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.

Snowpack accumulation in forested watersheds depends on the amount of snow intercepted in the canopy and its partitioning into sublimation, unloading, and melt. A lack of canopy snow measurements limits our ability to evaluate models that simulate canopy processes and predict snowpack and water supply. Here, we tested whether monitoring changes in wind-induced tree sway can enable snow interception detection and estimation of canopy snow water equivalent (SWE). We monitored hourly tree sway across six years based on 12 Hz accelerometer observations on two subalpine conifer trees in Colorado. We developed an approach to distinguish changes in sway frequency due to thermal effects on tree rigidity versus intercepted snow mass. Over 60% of days with canopy snow had a sway signal in the range of possible thermal effects. However, when tree sway decreased outside the range of thermal effects, canopy snow was present 93-95% of the time, as confirmed with classifications of PhenoCam imagery. Using sway tests, we converted significant changes in sway to canopy SWE, which was correlated with total snowstorm amounts from a nearby SNOTEL site (Spearman r=0.72 to 0.80, p<0.001). Greater canopy SWE was associated with storm temperatures between -7 C and 0 C and wind speeds less than 4 m/s. Lower canopy SWE prevailed in storms with lower temperatures and higher wind speeds. We conclude that monitoring tree sway is a viable approach for quantifying canopy SWE, but challenges remain in converting changes in sway to mass and further distinguishing thermal and mass effects on tree sway.

Anthropogenic global warming caused by increased atmospheric carbon forcing is expected to cause a decrease in peak snow water equivalent (SWE), shift the timing of snowmelt to earlier in the year, and lead to slower melt rates in the mountains of the Western United States. High-elevation forests in mountainous terrain represent a critical carbon sink. Understanding the ecohydrology of subalpine forests is crucial for assessing the health of these sinks. The Niwot Ridge Long Term Ecological Research station, located at 3000 m amsl in the southern Rocky Mountains of Colorado, receives just over 1 m of annual precipitation mostly as snow, supporting a persistent seasonal snowpack in alpine and subalpine ecosystems. Previous studies show that longer growing season length is correlated with shallower snowpack, earlier spring onset and reduced net CO2 uptake. Co-located sensors provide over 20 years of continuous SWE and eddy covariance (EC) data, allowing for robust direct comparison of snow and carbon phenomena in a high-elevation catchment. Linear regression and time series analysis was performed on snowmelt, meteorological, phenological and ecosystem productivity variables. Peak productivity is correlated with peak SWE (R2=0.54) and further correlated with snowmelt disappearance (R2=0.38) and the timing of spring growth onset (R2=0.30). Timing of both peak productivity and spring growth onset are correlated with snowmelt and meteorological variables. A multivariable regression of meteorological variables, timing of spring growth onset, a temporal trend, and snowmelt rate and explains 94% of interannual variability in the timing of peak forest productivity. These results develop support and introduce new evidence for the existing studies of Niwot Ridge ecohydrology. Future work will investigate the meteorological and hydrological record extending back to 1979 and the long-term trends in snowmelt and forest productivity.

The Western US accounts for a significant amount of the forested biomass and carbon uptake within the conterminous United States. Warming and drying climate trends combined with a legacy of fire suppression have left Western forests particularly vulnerable to disturbance from insects, fire and drought mortality. These challenging conditions may significantly weaken this region’s ability to uptake carbon from the atmosphere and warrant continued monitoring. Traditional methods of carbon monitoring are limited by the complex terrain of the Rocky Mountains that lead to complex atmospheric flows coupled with heterogeneous climate and soil conditions. Recently, solar induced fluorescence (SIF) has been found to be a strong indicator of GPP, and with the increased availability of remotely-sensed SIF, provides an opportunity to estimate GPP and ecosystem function across the Western US. Although the SIF-GPP empirical linkage is strong, the mechanistic understanding between SIF and GPP is lacking, and ultimately depends upon changes in leaf chemistry that convert absorbed radiation into photochemistry, heat (via non-photochemical quenching (NPQ)), leaf damage or SIF. Understanding of the mechanistic detail is necessary to fully leverage observed SIF to constrain model estimates of GPP and improve representation of ecosystem processes. Here, we include an improved fluorescence model within CLM 4.5 to simulate seasonal changes in SIF at a sub-alpine forest in Colorado. We find that when the model includes a representation of sustained NPQ the simulated fluorescence is much closer to the seasonal pattern of SIF observed from the GOME-2 satellite platform and a custom tower mounted spectrometer system. We also find that average air temperature may be used as a predictor of sustained NPQ when observations are not available. This relationship to air temperature is promising because it may allow for efficient spatial upscaling of SIF simulations, given widespread availability of temperature data, but not NPQ observations. Further improvements to the fluorescence model should focus upon distinguishing between the impacts of NPQ versus the de-activation of photosystems brought on by high-stress environmental conditions.

Accounting for temporal changes in carbon dioxide (CO2) emissions from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of eddy covariance flux measurements of CO2 from 13 lakes and reservoirs in the Northern Hemisphere (NH) and quantify the magnitude and dynamics at multiple temporal scales. We found pronounced diel and sub-monthly oscillatory variations in CO2 flux at all sites. Diel variation converted sites to daily net sinks of CO2 in only 11% of site-months. Upscaled annual emissions had an average of 25% (range 3-58%) interannual variation. Given temporal variation remains under-represented in inventories of CO2 emissions from lakes and reservoirs, revisions in CO2 flux are needed using a better representation of sub-daily to interannual variability. Constraining short- and long-term variability is necessary to improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers.

This collaborative article discusses the opportunities and challenges of adopting Integrated, Coordinated, Open, and Networked (ICON) principles in atmospheric sciences. From the global nature of the atmosphere, there has always been a need for atmospheric science to be an ICON science. With the help of evolving technology, it is possible to go further in implementing and spreading the ICON principles for productive global collaboration. In particular, technology transfer and applications could be approached with reproducibility in mind, and data-sharing infrastructure could enable easier and better international collaboration. There are, however, various challenges in following the ICON principles in the collection, quality control, and maintenance of data, and the publication of results in a systematic way. Moreover, the extent of such issues varies geographically and hence poses different challenges to ICON principles. In this commentary article, we briefly state our perspectives on the state of ICON, challenges we have met, and future opportunities. Furthermore, we describe how atmospheric science researchers have benefited from these collaborative multi-dimensional approaches that fulfill the core goal of ICON.

Accounting for temporal changes in carbon dioxide (CO2) emissions from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of eddy covariance flux measurements of CO2 from 13 lakes and reservoirs in the Northern Hemisphere (NH) and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO2 flux at all sites. Accounting for diel variation, only 11% of site-months were net daily sinks of CO2. Annual CO2 emissions had an average of 25% (range 3-58%) interannual variation. Nighttime emissions regularly exceeded daytime emissions. Sources of CO2 flux variability were delineated through mutual information analysis. Sample analysis of CO2 fluxes indicate importance of continuous sampling. Constraining short- and long-term variability is necessary to improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers.