Terrestrial Carbon Cycle Models Research Articles

Quantification of biomass availability for wood harvesting and storage in the continental United States with a carbon cycle model

BackgroundWood Harvesting and Storage (WHS) is a form of Biomass Carbon Removal and Storage (BiCRS) that utilizes a combined natural and engineered process to harvest woody biomass and put it into long term storage, most frequently in the form of subterranean burial. This paper aims to quantify the availability of woody biomass for the purposes of WHS in the continental United States using a carbon cycle modeling approach. Using a regional version of the VEGAS terrestrial carbon cycle model at 10 km resolution, this paper calculates the annual woody net primary production in the continental United States. It then applies a series of constraints to exclude woody biomass that is unavailable for WHS. These constraints include fine woody biomass, current land use, current wood utilization, land conservation, and topographical limitations. These results were then split into state by state and regional totals.ResultsIn total, the model projects the continental United States could produce 1,274 MtCO2e (CO2 equivalent) worth of coarse woody biomass annually in a scenario with no anthropogenic land use or constraints. In a scenario with anthropogenic land use and constraints on wood availability, the model projects that 415 MtCO2e of coarse woody biomass is available for WHS annually. This is enough to offset 8.5% of the United States’ 2020 greenhouse gas emissions. Of this potential, 20 MtCO2e is from the Pacific region, 77 MtCO2e is from the Western Interior, 91 MtCO2e is from the Northeast region, and 228 MtCO2e is from the Southeast region.ConclusionThere is enough coarse woody biomass available in the continental United States to make WHS a viable form of carbon removal and storage in the country. There is coarse woody biomass available across the continental United States. All four primary regions analyzed have enough coarse woody biomass available to justify investment in WHS projects.

Integrating Tide‐Driven Wetland Soil Redox and Biogeochemical Interactions Into a Land Surface Model

AbstractRedox processes, aqueous and solid‐phase chemistry, and pH dynamics are key drivers of subsurface biogeochemical cycling and methanogenesis in terrestrial and wetland ecosystems but are typically not included in terrestrial carbon cycle models. These omissions may introduce errors when simulating systems where redox interactions and pH fluctuations are important, such as wetlands where saturation of soils can produce anoxic conditions and coastal systems where sulfate inputs from seawater can influence biogeochemistry. Integrating cycling of redox‐sensitive elements could therefore allow models to better represent key elements of carbon cycling and greenhouse gas production. We describe a model framework that couples the Energy Exascale Earth System Model (E3SM) Land Model (ELM) with PFLOTRAN biogeochemistry, allowing geochemical processes and redox interactions to be integrated with land surface model simulations. We implemented a reaction network including aerobic decomposition, fermentation, sulfate reduction, sulfide oxidation, methanogenesis, and methanotrophy as well as pH dynamics along with iron oxide and iron sulfide mineral precipitation and dissolution. We simulated biogeochemical cycling in tidal wetlands subject to either saltwater or freshwater inputs driven by tidal hydrological dynamics. In simulations with saltwater tidal inputs, sulfate reduction led to accumulation of sulfide, higher dissolved inorganic carbon concentrations, lower dissolved organic carbon concentrations, and lower methane emissions than simulations with freshwater tidal inputs. Model simulations compared well with measured porewater concentrations and surface gas emissions from coastal wetlands in the Northeastern United States. These results demonstrate how simulating geochemical reaction networks can improve land surface model simulations of subsurface biogeochemistry and carbon cycling.

Temporal Error Correlations in a Terrestrial Carbon Cycle Model Derived by Comparison to Carbon Dioxide Eddy Covariance Flux Tower Measurements

