A long-standing challenge in studying the global carbon cycle has been understanding the factors controlling inter–annual variation (IAV) of carbon fluxes related to vegetation photosynthesis and respiration, and improving their representations in existing biogeochemical models. Here, we compared an optimality-based mechanistic model and a semi-empirical light use efficiency model to understand how current models can be improved to simulate IAV of gross primary production (GPP). Both models simulated hourly GPP and were parameterized for (1) each site–year, (2) each site with an additional constraint on IAV (CostIAV), (3) each site, (4) each plant–functional type, and (5) globally. This was followed by forward runs using calibrated parameters, and model evaluations at different temporal scales across 198 eddy covariance sites. Both models performed better on hourly scale than annual scale for most sites. Specifically, the mechanistic model substantially improved when drought stress was explicitly included. Most of the variability in model performances was due to model types and parameterization strategies. The semi-empirical model produced statistically better hourly simulations than the mechanistic model, and site–year parameterization yielded better annual performance for both models. Annual model performance did not improve even when parameterized using CostIAV. Furthermore, both models underestimated the peaks of diurnal GPP in each site–year, suggesting that improving predictions of peaks could produce a comparatively better annual model performance. GPP of forests were better simulated than grassland or savanna sites by both models. Our findings reveal current model deficiencies in representing IAV of carbon fluxes and guide improvements in further model development.

Biogeochemical cycling in permafrost-affected ecosystems remains associated with large uncertainties, which could impact the Earth’s greenhouse gas budget and future climate mitigation policies. In particular, increased nutrient availability following permafrost thaw could perturb biogeochemical cycling in permafrost systems, an effect largely unexplored in global assessments. In this study, we enhance the terrestrial ecosystem model QUINCY, which fully couples carbon (C), nitrogen (N) and phosphorus (P) cycles in vegetation and soil, with processes relevant in high latitudes (e.g., soil freezing and snow dynamics). We use this enhanced model to investigate impacts of increased carbon and nutrient availability from permafrost thawing in comparison to other climate-induced effects and CO2 fertilization over 1960 to 2019 over a multitude of tundra sites. Our simulation results suggest that vegetation growth in high latitudes is acutely N-limited at our case study sites. Despite this, enhanced availability of nutrients in the deep active layer following permafrost thaw, simulated to be around 0.1 m on average since the 1960s, accounts for only 11 % of the total GPP increase averaged over all sites. Our analysis suggests that the decoupling of the timing of peak vegetative growth (week 27-29 of the year, corresponding to mid-to-late July) and maximum thaw depth (week 34-37, corresponding to mid-to-late August), lead to an incomplete plant use of newly available nutrients at the permafrost front. Due to resulting increased availability of N at the permafrost table, as well as alternating water saturation levels, increases in both nitrification and denitrification enhance N2O emissions in the simulations. Our model thus suggests a weak (5 mg N m-2 yr-1) but increasing source of N2O, which reaches trends of up to +1 mg N m-2 yr-1 per decade, locally, which is potentially of large importance for the global N2O budget.

Many remote sensing-based evapotranspiration (RSBET) algorithms have been proposed in the past decades and evaluated using flux tower data, mainly over North America and Europe. Model evaluation across South America has been done locally or using only a single algorithm at a time. Here, we provide the first evaluation of multiple RSBET models, at a daily scale, across a wide variety of biomes, climate zones, and land uses in South America. We used meteorological data from 25 flux towers to force four remote sensing based ET models: Priestley & Taylor Jet Propulsion Laboratory (PT-JPL), Global Land Evaporation Amsterdam Model (GLEAM), Penman-Monteith Mu model (PM-MOD), and Penman-Monteith Nagler model (PM-VI). ET was predicted satisfactorily by all four models, with correlations consistently higher (R²>0.6) for GLEAM and PT-JPL, and PM-MOD and PM-VI presenting overall better responses in terms of PBIAS (-10

Carbon dioxide is a greenhouse gas that has a strong absorption in the 4.2– 4.3 micrometers region of the infrared spectrum. Consequently, non-dispersive infrared (NDIR) spectroscopy using interference optical filters tuned in this spectral band can be utilized to provide reliable, high resolution and fast response measurements of atmospheric CO2 concentrations. As part of eddy covariance systems, open-path gas analyzers based on this principle are widely used in remote locations around the world because of their low-power consumption, fast frequency response, and ease of operation. One of the challenges of the open-path design is that the in-situ optical beam is exposed to the rapid fluctuations in ambient temperature. Besides gas composition and pressure being the two major spectroscopic line broadening mechanisms that affect the absorption of infrared light, air temperature also can influence the broadened half-width and the intensity of the spectral lines. Consequently, the fast temperature fluctuations of the air parcels in the optical path of such a sensor can produce changes in the amount of absorbed light and cause errors in the gas concentration measurement that can propagate into the flux calculations. The temperature dependence of the infrared absorption has not been quantified in the context of the CO2 NDIR gas analyzer methodology. The study will evaluate the temperature effects on absorption spectra of CO2-air-mixtures across the 4.2–4.3 micrometers infrared active region, typically used by NDIR gas analyzers. Infrared spectra will be modeled line-by-line from spectral-line parameters obtained from the high-resolution transmission molecular spectroscopic database (HITRAN). HITRAN-predicted molecular cross sections, the product of component spectral line intensity and spectral line shape at different wavelength, will be used to generate absorption spectra of CO2 air mixtures at ambient pressure using different concentrations and temperatures. The temperature dependence of CO2 absorption will be inferred from the integrated area under the absorptivity curve. Results interpreted in the context of the Beer-Lambert law will further characterize the temperature related spectroscopic effects on CO2 concentration calculations.