stability, measurement ranges, accuracy, correlation and response time. The main results show that the measurement ranges align with those specified by their manufacturers, with percentage differences with respect to the reference monitor of between 5% and 16%. The diversity found for response time is remarkable, with values ranging from 1 to 15 h, with Pearson correlation factors between 0.63 and 0.90.

Long-term comparison and performance study of consumer grade electronic radon integrating monitors.

This study reports the performance of 7 types of consumer grade passive Electronic Radon Integrating Monitors, ERIM (AlphaE, AER Plus, Canary, Corentium Pro, Radon Scout Home, Ramon and Wave) and passive etched track radon detectors. All monitors and passive radon detectors were exposed side by side for 2 periods of 3 months under controlled conditions in the UKHSA radon chamber and in a stainless steel container to an average radon concentration of 4781 Bq m-3and 166 Bq m-3, respectively. The performance of each individual monitor was compared with Atmos 12DPX and AlphaGUARD P30 reference instruments. The performance of the monitors was evaluated by estimating the biased, precision and measurement errors of each type. It was found that UKHSA passive radon detectors showed excellent performance (measurement error < 10%) at both higher and lower exposures. The AlphaE, Canary and Ramon showed excellent performance, with measurement error <10%, when they were exposed to radon concentrations between 4000 Bq m-3and 6000 Bq m-3in the UKHSA radon chamber. However, when the monitors were exposed to radon levels below the UK radon Action Level of 200 Bq m-3, the only ERIM which had a measurement error <10% was the Radon Scout Home. All other monitors showed a significant decrease in their performance with measurement errors ranging between 20% and 50%. The calibration factor, which is the ratio between the measured value (background is subtracted) and the reference value, was also studied. It was found that the calibration factors of individual monitors changed significantly. Calibration measurements in 2019 and in 2023 found that the percentage change varied between -46% and +63%. This shows the importance of initial and regular calibration, and maintenance of the monitors.

Regional Sources and Sinks of Atmospheric Particulate Selenium in the United States Based on Seasonality Profiles.

Selenium (Se) is an essential nutrient for humans and enters our food chain through bioavailable Se in soil. Atmospheric deposition is a major source of Se to soils, driving the need to investigate the sources and sinks of atmospheric Se. Here, we used Se concentrations from PM2.5 data at 82 sites from 1988 to 2010 from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network in the US to identify the sources and sinks of particulate Se. We identified 6 distinct seasonal profiles of atmospheric Se, grouped by geographical location: West, Southwest, Midwest, Southeast, Northeast, and North Northeast. Across most of the regions, coal combustion is the largest Se source, with a terrestrial source dominating in the West. We also found evidence for gas-to-particle partitioning in the wintertime in the Northeast. Wet deposition is an important sink of particulate Se, as determined by Se/PM2.5 ratios. The Se concentrations from the IMPROVE network compare well to modeled output from a global chemistry-climate model, SOCOL-AER, except in the Southeast US. Our analysis constrains the sources and sinks of atmospheric Se, thereby improving the predictions of Se distribution under climate change.

A regional nuclear conflict would compromise global food security.

A limited nuclear war between India and Pakistan could ignite fires large enough to emit more than 5 Tg of soot into the stratosphere. Climate model simulations have shown severe resulting climate perturbations with declines in global mean temperature by 1.8 °C and precipitation by 8%, for at least 5 y. Here we evaluate impacts for the global food system. Six harmonized state-of-the-art crop models show that global caloric production from maize, wheat, rice, and soybean falls by 13 (±1)%, 11 (±8)%, 3 (±5)%, and 17 (±2)% over 5 y. Total single-year losses of 12 (±4)% quadruple the largest observed historical anomaly and exceed impacts caused by historic droughts and volcanic eruptions. Colder temperatures drive losses more than changes in precipitation and solar radiation, leading to strongest impacts in temperate regions poleward of 30°N, including the United States, Europe, and China for 10 to 15 y. Integrated food trade network analyses show that domestic reserves and global trade can largely buffer the production anomaly in the first year. Persistent multiyear losses, however, would constrain domestic food availability and propagate to the Global South, especially to food-insecure countries. By year 5, maize and wheat availability would decrease by 13% globally and by more than 20% in 71 countries with a cumulative population of 1.3 billion people. In view of increasing instability in South Asia, this study shows that a regional conflict using <1% of the worldwide nuclear arsenal could have adverse consequences for global food security unmatched in modern history.

How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies.

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler's experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in "trial-and-error" calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler's assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.

Intercomparison of equivalent black carbon (eBC) and elemental carbon (EC) concentrations with three-year continuous measurement in Beijing, China.

Due to the lack of black carbon (BC) measurement data in some cases, elemental carbon (EC) is often used as a surrogate of BC, with a simple assumption that they are interchangeable. Such assumption will inevitably lead to uncertainties in radiative forcing estimation and health impact assessment. In order to quantitatively and systematically evaluate the relationship between BC and EC as well as factors responsible for their difference, 3-year collocated equivalent BC (eBC) and EC measurements with 1-h resolution were performed in Beijing, China continuously from 2016 to 2019. EBC concentration was measured by the multi-wavelength aethalometer (AE-33) based on optical analysis, while EC concentration was determined by semi-continuous OC/EC analyzer with thermal-optical method. The results showed that around 90% of eBC concentration was higher than that of EC, with average difference between eBC and EC as 1.21 µg m-3 (accounting for 33% of average eBC in Beijing). EBC and EC concentrations exhibited strong correlation (r = 0.90) during the whole study period, but the slopes (or eBC/EC ratio) and correlation coefficients varied across seasons (spring: 1.67 and 0.94; summer: 0.91 and 0.65; fall: 1.15 and 0.88; winter: 1.09 and 0.91, respectively). Based on the information from shell/core ratios by Single Particle Soot Photometer (SP2), source apportionment results by positive matrix factorization model, and chemical composition of PM2.5, the differences between eBC and EC concentrations were found to be primarily related to BC aging process and secondary components as evidenced by strong positive correlation with secondary species (e.g., secondary organic carbon and nitrate). This study provided seasonal specific conversion factors of eBC and EC in Beijing and helpful reference for other areas, which will contribute new knowledge of carbonaceous aerosol and reduce uncertainty in assessing future climate change and health studies of BC.

How the future of the global forest sink depends on timber demand, forest management, and carbon policies.

Deforestation has contributed significantly to net greenhouse gas emissions, but slowing deforestation, regrowing forests and other ecosystem processes have made forests a net sink. Deforestation will still influence future carbon fluxes, but the role of forest growth through aging, management, and other silvicultural inputs on future carbon fluxes are critically important but not always recognized by bookkeeping and integrated assessment models. When projecting the future, it is vital to capture how management processes affect carbon storage in ecosystems and wood products. This study uses multiple global forest sector models to project forest carbon impacts across 81 shared socioeconomic (SSP) and climate mitigation pathway scenarios. We illustrate the importance of modeling management decisions in existing forests in response to changing demands for land resources, wood products and carbon. Although the models vary in key attributes, there is general agreement across a majority of scenarios that the global forest sector could remain a carbon sink in the future, sequestering 1.2-5.8 GtCO2e/yr over the next century. Carbon fluxes in the baseline scenarios that exclude climate mitigation policy ranged from -0.8 to 4.9 GtCO2e/yr, highlighting the strong influence of SSPs on forest sector model estimates. Improved forest management can jointly increase carbon stocks and harvests without expanding forest area, suggesting that carbon fluxes from managed forests systems deserve more careful consideration by the climate policy community.

Global ensemble projections reveal trophic amplification of ocean biomass declines with climate change.

While the physical dimensions of climate change are now routinely assessed through multimodel intercomparisons, projected impacts on the global ocean ecosystem generally rely on individual models with a specific set of assumptions. To address these single-model limitations, we present standardized ensemble projections from six global marine ecosystem models forced with two Earth system models and four emission scenarios with and without fishing. We derive average biomass trends and associated uncertainties across the marine food web. Without fishing, mean global animal biomass decreased by 5% (±4% SD) under low emissions and 17% (±11% SD) under high emissions by 2100, with an average 5% decline for every 1 °C of warming. Projected biomass declines were primarily driven by increasing temperature and decreasing primary production, and were more pronounced at higher trophic levels, a process known as trophic amplification. Fishing did not substantially alter the effects of climate change. Considerable regional variation featured strong biomass increases at high latitudes and decreases at middle to low latitudes, with good model agreement on the direction of change but variable magnitude. Uncertainties due to variations in marine ecosystem and Earth system models were similar. Ensemble projections performed well compared with empirical data, emphasizing the benefits of multimodel inference to project future outcomes. Our results indicate that global ocean animal biomass consistently declines with climate change, and that these impacts are amplified at higher trophic levels. Next steps for model development include dynamic scenarios of fishing, cumulative human impacts, and the effects of management measures on future ocean biomass trends.

An intercomparison of ozone taken from the Copernicus atmosphere monitoring service and the second Modern-Era retrospective analysis for research and applications over China during 2018 and 2019.

Spatiotemporal variations of ozone (O3) taken from the Copernicus Atmosphere Monitoring Service (CAMS) and the second Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) were intercompared and evaluated with ground and ozone-sonde observations over China in 2018 and 2019. Intercomparison of the surface ozone from CAMS and MERRA-2 reanalysis showed significant negative bias (CAMS minus MERRA-2, same below) at Tibetan Plateau of up to 80 µg/m3, and the average R2 was about 0.6 across China. Evaluated with the ground observations from China National Environmental Monitoring Center (CNEMC), we found that CAMS and MERRA-2 reanalysis were capable of capturing the key patterns of monthly and diurnal variations of surface ozone over China except for the western region, and MERRA-2 overestimated the observations compared to CAMS. Vertically, the CAMS profiles overestimated the ozone-sonde from the World Ozone and Ultraviolet Radiation Data Center (WOUDC) above 200 hPa with the magnitude reaching up to 150 µg/m3, while little bias was found between the reanalysis and observations below 200 hPa. Intercomparison drawn from the vertical distribution between CAMS and MERRA-2 reanalysis showed that the negative bias appeared throughout the troposphere over China, while the positive bias emerged in the upper troposphere and lower stratosphere (UTLS) with high order of magnitude exceeding 100 µg/m3, indicating large uncertainties at higher altitudes. In summary, we concluded that CAMS reanalysis showed better agreement with the observations in contrast to MERRA-2, and the large discrepancy especially at higher altitudes between these two reanalysis datasets could not be ignored.

The function-dominance correlation drives the direction and strength of biodiversity-ecosystem functioning relationships.

Community composition is a primary determinant of how biodiversity change influences ecosystem functioning and, therefore, the relationship between biodiversity and ecosystem functioning (BEF). We examine the consequences of community composition across six structurally realistic plant community models. We find that a positive correlation between species' functioning in monoculture versus their dominance in mixture with regard to a specific function (the "function-dominance correlation") generates a positive relationship between realised diversity and ecosystem functioning across species richness treatments. However, because realised diversity declines when few species dominate, a positive function-dominance correlation generates a negative relationship between realised diversity and ecosystem functioning within species richness treatments. Removing seed inflow strengthens the link between the function-dominance correlation and BEF relationships across species richness treatments but weakens it within them. These results suggest that changes in species' identities in a local species pool may more strongly affect ecosystem functioning than changes in species richness.

Atmospheric methane removal: a research agenda.

Atmospheric methane removal (e.g. in situ methane oxidation to carbon dioxide) may be needed to offset continued methane release and limit the global warming contribution of this potent greenhouse gas. Because mitigating most anthropogenic emissions of methane is uncertain this century, and sudden methane releases from the Arctic or elsewhere cannot be excluded, technologies for methane removal or oxidation may be required. Carbon dioxide removal has an increasingly well-established research agenda and technological foundation. No similar framework exists for methane removal. We believe that a research agenda for negative methane emissions-'removal' or atmospheric methane oxidation-is needed. We outline some considerations for such an agenda here, including a proposed Methane Removal Model Intercomparison Project (MR-MIP). This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 1)'.