AbstractAtmospheric CO2 flux inversions require as input an estimate of spatial and temporal correlations of errors in their estimate of the prior mean. Some previous studies have used the differences in CO2 daily average flux estimates produced by terrestrial carbon cycle models and eddy covariance measurements to constrain the flux error correlations. Since inversions are starting to resolve the daily cycle, we set out to examine the correlations at sub‐daily time scales, as well as the correlations across years. To this end, we examine the autocorrelations in the difference between net ecosystem‐atmosphere exchange measurements from 75 AmeriFlux towers and temporally downscaled high‐spatial‐resolution flux estimates from the Carnegie‐Ames‐Stanford Approach (CASA) terrestrial carbon cycle model. We find that the daily cycle is prominent in these hourly autocorrelations and that these autocorrelations persist across years. We propose a family of functions to model these temporal correlations in atmospheric inversions, and use cross validation to determine which of the correlation functions best fits autocorrelation data from towers not in the training set. Correlation functions with a component that attempts to model the daily cycle in the differences match correlations from other towers better than those without. Those models that reproduce the same correlation structures at 1‐year intervals while modulating the amplitudes of the correlations between those intervals improve the fit still further.

Crucial roles of the optimal time-scale of water condition on grassland biomass estimation on Qinghai–Tibet Plateau

The effect of the time-scale of water conditions on vegetation productivity has been widely studied by the academic community. However, the relationship between the time-scale of water conditions and the vegetation growth rhythm and the effect of this relationship on vegetation biomass estimation have rarely been discussed. Here, we analyzed the occurrence times of major phenological events on alpine grasslands using the widely distributed “site-dominant species” dataset and set a series of time-scales for accumulated precipitation and standardized precipitation evapotranspiration index based on phenological information. Then, we combined large-scale aboveground/belowground biomass datasets to evaluate the role of the optimal time-scale for water conditions in aboveground/belowground biomass estimation. The results showed that (1) the optimal time-scale for water conditions with the greatest effects on aboveground biomass was on the month before the end of flowering or the onset of fruit maturity. The optimal time-scale for water condition effects on belowground biomass was earlier and longer than that for the aboveground biomass. The optimal time-scales for accumulated precipitation and standardized precipitation evapotranspiration index effects on belowground biomass were at five months before the end of flowering or the beginning of fruit ripening and the three months before the first flowering, respectively. (2) The aboveground and belowground biomass were underestimated by 11 % and 9 %, respectively, when the water conditions at the optimal time-scales were ignored. (3) The interannual variability in aboveground/belowground biomass was more effectively captured by considering the optimal time-scales of water conditions, especially in water-restricted areas. Overall, this study indicated that terrestrial carbon cycle models should incorporate information on the lag-effects of the water conditions in previous periods. In the future, increasing the number of belowground biomass observations and conducting monthly belowground biomass monitoring sooner will be key to revealing the mechanisms of the belowground biomass response to climate change.

Improved process representation of leaf phenology significantly shifts climate sensitivity of ecosystem carbon balance

Abstract. Terrestrial carbon cycle models are routinely used to determine the response of the land carbon sink under expected future climate change, yet these predictions remain highly uncertain. Increasing the realism of processes in these models may help with predictive skill, but any such addition should be confronted with observations and evaluated in the context of the aggregate behavior of the carbon cycle. Here, two formulations for leaf area index (LAI) phenology are coupled to the same terrestrial biosphere model: one is climate agnostic, and the other incorporates direct environmental controls on both timing and growth. Each model is calibrated simultaneously to observations of LAI, net ecosystem exchange (NEE), and biomass using the CARbon DAta-MOdel fraMework (CARDAMOM) and validated against withheld data, including eddy covariance estimates of gross primary productivity (GPP) and ecosystem respiration (Re) across six ecosystems from the tropics to high latitudes. Both model formulations show similar predictive skill for LAI and NEE. However, with the addition of direct environmental controls on LAI, the integrated model explains 22 % more variability in GPP and Re and reduces biases in these fluxes by 58 % and 77 %, respectively, while also predicting more realistic annual litterfall rates due to changes in carbon allocation and turnover. We extend this analysis to evaluate the inferred climate sensitivity of LAI and NEE with the new model and show that the added complexity shifts the sign, magnitude, and seasonality of NEE sensitivity to precipitation and temperature. This highlights the benefit of process complexity when inferring underlying processes from Earth observations and representing the climate response of the terrestrial carbon cycle.

Substantial uncertainties in global soil organic carbon simulated by multiple terrestrial carbon cycle models

AbstractSoil organic carbon (SOC) is critical to the terrestrial ecosystem carbon cycle and global climate change. The carbon cycle model coupled with microbial processes can improve the projection of SOC. However, it remains unclear whether microbial models are superior to multiple terrestrial carbon cycle models and how large the simulation uncertainties of SOC are. Therefore, we simulate the spatial patterns of global SOC by the MIMICS (explicit nonlinear microbial carbon cycle model) and the DCC (implicit linear carbon cycle model), compare the SOC with that of the CMIP6 MME (multi‐model ensemble) and the observation to obtain the uncertainties of SOC, and analyze the sensitivity of the parameters in the two models. The results show that the SOC simulated by the MIMICS is 1615.4 ± 54.3 PgC, which is higher than that of the DCC (668.5 ± 102.1 PgC), the CMIP6 MME (1443.7 ± 795.8 PgC), and the observation (1519.1 PgC). The SOC of the DCC and MIMICS are regulated by NPP, and that of DCC is more sensitive to climate, while that of MIMICS is mainly influenced by the Michaelis–Menten equation and microbial carbon use efficiency. We reveal the spatial patterns of uncertainty in global SOC simulated by the explicit and implicit microbial processes of multiple terrestrial carbon cycle models. Unfortunately, neither the DCC, the MIMICS nor the CMIP6 MME simulate SOC satisfactorily, especially in the high latitudes of the Northern Hemisphere, and the simulation of SOC in this region needs to be improved.

Advances in influencing mechanism and model of total climatic production factors of plant phenology change

<p indent="0mm">Phenology is an important indicator of climate change. It is not only the key to vegetation dynamic simulation, but also directly affects the simulation accuracy of terrestrial carbon cycle and atmospheric general circulation model. This paper summarizes the research status of plant phenology from two aspects: phenological change mechanism and phenological simulation model, and analyzes the research trend of phenology. It points out that most of the current phenological studies only consider the impact of a single climate factor or a few climate factors, and there are still few studies involved in the interaction of multiple climate factors, let alone the interaction of total climate production factors determining plant phenology. Due to the limitation of the understanding of phenological change mechanism and the study of species, the existing phenological models cannot reflect the authenticity of vegetation growth, nor can they effectively simulate the phenological period. The paper points out that it is essential to study the relationship between phenology and total climatic production factors in order to comprehensively understand the mechanism of phenological change. Based on the fact that plant photosynthesis is the most important phenological influencing factor, this paper puts forward the viewpoint of using climate production potential as the driving factor of plant phenological change. Climate production potential not only reflects the comprehensive effect of climate and environmental factors on plant production and their consistency of these factors in the whole process of plant growth, but also includes the interaction between biological factors and environmental factors and the impact of extreme weather and climate events, which is able to realize the accurate simulation of phenology. The future phenological research directions proposed in this paper will help enrich phenological knowledge and improve the simulation accuracy of land carbon cycle model and atmospheric circulation model.

The History of Cenozoic Carbonate Flux in the Atlantic Ocean Constrained by Multiple Regional Carbonate Compensation Depth Reconstructions

AbstractThe Atlantic is the only ocean basin almost entirely surrounded by passive margins, and a major global long‐term sink of carbonate carbon that has evaded subduction. Quantifying the history of carbonate accumulation in the Atlantic has been limited by the absence of well‐defined regional carbonate compensation depth (CCD) models. We determine the CCD for the northern North Atlantic, central North Atlantic, and South Atlantic, and use these reconstructions to compute the carbonate carbon mass and carbonate carbon flux in a tectonic framework at 0.5 m.y. intervals since 66 Ma. We find that the total carbonate carbon mass of the Atlantic has grown 2.5‐fold from ∼1,500 Mt at 66 Ma to ∼3,800 Mt at present day. The overall Cenozoic increase in carbonate carbon flux toward the present day is punctuated by “carbonate crash” phases in the mid‐Eocene at ∼44–38 Ma and in the mid‐late Miocene at ∼19–8 Ma. During these times the flux decreases from ∼45 to ∼25 Mt C/yr, likely caused by carbonate dissolution and reductions in productivity. Reduced carbonate carbon flux in the mid‐Eocene also coincides with reduced calcification rates of small coccolithophores previously observed offshore Africa. After ∼8 Ma the carbonate carbon flux rises to a Cenozoic maximum of ∼75 Mt C/yr at ∼3 Ma, possibly driven by enhanced flux of nutrients into the ocean. Our CCD curves and the resulting carbonate accumulation history are useful for calibrating ocean chemistry models, and constraining global terrestrial weathering rates, climate perturbations, and carbon cycle models.

