Divergence in land carbon cycle simulation is persistent and widespread. Regardless of model intercomparison project, results from individual models diverge significantly from each other and, in consequence, from reference datasets. Here we link model spread to structure using a 15-member ensemble of land surface models from the Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP) as a test case. Our analysis uses functional benchmarks and model structure as predicted by model skill in a machine learning framework to isolate discrete aspects of model structure associated with divergence. Wealso quantify how initial conditions prejudice present-day model outcomes after centennial-scale transient simulations. Overall, the functional benchmark and machine learning exercises emphasize the importance of ecosystem structure in correctly simulating carbon and water cycling, highlight uncertainties in the structure of carbon pools, and advise against hard parametric limits on ecosystem function. Wealso find that initial conditions explain 90% of variation in global satellite-era values-initial conditions largely predetermine transient endpoints, historical environmental change not withstanding. As MsTMIP prescribes forcing data and spin-up protocol, the range in initial conditions and high levels of predetermination are also structural. Our results suggest that methodological tools linking divergence to discrete aspects of model structure would complement current community best practices in model development.

Given the magnitude of soil carbon stocks in northern ecosystems, and the vulnerability of these stocks to climate warming, land surface models must accurately represent soil carbon dynamics in these regions. We evaluate soil carbon stocks and turnover rates, and the relationship between soil carbon loss with soil temperature and moisture, from an ensemble of eleven global land surface models. We focus on the region of NASA's Arctic-Boreal vulnerability experiment (ABoVE) in North America to inform data collection and model development efforts. Models exhibit an order of magnitude difference in estimates of current total soil carbon stocks, generally under- or overestimating the size of current soil carbon stocks by greater than 50 PgC. We find that a model's soil carbon stock at steady-state in 1901 is the prime driver of its soil carbon stock a hundred years later-overwhelming the effect of environmental forcing factors like climate. The greatest divergence between modeled and observed soil carbon stocks is in regions dominated by peat and permafrost soils, suggesting that models are failing to capture the frozen soil carbon dynamics of permafrost regions. Using a set of functional benchmarks to test the simulated relationship of soil respiration to both soil temperature and moisture, we find that although models capture the observed shape of the soil moisture response of respiration, almost half of the models examined show temperature sensitivities, or Q10 values, that are half of observed. Significantly, models that perform better against observational constraints of respiration or carbon stock size do not necessarily perform well in terms of their functional response to key climatic factors like changing temperature. This suggests that models may be arriving at the right result, but for the wrong reason. The results of this work can help to bridge the gap between data and models by both pointing to the need to constrain initial carbon pool sizes, as well as highlighting the importance of incorporating functional benchmarks into ongoing, mechanistic modeling activities such as those included in ABoVE.

While substantial attention has been paid to the effects of both global climate oscillations and local meteorological conditions on the interannual variability of ecosystem carbon exchange, the relationship between the interannual variability of synoptic meteorology and ecosystem carbon exchange has not been well studied. Here we use a clustering algorithm to identify a summertime cyclonic precipitation system northwest of the Great Lakes to determine (a) the association at a daily scale between the occurrence of this system and the local meteorology and net ecosystem exchange at three Great Lakes region forested eddy covariance sites and (b) the association between the seasonal prevalence of this system and the summertime net ecosystem exchange of these sites. We find that temperature, in addition to precipitation and cloud cover, is an important explanatory factor for the suppression of net ecosystem productivity that occurs during these cyclonic events in this region. In addition, the prevalence of this cyclonic system can explain a significant proportion of the interannual variability in summertime forest ecosystem exchange in this region. This explanatory power is not due to a simple accumulation of low-productivity days that cooccur with this meteorological event, but rather a broader association between the frequency of these events and several aspects of prevailing seasonal conditions. This work demonstrates the usefulness of conceptualizing meteorology in terms of synoptic systems for explaining the interannual variability of regional carbon fluxes.