Modelling climate change impacts on maize yields under low nitrogen input conditions in sub-Saharan Africa.

Smallholder farmers in sub-Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low-input systems is currently lacking. We evaluated the impact of varying [CO2 ], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi-arid Rwanda, hot subhumid Ghana and hot semi-arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in-season soil water content from 2-year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2 ], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2 ]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low-input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.

Early vigour in wheat: Could it lead to more severe terminal drought stress under elevated atmospheric [CO2 ] and semi-arid conditions?

Early vigour in wheat is a trait that has received attention for its benefits reducing evaporation from the soil surface early in the season. However, with the growth enhancement common to crops grown under elevated atmospheric CO2 concentrations (e[CO2 ]), there is a risk that too much early growth might deplete soil water and lead to more severe terminal drought stress in environments where production relies on stored soil water content. If this is the case, the incorporation of such a trait in wheat breeding programmes might have unintended negative consequences in the future, especially in dry years. We used selected data from cultivars with proven expression of high and low early vigour from the Australian Grains Free Air CO2 Enrichment (AGFACE) facility, and complemented this analysis with simulation results from two crop growth models which differ in the modelling of leaf area development and crop water use. Grain yield responses to e[CO2 ] were lower in the high early vigour group compared to the low early vigour group, and although these differences were not significant, they were corroborated by simulation model results. However, the simulated lower response with high early vigour lines was not caused by an earlier or greater depletion of soil water under e[CO2 ] and the mechanisms responsible appear to be related to an earlier saturation of the radiation intercepted. Whether this is the case in the field needs to be further investigated. In addition, there was some evidence that the timing of the drought stress during crop growth influenced the effect of e[CO2 ] regardless of the early vigour trait. There is a need for FACE investigations of the value of traits for drought adaptation to be conducted under more severe drought conditions and variable timing of drought stress, a risky but necessary endeavour.

Time of Emergence and Large Ensemble Intercomparison for Ocean Biogeochemical Trends.

Anthropogenically forced changes in ocean biogeochemistry are underway and critical for the ocean carbon sink and marine habitat. Detecting such changes in ocean biogeochemistry will require quantification of the magnitude of the change (anthropogenic signal) and the natural variability inherent to the climate system (noise). Here we use Large Ensemble (LE) experiments from four Earth system models (ESMs) with multiple emissions scenarios to estimate Time of Emergence (ToE) and partition projection uncertainty for anthropogenic signals in five biogeochemically important upper-ocean variables. We find ToEs are robust across ESMs for sea surface temperature and the invasion of anthropogenic carbon; emergence time scales are 20-30 yr. For the biological carbon pump, and sea surface chlorophyll and salinity, emergence time scales are longer (50+ yr), less robust across the ESMs, and more sensitive to the forcing scenario considered. We find internal variability uncertainty, and model differences in the internal variability uncertainty, can be consequential sources of uncertainty for projecting regional changes in ocean biogeochemistry over the coming decades. In combining structural, scenario, and internal variability uncertainty, this study represents the most comprehensive characterization of biogeochemical emergence time scales and uncertainty to date. Our findings delineate critical spatial and duration requirements for marine observing systems to robustly detect anthropogenic change.

Comparison of pyrolysis gas chromatography/mass spectrometry and hyperspectral FTIR imaging spectroscopy for the analysis of microplastics.