Uncertainties of soil organic carbon stock estimation caused by paleoclimate and human footprint on the Qinghai Plateau

BackgroundQuantifying the stock of soil organic carbon (SOC) and evaluating its potential impact factors is important to evaluating global climate change. Human disturbances and past climate are known to influence the rates of carbon fixation, soil physiochemical properties, soil microbial diversity and plant functional traits, which ultimately affect the current SOC storage. However, whether and how the paleoclimate and human disturbances affect the distribution of SOC storage on the high-altitude Tibetan Plateau remain largely unknown. Here, we took the Qinghai Plateau, the main component of the Tibetan Plateau, as our study region and applied three machine learning models (random forest, gradient boosting machine and support vector machine) to estimate the spatial and vertical distributions of the SOC stock and then evaluated the effects of the paleoclimate during the Last Glacial Maximum and the mid-Holocene periods as well as the human footprint on SOC stock at 0 to 200 cm depth by synthesizing 827 soil observations and 71 environmental factors.ResultsOur results indicate that the vegetation and modern climate are the determinant factors of SOC stocks, while paleoclimate (i.e., paleotemperature and paleoprecipitation) is more important than modern temperature, modern precipitation and the human footprint in shaping current SOC stock distributions. Specifically, the SOC stock was deeply underestimated in near natural ecosystems and overestimated in the strongly human disturbance ecosystems if the model did not consider the paleoclimate. Overall, the total SOC stock of the Qinghai Plateau was underestimated by 4.69%, 12.25% and 6.67% at depths of 0 to 100 cm, 100 to 200 cm and 0 to 200 cm, respectively. In addition, the human footprint had a weak influence on the distributions of the SOC stock. We finally estimated that the total and mean SOC stock at 200 cm depth by including the paleoclimate effects was 11.36 Pg C and 16.31 kg C m−2, respectively, and nearly 40% SOC was distributed in the top 30 cm.ConclusionThe paleoclimate is relatively important for the accurate modeling of current SOC stocks. Overall, our study provides a benchmark for predicting SOC stock patterns at depth and emphasizes that terrestrial carbon cycle models should incorporate information on how the paleoclimate has influenced SOC stocks.

Definitions and methods to estimate regional land carbon fluxes for the second phase of the REgional Carbon Cycle Assessment and Processes Project (RECCAP-2)

Abstract. Regional land carbon budgets provide insights into the spatial distribution of the land uptake of atmospheric carbon dioxide and can be used to evaluate carbon cycle models and to define baselines for land-based additional mitigation efforts. The scientific community has been involved in providing observation-based estimates of regional carbon budgets either by downscaling atmospheric CO2 observations into surface fluxes with atmospheric inversions, by using inventories of carbon stock changes in terrestrial ecosystems, by upscaling local field observations such as flux towers with gridded climate and remote sensing fields, or by integrating data-driven or process-oriented terrestrial carbon cycle models. The first coordinated attempt to collect regional carbon budgets for nine regions covering the entire globe in the RECCAP-1 project has delivered estimates for the decade 2000–2009, but these budgets were not comparable between regions due to different definitions and component fluxes being reported or omitted. The recent recognition of lateral fluxes of carbon by human activities and rivers that connect CO2 uptake in one area with its release in another also requires better definitions and protocols to reach harmonized regional budgets that can be summed up to a globe scale and compared with the atmospheric CO2 growth rate and inversion results. In this study, using the international initiative RECCAP-2 coordinated by the Global Carbon Project, which aims to be an update to regional carbon budgets over the last 2 decades based on observations for 10 regions covering the globe with a better harmonization than the precursor project, we provide recommendations for using atmospheric inversion results to match bottom-up carbon accounting and models, and we define the different component fluxes of the net land atmosphere carbon exchange that should be reported by each research group in charge of each region. Special attention is given to lateral fluxes, inland water fluxes, and land use fluxes.

