Attribution of Space-Time Variability in Global-Ocean Dissolved Inorganic Carbon.

The inventory and variability of oceanic dissolved inorganic carbon (DIC) is driven by the interplay of physical, chemical, and biological processes. Quantifying the spatiotemporal variability of these drivers is crucial for a mechanistic understanding of the ocean carbon sink and its future trajectory. Here, we use the Estimating the Circulation and Climate of the Ocean-Darwin ocean biogeochemistry state estimate to generate a global-ocean, data-constrained DIC budget and investigate how spatial and seasonal-to-interannual variability in three-dimensional circulation, air-sea CO2 flux, and biological processes have modulated the ocean sink for 1995-2018. Our results demonstrate substantial compensation between budget terms, resulting in distinct upper-ocean carbon regimes. For example, boundary current regions have strong contributions from vertical diffusion while equatorial regions exhibit compensation between upwelling and biological processes. When integrated across the full ocean depth, the 24-year DIC mass increase of 64 Pg C (2.7 Pg C year-1) primarily tracks the anthropogenic CO2 growth rate, with biological processes providing a small contribution of 2% (1.4 Pg C). In the upper 100 m, which stores roughly 13% (8.1 Pg C) of the global increase, we find that circulation provides the largest DIC gain (6.3 Pg C year-1) and biological processes are the largest loss (8.6 Pg C year-1). Interannual variability is dominated by vertical advection in equatorial regions, with the 1997-1998 El Niño-Southern Oscillation causing the largest year-to-year change in upper-ocean DIC (2.1 Pg C). Our results provide a novel, data-constrained framework for an improved mechanistic understanding of natural and anthropogenic perturbations to the ocean sink.

Observing carbon cycle-climate feedbacks from space.

The impact of human emissions of carbon dioxide and methane on climate is an accepted central concern for current society. It is increasingly evident that atmospheric concentrations of carbon dioxide and methane are not simply a function of emissions but that there are myriad feedbacks forced by changes in climate that affect atmospheric concentrations. If these feedbacks change with changing climate, which is likely, then the effect of the human enterprise on climate will change. Quantifying, understanding, and articulating the feedbacks within the carbon-climate system at the process level are crucial if we are to employ Earth system models to inform effective mitigation regimes that would lead to a stable climate. Recent advances using space-based, more highly resolved measurements of carbon exchange and its component processes-photosynthesis, respiration, and biomass burning-suggest that remote sensing can add key spatial and process resolution to the existing in situ systems needed to provide enhanced understanding and advancements in Earth system models. Information about emissions and feedbacks from a long-term carbon-climate observing system is essential to better stewardship of the planet.

Prolonged suppression of ecosystem carbon dioxide uptake after an anomalously warm year.

Terrestrial ecosystems control carbon dioxide fluxes to and from the atmosphere through photosynthesis and respiration, a balance between net primary productivity and heterotrophic respiration, that determines whether an ecosystem is sequestering carbon or releasing it to the atmosphere. Global and site-specific data sets have demonstrated that climate and climate variability influence biogeochemical processes that determine net ecosystem carbon dioxide exchange (NEE) at multiple timescales. Experimental data necessary to quantify impacts of a single climate variable, such as temperature anomalies, on NEE and carbon sequestration of ecosystems at interannual timescales have been lacking. This derives from an inability of field studies to avoid the confounding effects of natural intra-annual and interannual variability in temperature and precipitation. Here we present results from a four-year study using replicate 12,000-kg intact tallgrass prairie monoliths located in four 184-m(3) enclosed lysimeters. We exposed 6 of 12 monoliths to an anomalously warm year in the second year of the study and continuously quantified rates of ecosystem processes, including NEE. We find that warming decreases NEE in both the extreme year and the following year by inducing drought that suppresses net primary productivity in the extreme year and by stimulating heterotrophic respiration of soil biota in the subsequent year. Our data indicate that two years are required for NEE in the previously warmed experimental ecosystems to recover to levels measured in the control ecosystems. This time lag caused net ecosystem carbon sequestration in previously warmed ecosystems to be decreased threefold over the study period, compared with control ecosystems. Our findings suggest that more frequent anomalously warm years, a possible consequence of increasing anthropogenic carbon dioxide levels, may lead to a sustained decrease in carbon dioxide uptake by terrestrial ecosystems.