Analysis of microplastics (MP) in environmental samples is an emerging field, which is performed with various methods and instruments based either on spectroscopy or thermoanalytical methods. In general, both approaches result in two different types of data sets that are either mass or particle number related. Depending on detection limits of the respective method and instrumentation the derived polymer composition trends may vary. In this study, we compare the results of hyperspectral Fourier-transform infrared (FTIR) imaging analysis and pyrolysis gas chromatography-mass spectrometry (Py-GC/MS) analysis performed on a set of environmental samples that differ in complexity and degree of microplastic contamination. The measurements were conducted consecutively, and on exactly the same sample. First, the samples were investigated with FTIR using aluminum oxide filters; subsequently, these were crushed, transferred to glass fiber filters, in pyrolysis cups, and measured via Py-GC/MS. After a general data harmonization step, the trends in MP contamination were thoroughly investigated with regard to the respective sample set and the derived polymer compositions. While the overall trends in MP contamination were very similar, differences were observed in the polymer compositions. Furthermore, polymer masses were empirically calculated from FTIR data and compared with the Py-GC/MS results. Here, a most plausible shape-related overestimation of the calculated polymer masses was observed in samples with larger particles and increased particle numbers. Taking into account the different measurement principles of both methods, all results were examined and discussed, and future needs for harmonization of intermethodological results were identified and highlighted. Graphical abstract.

Estimation of Land Surface Temperature in an Agricultural Region of Bangladesh from Landsat 8: Intercomparison of Four Algorithms.

The presence of two thermal bands in Landsat 8 brings the opportunity to use either one or both of these bands to retrieve Land Surface Temperature (LST). In order to compare the performances of existing algorithms, we used four methods to retrieve LST from Landsat 8 and made an intercomparison among them. Apart from the direct use of the Radiative Transfer Equation (RTE), Single-Channel Algorithm and two Split-Window Algorithms were used taking an agricultural region in Bangladesh as the study area. The LSTs retrieved in the four methods were validated in two ways: first, an indirect validation against reference LST, which was obtained in the Atmospheric and Topographic CORection (ATCOR) software module; second, cross-validation with Terra MODerate Resolution Imaging Spectroradiometer (MODIS) daily LSTs that were obtained from the Application for Extracting and Exploring Analysis Ready Samples (A ρ ρ EEARS) online tool. Due to the absence of LST-monitoring radiosounding instruments surrounding the study area, in situ LSTs were not available; hence, validation of satellite retrieved LSTs against in situ LSTs was not performed. The atmospheric parameters necessary for the RTE-based method, as well as for other methods, were calculated from the National Centers for Environmental Prediction (NCEP) database using an online atmospheric correction calculator with MODerate resolution atmospheric TRANsmission (MODTRAN) codes. Root-mean-squared-error (RMSE) against reference LST, as well as mean bias error against both reference and MODIS daily LSTs, was used to interpret the relative accuracy of LST results. All four methods were found to result in acceptable LST products, leaving atmospheric water vapor content (w) as the important determinant for the precision result. Considering a set of several Landsat 8 images of different dates, Jiménez-Muñoz et al.'s (2014) Split-Window algorithm was found to result in the lowest mean RMSE of 1 . 19 ∘ C . Du et al.'s (2015) Split-Window algorithm resulted in mean RMSE of 1 . 50 ∘ C . The RTE-based direct method and the Single-Channel algorithm provided the mean RMSE of 2 . 47 ∘ C and 4 . 11 ∘ C , respectively. For Du et al.'s algorithm, the w range of 0 . 0 to 6 . 3 g / c m 2 was considered, whereas for the other three methods, w values as retrieved from the NCEP database were considered for corresponding images. Land surface emissivity was retrieved through the Normalized Difference Vegetation Index (NDVI)-threshold method. This intercomparison study provides an LST retrieval methodology for Landsat 8 that involves four algorithms. It proves that (i) better LST results can be obtained using both thermal bands of Landsat 8; (ii) the NCEP database can be used to determine atmospheric parameters using the online calculator; (iii) MODIS daily LSTs from A ρ ρ EEARS can be used efficiently in cross-validation and intercomparison of Landsat 8 LST algorithms; and (iv) when in situ LST data are not available, the ATCOR-derived LSTs can be used for indirect verification and intercomparison of Landsat 8 LST algorithms.

A network-based toolkit for evaluation and intercomparison of weather prediction and climate modeling.

