The mathematical representation of large data sets of electronic energies has seen substantial progress in the past 10 years. The so-called Permutationally Invariant Polynomial (PIP) representation ...is one established approach. This approach dates from 2003, when a global potential energy surface (PES) for CH5 + was reported using a basis of polynomials that are invariant with respect to the 120 permutations of the five equivalent H atoms. More recently, several approaches from “machine learning” have been applied to fit these large data sets. Gaussian Process (GP) regression is such an approach. Here, we consider the implementation of the (full) GP due to Krems and co-workers, with a modification that renders it permutationally invariant, which we denote by PIP-GP. This modification uses the approach of Guo and co-workers and later extended by Zhang and co-workers, to achieve permutational invariance for neural-network fits. The PIP, GP, and PIP-GP approaches are applied to four case studies for fitting data sets of electronic energies: H3O+, OCHCO+, and H2CO/cis-HCOH/trans-HCOH with the goal of assessing precision, accuracy in normal-mode analysis and barrier heights, and timings. We also report an application to (HCOOH)2, where the full PIP approach is possible but where the PIP-GP one is not feasible. However, by replicating data, which is feasible in this case, the GP approach is able to represent the data with precision comparable to that of the PIP approach. We examine these assessments for varying sizes of data sets in each case to determine the dependence of properties of the fits on the training data size. We conclude with some comments on the different aspects of computational effort of the PIP, GP, and PIP-GP approaches and also challenges these methods face for more “rugged” PESs, exemplified here by H2CO/cis-HCOH/trans-HCOH.
Annual soil moisture estimates are useful to characterize trends in the climate system, in the capacity of soils to retain water and for predicting land and atmosphere interactions. The main source ...of soil moisture spatial information across large areas (e.g., continents) is satellite-based microwave remote sensing. However, satellite soil moisture datasets have coarse spatial resolution (e.g., 25-50 km grids); and large areas from regional-to-global scales have spatial information gaps. We provide an alternative approach to predict soil moisture spatial patterns (and associated uncertainty) with higher spatial resolution across areas where no information is otherwise available. This approach relies on geomorphometry derived terrain parameters and machine learning models to improve the statistical accuracy and the spatial resolution (from 27km to 1km grids) of satellite soil moisture information across the conterminous United States on an annual basis (1991-2016). We derived 15 primary and secondary terrain parameters from a digital elevation model. We trained a machine learning algorithm (i.e., kernel weighted nearest neighbors) for each year. Terrain parameters were used as predictors and annual satellite soil moisture estimates were used to train the models. The explained variance for all models-years was >70% (10-fold cross-validation). The 1km soil moisture grids (compared to the original satellite soil moisture estimates) had higher correlations (improving from r2 = 0.1 to r2 = 0.46) and lower bias (improving from 0.062 to 0.057 m3/m3) with field soil moisture observations from the North American Soil Moisture Database (n = 668 locations with available data between 1991-2013; 0-5cm depth). We conclude that the fusion of geomorphometry methods and satellite soil moisture estimates is useful to increase the spatial resolution and accuracy of satellite-derived soil moisture. This approach can be applied to other satellite-derived soil moisture estimates and regions across the world.
Global soils store at least twice as much carbon as Earth's atmosphere
. The global soil-to-atmosphere (or total soil respiration, R
) carbon dioxide (CO
) flux is increasing
, but the degree to ...which climate change will stimulate carbon losses from soils as a result of heterotrophic respiration (R
) remains highly uncertain
. Here we use an updated global soil respiration database
to show that the observed soil surface R
:R
ratio increased significantly, from 0.54 to 0.63, between 1990 and 2014 (P = 0.009). Three additional lines of evidence provide support for this finding. By analysing two separate global gross primary production datasets
, we find that the ratios of both R
and R
to gross primary production have increased over time. Similarly, significant increases in R
are observed against the longest available solar-induced chlorophyll fluorescence global dataset, as well as gross primary production computed by an ensemble of global land models. We also show that the ratio of night-time net ecosystem exchange to gross primary production is rising across the FLUXNET2015
dataset. All trends are robust to sampling variability in ecosystem type, disturbance, methodology, CO
fertilization effects and mean climate. Taken together, our findings provide observational evidence that global R
is rising, probably in response to environmental changes, consistent with meta-analyses
and long-term experiments
. This suggests that climate-driven losses of soil carbon are currently occurring across many ecosystems, with a detectable and sustained trend emerging at the global scale.
We present a machine-learning method for predicting sharp transitions in a Hamiltonian phase diagram by extrapolating the properties of quantum systems. The method is based on Gaussian process ...regression with a combination of kernels chosen through an iterative procedure maximizing the predicting power of the kernels. The method is capable of extrapolating across the transition lines. The calculations within a given phase can be used to predict not only the closest sharp transition but also a transition removed from the available data by a separate phase. This makes the present method particularly valuable for searching phase transitions in the parts of the parameter space that cannot be probed experimentally or theoretically.