Grazing and no-till cropping impacts on nitrogen retention in dryland agroecosystems.

As the world's population increases, marginal lands such as drylands are likely to become more important for food production. One proven strategy for improving crop production in drylands involves shifting from conventional tillage to no-till to increase water use efficiency, especially when this shift is coupled with more intensive crop rotations. Practices such as no-till that reduce soil disturbance and increase crop residues may promote C and N storage in soil organic matter, thus promoting N retention and reducing N losses. By sampling soils 15 yr after a N tracer addition, this study compared long-term soil N retention across several agricultural management strategies in current and converted shortgrass steppe ecosystems: grazed and ungrazed native grassland, occasionally mowed planted perennial grassland, and three cropping intensities of no-till dryland cropping. We also examined effects of the environmental variables site location and topography on N retention. Overall, the long-term soil N retention of >18% in these managed semiarid ecosystems was high compared with published values for other cropped or grassland ecosystems. Cropping practices strongly influenced long-term N retention, with planted perennial grass systems retaining >90% of N in soil compared with 30% for croplands. Grazing management, topography, and site location had smaller effects on long-term N retention. Estimated 15-yr N losses were low for intact and cropped systems. This work suggests that semiarid perennial grass ecosystems are highly N retentive and that increased intensity of semiarid land management can increase the amount of protein harvested without increasing N losses.

Ecological forecasting and data assimilation in a data-rich era.

Several forces are converging to transform ecological research and increase its emphasis on quantitative forecasting. These forces include (1) dramatically increased volumes of data from observational and experimental networks, (2) increases in computational power, (3) advances in ecological models and related statistical and optimization methodologies, and most importantly, (4) societal needs to develop better strategies for natural resource management in a world of ongoing global change. Traditionally, ecological forecasting has been based on process-oriented models, informed by data in largely ad hoc ways. Although most ecological models incorporate some representation of mechanistic processes, today's models are generally not adequate to quantify real-world dynamics and provide reliable forecasts with accompanying estimates of uncertainty. A key tool to improve ecological forecasting and estimates of uncertainty is data assimilation (DA), which uses data to inform initial conditions and model parameters, thereby constraining a model during simulation to yield results that approximate reality as closely as possible. This paper discusses the meaning and history of DA in ecological research and highlights its role in refining inference and generating forecasts. DA can advance ecological forecasting by (1) improving estimates of model parameters and state variables, (2) facilitating selection of alternative model structures, and (3) quantifying uncertainties arising from observations, models, and their interactions. However, DA may not improve forecasts when ecological processes are not well understood or never observed. Overall, we suggest that DA is a key technique for converting raw data into ecologically meaningful products, which is especially important in this era of dramatically increased availability of data from observational and experimental networks.

Concurrent and lagged impacts of an anomalously warm year on autotrophic and heterotrophic components of soil respiration: a deconvolution analysis.

*Partitioning soil respiration into autotrophic (R(A)) and heterotrophic (R(H)) components is critical for understanding their differential responses to climate warming. *Here, we used a deconvolution analysis to partition soil respiration in a pulse warming experiment. We first conducted a sensitivity analysis to determine which parameters can be identified by soil respiration data. A Markov chain Monte Carlo technique was then used to optimize those identifiable parameters in a terrestrial ecosystem model. Finally, the optimized parameters were employed to quantify R(A) and R(H) in a forward analysis. *Our results displayed that more than one-half of parameters were constrained by daily soil respiration data. The optimized model simulation showed that warming stimulated R(H) and had little effect on R(A) in the first 2 months, but decreased both R(H) and R(A) during the remainder of the treatment and post-treatment years. Clipping of above-ground biomass stimulated the warming effect on R(H) but not on R(A). Overall, warming decreased R(A) and R(H) significantly, by 28.9% and 24.9%, respectively, during the treatment year and by 27.3% and 33.3%, respectively, during the post-treatment year, largely as a result of decreased canopy greenness and biomass. *Lagged effects of climate anomalies on soil respiration and its components are important in assessing terrestrial carbon cycle feedbacks to climate warming.

Competing effects of soil fertility and toxicity on tropical greening.