Model evaluation is a critical component in the development and applications of environmental modeling systems. Conventional metrics such as Pearson product-moment correlation coefficient (r), root-mean-square error (RMSE), and mean absolute error (MAE), albeit process-based and limited to point-to-point statistical comparison, have been widely used in model evaluations. In this study, we propose a network-based toolkit for evaluation of model performance and multi-model comparisons with applications to weather prediction and climate modeling. The model outputs are topologically quantified through a range of network metrics to provide a holistic measure of system dynamics. We first use this toolkit to evaluate the performance of air temperature simulated by the Weather Research and Forecasting model with station measurements over the contiguous United States (CONUS). Results of network analysis suggest a good match between simulation and measurement, as indicated by conventional metrics (r, RMSE, and MAE) as well. The sensitivity of these network metrics is then analyzed based on CONUS station measurements with additive random errors using Monte Carlo simulations. Network metrics show more sensitive and highly nonlinear responses to increasing random errors as compared to conventional ones. Moreover, we use the new toolkit for intercomparison of the downscaled historical air temperature outputs from four global climate models. The similarity in both metrics and spatial structure highlights the capability of network analysis for capturing system dynamics in models alike. The network theory is therefore promising for evaluation and intercomparison of various environmental modeling systems with complex dynamics.

A comparison of consumer-grade electronic radon monitors.

Radon is a radioactive gas which is naturally occurring in soil and can accumulate to concentrated levels inside homes and buildings. Exposure to elevated levels of radon leads to an increased risk of developing lung cancer. In recent years there has been a rise in the popularity of consumer-grade electronic radon monitors. The monitors are appealing to homeowners due to the ease of use and the ability to keep track of radon levels during the process of conducting a radon test. However, there is currently no independent process to evaluate the relative performance of these monitors against known levels of radon. In this study, three sample units of six different models representing three different manufacturers of consumer-grade electronic radon monitors were exposed to three different levels of radon in a controlled environment to evaluate their precision and accuracy. Two separate tests were conducted at the Canadian guideline level to accommodate for 'indoor winter' and 'summer' conditions. The purpose of the study was to compare the performance of the different consumer-grade electronic radon monitors and determine which factors should be considered when using these monitors to inform mitigation decisions. The monitors had a range of uncertainty from 2%-15% with a range of precision from 1%-24%. The monitors performed better at higher radon levels than at levels near the Canadian guideline level of 200 Bq m-3, and slightly better during 'summer' conditions than during 'indoor winter' conditions. While the monitors provide homeowners with a very specific number indicating their radon level, it was noted that this number should be considered with respect to a 'confidence ratio' or 'range' which could be done through a publicly available online tool which could provide the radon level range for a given radon level and device grade.

Twenty-first-century climate change impacts on marine animal biomass and ecosystem structure across ocean basins.

Climate change effects on marine ecosystems include impacts on primary production, ocean temperature, species distributions, and abundance at local to global scales. These changes will significantly alter marine ecosystem structure and function with associated socio-economic impacts on ecosystem services, marine fisheries, and fishery-dependent societies. Yet how these changes may play out among ocean basins over the 21st century remains unclear, with most projections coming from single ecosystem models that do not adequately capture the range of model uncertainty. We address this by using six marine ecosystem models within the Fisheries and Marine Ecosystem Model Intercomparison Project (Fish-MIP) to analyze responses of marine animal biomass in all major ocean basins to contrasting climate change scenarios. Under a high emissions scenario (RCP8.5), total marine animal biomass declined by an ensemble mean of 15%-30% (±12%-17%) in the North and South Atlantic and Pacific, and the Indian Ocean by 2100, whereas polar ocean basins experienced a 20%-80% (±35%-200%) increase. Uncertainty and model disagreement were greatest in the Arctic and smallest in the South Pacific Ocean. Projected changes were reduced under a low (RCP2.6) emissions scenario. Under RCP2.6 and RCP8.5, biomass projections were highly correlated with changes in net primary production and negatively correlated with projected sea surface temperature increases across all ocean basins except the polar oceans. Ecosystem structure was projected to shift as animal biomass concentrated in different size-classes across ocean basins and emissions scenarios. We highlight that climate change mitigation measures could moderate the impacts on marine animal biomass by reducing biomass declines in the Pacific, Atlantic, and Indian Ocean basins. The range of individual model projections emphasizes the importance of using an ensemble approach in assessing uncertainty of future change.