Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

State of the climate in 2013

In 2013, the vast majority of the monitored climate variables reported here maintained trends established in recent decades. ENSO was in a neutral state during the entire year, remaining mostly on the cool side of neutral with modest impacts on regional weather patterns around the world. This follows several years dominated by the effects of either La Niña or El Niño events. According to several independent analyses, 2013 was again among the 10 warmest years on record at the global scale, both at the Earths surface and through the troposphere. Some regions in the Southern Hemisphere had record or near-record high temperatures for the year. Australia observed its hottest year on record, while Argentina and New Zealand reported their second and third hottest years, respectively. In Antarctica, Amundsen-Scott South Pole Station reported its highest annual temperature since records began in 1957. At the opposite pole, the Arctic observed its seventh warmest year since records began in the early 20th century. At 20-m depth, record high temperatures were measured at some permafrost stations on the North Slope of Alaska and in the Brooks Range. In the Northern Hemisphere extratropics, anomalous meridional atmospheric circulation occurred throughout much of the year, leading to marked regional extremes of both temperature and precipitation. Cold temperature anomalies during winter across Eurasia were followed by warm spring temperature anomalies, which were linked to a new record low Eurasian snow cover extent in May. Minimum sea ice extent in the Arctic was the sixth lowest since satellite observations began in 1979. Including 2013, all seven lowest extents on record have occurred in the past seven years. Antarctica, on the other hand, had above-average sea ice extent throughout 2013, with 116 days of new daily high extent records, including a new daily maximum sea ice area of 19.57 million km2 reached on 1 October. ENSO-neutral conditions in the eastern central Pacific Ocean and a negative Pacific decadal oscillation pattern in the North Pacific had the largest impacts on the global sea surface temperature in 2013. The North Pacific reached a historic high temperature in 2013 and on balance the globally-averaged sea surface temperature was among the 10 highest on record. Overall, the salt content in nearsurface ocean waters increased while in intermediate waters it decreased. Global mean sea level continued to rise during 2013, on pace with a trend of 3.2 mm yr-1 over the past two decades. A portion of this trend (0.5 mm yr-1) has been attributed to natural variability associated with the Pacific decadal oscillation as well as to ongoing contributions from the melting of glaciers and ice sheets and ocean warming. Global tropical cyclone frequency during 2013 was slightly above average with a total of 94 storms, although the North Atlantic Basin had its quietest hurricane season since 1994. In the Western North Pacific Basin, Super Typhoon Haiyan, the deadliest tropical cyclone of 2013, had 1-minute sustained winds estimated to be 170 kt (87.5 m s-1) on 7 November, the highest wind speed ever assigned to a tropical cyclone. High storm surge was also associated with Haiyan as it made landfall over the central Philippines, an area where sea level is currently at historic highs, increasing by 200 mm since 1970. In the atmosphere, carbon dioxide, methane, and nitrous oxide all continued to increase in 2013. As in previous years, each of these major greenhouse gases once again reached historic high concentrations. In the Arctic, carbon dioxide and methane increased at the same rate as the global increase. These increases are likely due to export from lower latitudes rather than a consequence of increases in Arctic sources, such as thawing permafrost. At Mauna Loa, Hawaii, for the first time since measurements began in 1958, the daily average mixing ratio of carbon dioxide exceeded 400 ppm on 9 May. The state of these variables, along with dozens of others, and the 2013 climate conditions of regions around the world are discussed in further detail in this 24th edition of the State of the Climate series. © 2014, American Meteorological Society. All rights reserved

Protein and fat meal content increase insulin requirement in children with type 1 diabetes â Role of duration of diabetes