Large uncertainties in North American terrestrial carbon fluxes hinder regional climate projections. Terrestrial biosphere models (TBMs), the essential tools for understanding continental-scale carbon cycle, diverge on whether temperate forests or croplands dominate carbon uptake in North America. Evidence from novel photosynthetic proxies, such as those based on chlorophyll fluorescence, has cast doubt on the "weak cropland, strong forest" carbon uptake patterns simulated by most TBMs. However, no systematic evaluation of TBMs has yet been attempted to pin down space-time patterns that are most consistent with regional CO2 observational constraints. Here, we leverage atmospheric CO2 observations and satellite-observed photosynthetic proxies to understand emergent space-time patterns in North American carbon fluxes from a large suite of TBMs and data-driven models. To do so, we evaluate how well the atmospheric signals resulting from carbon flux estimates reproduce the space-time variability in atmospheric CO2, as is observed by a network of continuous-monitoring towers over North America. Models with gross or net carbon fluxes that are consistent with the observed CO2 variability share a salient feature of growing-season carbon uptake in Midwest US croplands. Conversely, the remaining models place most growing-season uptake in boreal or temperate forests. Differences in model explanatory power depend mainly on the simulated annual cycles of cropland uptake-especially, the timing of peak uptake-rather than the distribution of annual mean fluxes across biomes. Our results suggest that improved model representation of cropland phenology is crucial to robust, policy-relevant estimation of North American carbon exchange.

Understanding plant responses to hydrological extremes is critical for projections of the future terrestrial carbon uptake, but much more is known about the impacts of drought than of extreme wet conditions. However, the latter may control ecosystem-scale photosynthesis more strongly than the former in certain regions. Here we take a data-driven, location-based approach to evaluate where wet and dry extremes most affect photosynthesis. By comparing the sensitivity of vegetation greenness during extreme wetness to that during extreme dryness over a 34 year record, we find that regions where the impact of wet extremes dominates are nearly as common as regions where drought impacts dominate. We also demonstrate that the responses of wet-sensitive regions are not uniform and are instead controlled by multiple, often interacting, mechanisms. Given predicted increases in the frequency and intensity of extreme hydrological events with climate change, the consequences of extreme wet conditions on local and global carbon cycling will likely be amplified in future decades.

The future carbon balance of boreal ecosystems under increasing temperatures is highly uncertain. In particular, the net effects of a longer growing season versus enhanced respiration are poorly understood. Here, we use a geostatistical inverse model from 2012 to 2014 to determine temperature sensitivity in Alaskan biomes throughout the growing season, in order to identify the relative effects of these competing phenomena. We find that temperature explains a large portion of the disparities in autumn carbon flux between 2013 and 2014. Boreal forests experienced a growing season extension during the warm October of 2013 that offset increased respiration into autumn in years with high temperatures. In contrast, increased temperatures in the tundra and shrublands led to a large respiration signal during October 2013, producing a greater net carbon release. These results suggest a greater vulnerability of Alaskan tundra and shrubland carbon stocks compared to boreal forest carbon stocks under warming.

Agricultural intensification in India has increased nitrogen pollution, leading to water quality impairments. The fate of reactive nitrogen applied to the land is largely unknown, however. Long-term records of riverine nitrogen fluxes are nonexistent and drivers of variability remain unexamined, limiting the development of nitrogen management strategies. Here, we leverage dissolved inorganic nitrogen (DIN) and discharge data to characterize the seasonal, annual, and regional variability of DIN fluxes and their drivers for seven major river basins from 1981 to 2014. We find large seasonal and interannual variability in nitrogen runoff, with 68% to 94% of DIN fluxes occurring in June through October and with the coefficient of variation across years ranging from 44% to 93% for individual basins. This variability is primarily explained by variability in precipitation, with year- and basin-specific annual precipitation explaining 52% of the combined regional and interannual variability. We find little correlation with rising fertilizer application rates in five of the seven basins, implying that agricultural intensification has thus far primarily impacted groundwater and atmospheric emissions rather than riverine runoff. These findings suggest that riverine nitrogen runoff in India is highly sensitive to projected future increases in precipitation and intensification of the seasonal monsoon, while the impact of projected continued land use intensification is highly uncertain.

We have detected Ly alpha emission from a damped Ly alpha system (DLA) that lies near the bright quasar HS1549+1919. The DLA has the same redshift as HS1549+1919 and was discovered in the spectrum of a faint QSO that lies 4900 away (380 proper kpc). The emission line's luminosity, double-peaked profile, and small spatial separation from the DLA suggest that it may be fluorescent Ly alpha emission from gas that is absorbing the nearby QSO's radiation. If this is the case, our observations show that the DLA has a size of at least 1."5 and that the QSO's luminosity 1 million years ago was similar to its luminosity today. A survey for similar systems within similar to 1' of bright QSOs would put interesting limits on the mean quasar lifetime.