Tropical forests are expected to green up with increasing atmospheric CO2 concentrations, but primary productivity may be limited by soil nutrient availability. However, rarely have canopy-scale measurements been assessed against soil measurements in the tropics. Here, we sought to assess remotely sensed canopy greenness against steep soil nutrient gradients across 50 1-ha mature forest plots in Panama. Contrary to expectations, increases in in situ extractable soil phosphorus (P) and base cations (K, Mg) corresponded to declines in remotely sensed mean annual canopy greenness (r2 = 0.77-0.85; p < 0.1), controlling for precipitation. The reason for this inverse relationship appears to be that litterfall also increased with increasing soil P and cation availability (r2 = 0.88-0.98; p < 0.1), resulting in a decline in greenness with increasing annual litterfall (r2 = 0.94; p < 0.1). As such, greater soil nutrient availability corresponded to greater leaf turnover, resulting in decreased greenness. However, these decreases in greenness with increasing soil P and cations were countered by increases in greenness with increasing soil nitrogen (N) (r2 = 0.14; p < 0.1), which had no significant relationship with litterfall, likely reflecting a direct effect of soil N on leaf chlorophyll content, but not on litterfall rates. In addition, greenness increased with extractable soil aluminum (Al) (r2 = 0.97; p < 0.1), but Al had no significant relationship with litterfall, suggesting a physiological adaptation of plants to high levels of toxic metals. Thus, spatial gradients in canopy greenness are not necessarily positive indicators of soil nutrient scarcity. Using a novel remote sensing index of canopy greenness limitation, we assessed how observed greenness compares with potential greenness. We found a strong relationship with soil N only (r2 = 0.65; p < 0.1), suggesting that tropical canopy greenness in Panama is predominantly limited by soil N, even if plant productivity (e.g., litterfall) responds to rock-derived nutrients. Moreover, greenness limitation was also significantly correlated with fine root biomass and soil carbon stocks (r2 = 0.62-0.71; p < 0.1), suggesting a feedback from soil N to canopy greenness to soil carbon storage. Overall, these data point to the potential utility of a remote sensing product for assessing belowground properties in tropical ecosystems.

Mapping functional diversity from remotely sensed morphological and physiological forest traits.

Assessing functional diversity from space can help predict productivity and stability of forest ecosystems at global scale using biodiversity-ecosystem functioning relationships. We present a new spatially continuous method to map regional patterns of tree functional diversity using combined laser scanning and imaging spectroscopy. The method does not require prior taxonomic information and integrates variation in plant functional traits between and within plant species. We compare our method with leaf-level field measurements and species-level plot inventory data and find reasonable agreement. Morphological and physiological diversity show consistent change with topography and soil, with low functional richness at a mountain ridge under specific environmental conditions. Overall, functional richness follows a logarithmic increase with area, whereas divergence and evenness are scale invariant. By mapping diversity at scales of individual trees to whole communities we demonstrate the potential of assessing functional diversity from space, providing a pathway only limited by technological advances and not by methodology.

Contrasting carbon cycle responses of the tropical continents to the 2015-2016 El Niño.

The 2015-2016 El Niño led to historically high temperatures and low precipitation over the tropics, while the growth rate of atmospheric carbon dioxide (CO2) was the largest on record. Here we quantify the response of tropical net biosphere exchange, gross primary production, biomass burning, and respiration to these climate anomalies by assimilating column CO2, solar-induced chlorophyll fluorescence, and carbon monoxide observations from multiple satellites. Relative to the 2011 La Niña, the pantropical biosphere released 2.5 ± 0.34 gigatons more carbon into the atmosphere in 2015, consisting of approximately even contributions from three tropical continents but dominated by diverse carbon exchange processes. The heterogeneity of the carbon-exchange processes indicated here challenges previous studies that suggested that a single dominant process determines carbon cycle interannual variability.

Spaceborne detection of localized carbon dioxide sources.

Spaceborne measurements by NASA's Orbiting Carbon Observatory-2 (OCO-2) at the kilometer scale reveal distinct structures of atmospheric carbon dioxide (CO2) caused by known anthropogenic and natural point sources. OCO-2 transects across the Los Angeles megacity (USA) show that anthropogenic CO2 enhancements peak over the urban core and decrease through suburban areas to rural background values more than ~100 kilometers away, varying seasonally from ~4.4 to 6.1 parts per million. A transect passing directly downwind of the persistent isolated natural CO2 plume from Yasur volcano (Vanuatu) shows a narrow filament of enhanced CO2 values (~3.4 parts per million), consistent with a CO2 point source emitting 41.6 kilotons per day. These examples highlight the potential of the OCO-2 sensor, with its unprecedented resolution and sensitivity, to detect localized natural and anthropogenic CO2 sources.