Background and objective: Hyperglycaemia remains a challenge in type 1 diabetes since current regimes used to determine meal insulin requirements prove to be ineffective. This is particularly problematic for meals containing high amounts of protein and fat. We aimed to determine the post-prandial glycaemic response and total insulin need for mixed meals, using sensor-augmented insulin pumps in children with type 1 diabetes. Methods: Twenty-two children with type 1 diabetes, aged 4â17â¯years on insulin pump therapy completed this home-based, cross-over, randomised controlled trial. Two meals with identical carbohydrate content â one with low fat and protein (LFLP) and one with high fat and protein (HFHP) contents â were consumed using normal insulin boluses. Blood glucose monitoring was done for 10â¯h post-meal, with correction bolus insulin given two-hourly if required. Results: The HFHP meal required significantly more total insulin (3.48 vs. 2.7 units) as a result of increased post-meal correction insulin requirement (1.2 vs. 0.15 units) spread over a longer duration (6 vs. 3â¯h). The HFHP meals significantly increased the time spent above target glucose level. Duration of diabetes and total daily insulin use significantly influenced the post-prandial blood glucose response to the two meals. Conclusion: When consuming carbohydrate-based mixed meals, children with type 1 diabetes on insulin pump therapy, required significantly more insulin over a longer period of time than the insulin requirement calculated using current regimes. This additional amount required is influenced by the duration of diabetes and total daily insulin use. Keywords: Carbohydrate, Protein and fat, Type 1 diabetes, Glucose, Insulin infusion system

Interactions between C-reactive protein genotypes with markers of nutritional status in relation to inflammation

Inflammation, as indicated by C-reactive protein concentrations (CRP), is a risk factor for chronic diseases. Both genetic and environmental factors affect susceptibility to inflammation. As dietary interventions can influence inflammatory status, we hypothesized that dietary effects could be influenced by interactions with single nucleotide polymorphisms (SNPs) in the CRP gene. We determined 12 CRP SNPs, as well as various nutrition status markers in 2010 black South Africans and analyzed their effect on CRP. Interactions were observed for several genotypes with obesity in determining CRP. Lipid intake modulated the pro-inflammatory effects of some SNPs, i.e., an increase in both saturated fatty acid and monounsaturated fatty acid intake in those homozygous for the polymorphic allele at rs2808630 was associated with a larger increase in CRP. Those harboring the minor alleles at rs3093058 and rs3093062 presented with significantly higher CRP in the presence of increased triglyceride or cholesterol intake. When harboring the minor allele of these SNPs, a high omega-6 to -3 ratio was, however, found to be anti-inflammatory. Carbohydrate intake also modulated CRP SNPs, as HbA1C and fasting glucose levels interacted with some SNPs to influence the CRP. This investigation highlights the impact that nutritional status can have on reducing the inherent genetic susceptibility to a heightened systemic inflammatory state

The use of predefined diet quality scores in the context of CVD risk during urbanization in the South African Prospective Urban and Rural Epidemiological (PURE) study

Objective Urbanization is generally associated with increased CVD risk and accompanying dietary changes. Little is known regarding the association between increased CVD risk and dietary changes using approaches such as diet quality. The relevance of predefined diet quality scores (DQS) in non-Western developing countries has not yet been established. Design The association between dietary intakes and CVD risk factors was investigated using two DQS, adapted to the black South African diet. Dietary intake data were collected using a quantitative FFQ. CVD risk was determined by analysing known CVD risk factors. Setting Urban and rural areas in North West Province, South Africa. Subjects Apparently healthy volunteers from the South African Prospective Urban and Rural Epidemiological (PURE) study population (n 1710). Results CVD risk factors were significantly increased in the urban participants, especially women. Urban men and women had significantly higher intakes of both macro- and micronutrients with macronutrient intakes well within the recommended CVD guidelines. While micronutrient intakes were generally higher in the urban groups than in the rural groups, intakes of selected micronutrients were low in both groups. Both DQS indicated improved diet quality in the urban groups and good agreement was shown between the scores, although they seemed to measure different aspects of diet quality. Conclusions The apparent paradox between improved diet quality and increased CVD risk in the urban groups can be explained when interpreting the cut-offs used in the scores against the absolute intakes of individual nutrients. Predefined DQS as well as current guidelines for CVD prevention should be interpreted with caution in non-Western developing countries

Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

© 2021 Elsevier B.V.Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)