Exploring the instability of the relationship between maximum potential electron transport rate and maximum carboxylation rate in cool-temperate deciduous forests

A clear understanding of the relationship between two photosynthetic capacity parameters, maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax), is expected to reveal plant photosynthetic physiology strategies and is critical in determining the carbon and water cycle in the terrestrial biosphere. Increasing evidence in recent suggests the relationship is unstable in spatiotemporal contexts, casting a doubt on deriving Jmax solely from a single linear function of Vcmax as in most gas exchange models do. In this study, we have examined the instability of the relationship in terms of seasonal and interannual courses (from 2006 to 2014) as well as at different altitudes (550, 900, and 1500 m a.s.l.) and in different leaf types (sunlit and shaded) of the only dominant species of Fagus crenata Blume in typical cool temperate forests in Japan based on a long-term field dataset. Our results clearly indicated that there is not only remarkable seasonal variability in the two parameters themselves but also large variations in their ratios. The slope parameter of the best fitted single linear function for estimating Jmax from Vcmax varied also in different seasons and different years and different leaf types, as well as at different sites. Further exploration of the slope parameter with internal leaf traits and external climatic drivers revealed that the leaf chlorophyll content had a significant correlation with the slope parameter (R2 = 0.72, RMSE = 0.08, RPD = 2.00) but the impacts of climatic drivers were rather versatile. Our findings should hence advance understanding of the dynamics of photosynthetic capacity and the instability of the Jmax–Vcmax relationship, which should be considered explicitly in the future in order to substantially improve model predictions in terrestrial carbon cycle models.

Temporally variable parameters simulate asymmetrical interannual variation of aboveground and belowground carbon pools in an alpine meadow

Exploring interannual variation (IAV) of terrestrial carbon cycle is crucial for understanding the climate-carbon cycle feedback. However, model simulations of IAV in carbon pools are highly sensitive to parameterization. Although researches indicate that parameters vary as environment changes, most terrestrial carbon cycle models apply time-invariant parameters, leading to uncertainty in reproducing IAV. We compared the IAV simulation accuracy of carbon pools between constant and temporally variable parameters, generated by a piecewise model-data fusion framework based on long-term multi-observations in an alpine meadow over the Tibetan Plateau. Piecewise assimilation showed that key parameters involved in photosynthesis and allocation processes exhibited significant temporal variation under warming and nitrogen deposition. Specifically, day length threshold for leaf senescence was postponed, while allocation to autotrophic respiration, allocation to root, leaf nitrogen content, and nitrogen use efficiency increased. Compared with constant parameters, temporally variable parameters captured the observed asymmetrical response of aboveground and belowground carbon pools to climate fluctuations, improved modeled magnitude of IAV in carbon pools by 34% on average, and advanced carbon sink estimation accuracy by 43.15%. Therefore, fitting parameter dynamics to climatic conditions can provide a benchmark for future simulation in the Tibetan Plateau. Our research suggests that IAV in different carbon pools is crucial in determining carbon sinks. Temporal variations of parameters present how ecosystems adapt to a changing environment and should be incorporated to terrestrial carbon cycle models to reduce uncertainty in model prediction.

Participatory intercomparison strategy for terrestrial carbon cycle models based on a service-oriented architecture