Modeled responses of terrestrial ecosystems to elevated atmospheric CO2: a comparison of simulations by the biogeochemistry models of the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP).

Although there is a great deal of information concerning responses to increases in atmospheric CO2 at the tissue and plant levels, there are substantially fewer studies that have investigated ecosystem-level responses in the context of integrated carbon, water, and nutrient cycles. Because our understanding of ecosystem responses to elevated CO2 is incomplete, modeling is a tool that can be used to investigate the role of plant and soil interactions in the response of terrestrial ecosystems to elevated CO2. In this study, we analyze the responses of net primary production (NPP) to doubled CO2 from 355 to 710 ppmv among three biogeochemistry models in the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP): BIOME-BGC (BioGeochemical Cycles), Century, and the Terrestrial Ecosystem Model (TEM). For the conterminous United States, doubled atmospheric CO2 causes NPP to increase by 5% in Century, 8% in TEM, and 11% in BIOME-BGC. Multiple regression analyses between the NPP response to doubled CO2 and the mean annual temperature and annual precipitation of biomes or grid cells indicate that there are negative relationships between precipitation and the response of NPP to doubled CO2 for all three models. In contrast, there are different relationships between temperature and the response of NPP to doubled CO2 for the three models: there is a negative relationship in the responses of BIOME-BGC, no relationship in the responses of Century, and a positive relationship in the responses of TEM. In BIOME-BGC, the NPP response to doubled CO2 is controlled by the change in transpiration associated with reduced leaf conductance to water vapor. This change affects soil water, then leaf area development and, finally, NPP. In Century, the response of NPP to doubled CO2 is controlled by changes in decomposition rates associated with increased soil moisture that results from reduced evapotranspiration. This change affects nitrogen availability for plants, which influences NPP. In TEM, the NPP response to doubled CO2 is controlled by increased carboxylation which is modified by canopy conductance and the degree to which nitrogen constraints cause down-regulation of photosynthesis. The implementation of these different mechanisms has consequences for the spatial pattern of NPP responses, and represents, in part, conceptual uncertainty about controls over NPP responses. Progress in reducing these uncertainties requires research focused at the ecosystem level to understand how interactions between the carbon, nitrogen, and water cycles influence the response of NPP to elevated atmospheric CO2.

Coupling between carbon cycling and climate in a high-elevation, subalpine forest: a model-data fusion analysis.

Fundamental questions exist about the effects of climate on terrestrial net ecosystem CO(2) exchange (NEE), despite a rapidly growing body of flux observations. One strategy to clarify ecosystem climate-carbon interactions is to partition NEE into its component fluxes, gross ecosystem CO(2) exchange (GEE) and ecosystem respiration (R (E)), and evaluate the responses to climate of each component flux. We separated observed NEE into optimized estimates of GEE and R (E) using an ecosystem process model combined with 6 years of continuous flux data from the Niwot Ridge AmeriFlux site. In order to gain further insight into the processes underlying NEE, we partitioned R (E) into its components: heterotrophic (R (H)) and autotrophic (R (A)) respiration. We were successful in separating GEE and R (E), but less successful in accurately partitioning R (E) into R (A) and R (H). Our failure in the latter was due to a lack of adequate contrasts in the assimilated data set to distinguish between R (A) and R (H). We performed most model runs at a twice-daily time step. Optimizing on daily-aggregated data severely degraded the model's ability to separate GEE and R (E). However, we gained little benefit from using a half-hourly time step. The model-data fusion showed that most of the interannual variability in NEE was due to variability in GEE, and not R (E). In contrast to several previous studies in other ecosystems, we found that longer growing seasons at Niwot Ridge were correlated with less net CO(2) uptake, due to a decrease of available snow-melt water during the late springtime photosynthetic period. Warmer springtime temperatures resulted in increased net CO(2) uptake only if adequate moisture was available; when warmer springtime conditions led into mid-summer drought, the annual net uptake declined.