First measurement of the 14N(p,gamma)15O cross section down to 70 keV

In stars with temperatures above 20*10^6 K, hydrogen burning is dominated by the CNO cycle. Its rate is determined by the slowest process, the 14N(p,gamma)15O reaction. Deep underground in Italy's Gran Sasso laboratory, at the LUNA 400 kV accelerator, the cross section of this reaction has been measured at energies much lower than ever achieved before. Using a windowless gas target and a 4pi BGO summing detector, direct cross section data has been obtained down to 70 keV, reaching a value of 0.24 picobarn. The Gamow peak has been covered by experimental data for several scenarios of stable and explosive hydrogen burning. In addition, the strength of the 259 keV resonance has been remeasured. The thermonuclear reaction rate has been calculated for temperatures 90 - 300 *10^6 K, for the first time with negligible impact from extrapolations

FLUXNET-CH₄ synthesis activity: objectives, observations, and future directions

We describe a new coordination activity and initial results for a global synthesis of eddy covariance CH4 flux measurements

Substantial hysteresis in emergent temperature sensitivity of global wetland CH₄ emissions

Wetland methane (CH4) emissions (FCH4 ) are important in global carbon budgets and climate change assessments. Currently, FCH4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent FCH4 temperature dependence across spatial scales for use in models; however, sitelevel studies demonstrate that FCH4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between FCH4 and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between FCH4 and temperature, suggesting larger FCH4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.s

Substantial hysteresis in emergent temperature sensitivity of global wetland CH4_{4} emissions

Wetland methane (CH$_{4}$) emissions (F$_{CH_{4}}$) are important in global carbon budgets and climate change assessments. Currently, F$_{CH_{4}}$ projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent F$_{CH_{4}}$ temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that F$_{CH_{4}}$ are often controlled by factors beyond temperature. Here, we evaluate the relationship between F$_{CH_{4}}$ and temperature using observations from the FLUXNET-CH$_{4}$ database. Measurements collected across the globe show substantial seasonal hysteresis between F$_{CH_{4}}$ and temperature, suggesting larger F$_{CH_{4}}$ sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH$_{4}$ production are thus needed to improve global CH$_{4}$ budget assessments

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)

Golden Rule of Forecasting: Be Conservative

This article proposes a unifying theory, or the Golden Rule, or forecasting. The Golden Rule of Forecasting is to be conservative. A conservative forecast is consistent with cumulative knowledge about the present and the past. To be conservative, forecasters must seek out and use all knowledge relevant to the problem, including knowledge of methods validated for the situation. Twenty-eight guidelines are logically deduced from the Golden Rule. A review of evidence identified 105 papers with experimental comparisons; 102 support the guidelines. Ignoring a single guideline increased forecast error by more than two-fifths on average. Ignoring the Golden Rule is likely to harm accuracy most when the situation is uncertain and complex, and when bias is likely. Non-experts who use the Golden Rule can identify dubious forecasts quickly and inexpensively. To date, ignorance of research findings, bias, sophisticated statistical procedures, and the proliferation of big data, have led forecasters to violate the Golden Rule. As a result, despite major advances in evidence-based forecasting methods, forecasting practice in many fields has failed to improve over the past half-century