Terrestrial carbon cycle models are important tools for simulating carbon exchange; however, there are still significant uncertainties in the simulation results of different models. Model-data intercomparison has therefore been widely recognized as an effective approach for evaluating model performance and acquiring a more reliable understanding of the terrestrial carbon cycle. Although considerable efforts have been made in establishing model intercomparison projects (MIPs), existing MIPs still experience limitations in supporting teams of researchers working collaboratively online and ensuring the reproducibility of model experiments. This article proposes a participatory intercomparison strategy based on a service-oriented architecture (SOA), which aims to offer a web-based platform for researchers to construct participatory intercomparison (PIC) projects. The three fundamental components of a PIC project are the PIC topic, PIC instance, and PIC task. The PIC topic is used to help participants co-design backgrounds, goals, and comparison protocols. The PIC instance is used to help participants provide models, observations and benchmark data as reusable services. The PIC task is used to help participants formulate comparison workflows and acquire customized comparison results. Using the proposed strategy, a PIC project can be easily created and maintained by a group of geographically distributed participants. Reusability of models and data can be achieved through the proposed service-based wrapping method, and the reproducibility of model-data comparison experiments can be achieved through the workflow-based comparison method. A web platform named “P-MIP” was implemented and tested that helps researchers collaborate online, and a demonstrative PIC project was constructed to verify the feasibility and capability of the proposed strategy.

Spatiotemporal Simulation of Net Ecosystem Productivity and Its Response to Climate Change in Subtropical Forests

Subtropical forests have great potential as carbon sinks; however, the relationship between net ecosystem productivity (NEP) and climate change is still unclear. This study took Zhejiang Province, a subtropical region, as an example. Based on remote sensing classification data of forest resources, the integrated terrestrial ecosystem carbon cycle (InTEC) model was used to simulate the spatiotemporal dynamics of the forest NEP in Zhejiang Province during 1985–2015 and analyze its response to meteorological factors such as temperature, precipitation, relative humidity, and radiation. Three patterns emerged: (1) The optimized InTEC model can better simulate the forest NEP in Zhejiang Province, and the correlation coefficient between the simulated NEP and observed NEP was up to 0.75. (2) From 1985 to 2015, the increase in the total NEP was rapid, with an average annual growth rate of 1.52 Tg·C·yr−1. During 1985–1988, the forests in Zhejiang Province were carbon sources. After 1988, the forests turned into carbon sinks and this continued to increase. During 2000–2015, more than 97% of the forests in Zhejiang Province were carbon sinks. The total NEP reached 32.02 Tg·C·yr−1, and the annual mean NEP increased to 441.91 gC·m−2·yr−1. The carbon sequestration capacity of forests in the east and southwest of Zhejiang Province is higher than that in the northeast of Zhejiang Province. (3) From 2000 to 2015, there was an extremely significant correlation between forest NEP and precipitation, with a correlation coefficient of 0.85. Simultaneously, the forest NEP showed a negative correlation with temperature and radiation, with a correlation coefficient of −0.56 for both, and the forest NEP was slightly negatively correlated with relative humidity. The relative contribution rates of temperature, precipitation, relative humidity, and radiation data to NEP showed that the contribution of precipitation to NEP is the largest, reaching 61%, followed by temperature and radiation at 18% and 17%, respectively. The relative contribution rate of relative humidity is the smallest at only 4%. During the period of 1985–1999, due to significant man-made disturbances, the NEP had a weak correlation with temperature, precipitation, relative humidity, and radiation. The results of this study are important for addressing climate change and illustrating the response mechanism between subtropical forest NEP and climate change.

Interpretation of vegetation phenology changes using daytime and night-time temperatures across the Yellow River Basin, China

Vegetation phenology is highly sensitive to climate change and has a crucial effect on the carbon balance. Prior studies have mainly investigated the effects of mean temperature and precipitation on phenology. The asymmetric and opposing response of phenology to daytime and night-time temperature remains largely unknown. Using the satellite phenology derived from GIMMS NDVI3g datasets dating back to the 1980s, we show that significantly advanced start of the season (SOS), delayed end of the season (EOS) and prolonged length of growing season (LEN) (P < 0.05) has been taking place in the Yellow River Basin in China. The extension of LEN was more attributed to the advance of SOS than a delayed EOS. The daytime Tmax and night-time Tmin had opposite effects on the timing of SOS, MOS, and EOS in 63.1%,40.0%, and 53.5% of the pixels of the study area, respectively. If higher Tmax leads to an earlier or later transition date, an increase in Tmin systematically leads to an opposite effect. These opposite effects were obvious in SOS of 70.5%, 66.2%, and 70.6% of shrubland, grassland, and crop fields, respectively. For EOS, the opposing effects accounting for 58.2%,60.2%, and 54.5% of forest land, shrubland, and grassland, respectively. These results reveal different impacts of climate change on the daytime and night-time carbon cycle in terrestrial ecosystems, and such impacts vary with the land surface type. Knowledge of these opposing responses of phenology to daytime and night-time warming may help to understand the feedback of terrestrial ecosystem structure and function to climate change, thus to improve the existing terrestrial ecosystem carbon cycle model, which is of great significance for climate change and ecology research.