Climate reanalyses complement traditional surface-based measurements and offer unprecedented coverage over previously inaccessible or unmonitored regions. Even though these have improved the ...quantification of the global water cycle, their varying performances and uncertainties limit their applicability. Herein, we discuss how a framework encompassing precipitation, evaporation, their difference, and their sum could further constrain uncertainty by unveiling discrepancies otherwise overlooked. Ahead, we physically define precipitation plus evaporation to describe the global water cycle fluxes in four reanalysis data sets (20CR v3, ERA-20C, ERA5, and NCEP1). Among them, we observe four different responses to the temperature increase between 1950-2010, with ERA5 showing the best agreement with the water cycle acceleration hypothesis. Our results show that implementing the framework proposed can improve the evaluation of reanalyses' performance and enhance our understanding of the water cycle changes on a global scale.
Average net ecosystem exchange (NEE) and CH4 exchange in grams of carbon per square meter across different plant phenological phases in this temperate salt marsh dominated by grass species. Plant ...phenological phases are: Greenup (˜April to June), when grasses start to grow, Maturity (˜July to September), when grasses reach their peak of growth and greenness, Senescence (˜September to October), when grasses start to decrease in greenness, and Dormancy (˜November to March), when grasses are inactive. Solid blue arrows represent carbon uptake by the ecosystem; solid red arrows represent carbon emissions from CO2; and red dashed arrows carbon emissions from CH4. This salt marsh was a net source of carbon during the annual cycle.
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•Plant phenological phases influence NEE and CH4 exchange•CO2 and CH4 emissions during senescence and dormancy overshadowed annual carbon uptakes•Light availability partially explained NEE variability•Lower water table level increased ecosystem-scale CH4 emissions•This temperate tidal salt marsh was a net source of carbon to the atmosphere
Salt marshes are large carbon reservoirs as part of blue carbon ecosystems. Unfortunately, there is limited information about the net ecosystem (NEE) and methane (CH4) exchange between salt marshes and the atmosphere to fully understand their carbon dynamics. We tested the influence of biophysical drivers by plant phenological phases (i.e., Greenup, Maturity, Senescence and Dormancy) on NEE and CH4 exchange in a grass-dominated temperate tidal salt marsh. We used three years of data derived from eddy covariance, PhenoCam (to measure vegetation phenology), and ancillary meteorological and water/soil variables. Overall, NEE showed significant differences among all phenological phases (p < 0.05), while CH4 exchange had significant differences among all phases except for Greenup and Dormancy. Net CO2 uptake was higher across Maturity (-61 g C-CO2 m2), while CO2 emissions were higher during Dormancy (182 g C-CO2 m2). The lower but constant CO2 emissions during Dormancy overshadowed the CO2 uptake during the growing season and contributed to >72% of the annual CO2 emissions in this ecosystem. Net CH4 emissions were higher during Maturity (3.7 g C-CH4 m2) and Senescence (4.2 g C-CH4 m2). Photosynthetically active radiation (PAR) substantially influenced (r2 > 0.57) daytime NEE across phenological phases, but a combination of variables including water table level (WTL), water temperature and atmospheric pressure were relevant to explain CH4 exchange. The study site was an overall net carbon source to the atmosphere with annual emissions of 13-201 g C-CO2 m−2yr−1 and 8.5-15.2 g C-CH4 m−2yr−1. Our findings provide insights on: a) the role of plant phenological phases on ecosystem-scale CO2 and CH4 fluxes; b) challenges for modeling ecosystem-scale CO2 and CH4 fluxes in salt marshes; and c) the potential net loss of carbon to the atmosphere that should be considered for carbon management and accounting in these ecosystems.
A current challenge is to understand what are the legacies left by disturbances on ecosystems for predicting response patterns and trajectories. This work focuses on the ecological implications of a ...major hurricane and analyzes its influence on forest gross primary productivity (GPP; derived from the moderate-resolution imaging spectroradiometer, MODIS) and soil CO2 efflux. Following the hurricane, there was a reduction of nearly 0.5 kgC m−2 yr−1, equivalent to ∼15% of the long-term mean GPP (∼3.0 ± 0.2 kgC m−2 yr−1; years 2003-8). Annual soil CO2 emissions for the year following the hurricane were > 3.9 ± 0.5 kgC m−2 yr−1, whereas for the second year emissions were 1.7 ± 0.4 kgC m−2 yr−1. Higher annual emissions were associated with higher probabilities of days with extreme soil CO2 efflux rates ( > 9.7 μmol CO2 m−2 s−1). The variance of GPP was highly variable across years and was substantially increased following the hurricane. Extreme soil CO2 efflux after the hurricane was associated with deposition of nitrogen-rich fresh organic matter, higher basal soil CO2 efflux rates and changes in variance of the soil temperature. These results show that CO2 dynamics are highly variable following hurricanes, but also demonstrate the strong resilience of tropical forests following these events.
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