Widespread inhibition of daytime ecosystem respiration.

The global land surface absorbs about a third of anthropogenic emissions each year, due to the difference between two key processes: ecosystem photosynthesis and respiration. Despite the importance of these two processes, it is not possible to measure either at the ecosystem scale during daytime. Eddy-covariance measurements are widely used as the closest ‘quasi-direct’ ecosystem-scale observation from which to estimate ecosystem photosynthesis and respiration. Recent research, however, suggests that current estimates may be biased by up to 25%, due to a previously unaccounted-for process: the inhibition of leaf respiration in the light. Yet the extent of inhibition remains debated, and implications for estimates of ecosystem-scale respiration and photosynthesis remain unquantified. Here, we quantify an apparent inhibition of daytime ecosystem respiration across the global FLUXNET eddy-covariance network, and identify a pervasive influence that varies by season and ecosystem type. We develop partitioning methods that can detect an apparent ecosystem-scale inhibition of daytime respiration and find that diurnal patterns of ecosystem respiration might be markedly different than previously thought. The results call for the reevaluation of global terrestrial carbon cycle models, and also suggest that current global estimates of photosynthesis and respiration may be biased, some on the order of magnitude of anthropogenic fossil fuel emissions.

Widespread seasonal compensation effects of spring warming on northern plant productivity.

Climate change is shifting the phenological cycles of plants1, thereby altering the functioning of ecosystems, which in turn induces feedbacks to the climate system2. In northern (north of 30°N) ecosystems, warmer springs lead generally to an earlier onset of the growing season3,4 and increased ecosystem productivity early in the season5. In situ6 and regional7-9 studies also provide evidence for lagged effects of spring warmth on plant productivity during the subsequent summer and autumn. However, our current understanding of these lagged effects, including their direction (beneficial or adverse) and geographic distribution, is still very limited. Here we analyse satellite, field-based and modelled data for the period 1982-2011 and show that there are widespread and contrasting lagged productivity responses to spring warmth across northern ecosystems. On the basis of the observational data, we find that roughly 15 per cent of the total study area of about 41 million square kilometres exhibits adverse lagged effects and that roughly 5 per cent of the total study area exhibits beneficial lagged effects. By contrast, current-generation terrestrial carbon-cycle models predict much lower areal fractions of adverse lagged effects (ranging from 1 to 14 per cent) and much higher areal fractions of beneficial lagged effects (ranging from 9 to 54 per cent). We find that elevation and seasonal precipitation patterns largely dictate the geographic pattern and direction of the lagged effects. Inadequate consideration in current models of the effects of the seasonal build-up of water stress on seasonal vegetation growth may therefore be able to explain the differences that we found between our observation-constrained estimates and the model-constrained estimates of lagged effects associated with spring warming. Overall, our results suggest that for many northern ecosystems the benefits of warmer springs on growing-season ecosystem productivity are effectively compensated for by the accumulation of seasonal water deficits, despite the fact that northern ecosystems are thought to be largely temperature- and radiation-limited10.

Multi-scale dynamics and environmental controls on net ecosystem CO2 exchange over a temperate semiarid shrubland

Our understanding of the variability in net ecosystem CO2 exchange (NEE) across different timescales is still limited, as terrestrial carbon cycle models often mismatch data at multiple timescales. Especially, multi-scale environmental controls on NEE are less well understood in semiarid shrublands than in mesic ecosystems. We collected eddy-covariance measurements of NEE over five years (2012–2016) from a semiarid shrubland in northern China, and then used continuous wavelet transform (CWT), wavelet coherence (WTC), and partial wavelet coherence (PWC) analysis to investigate how photosynthetically active radiation (PAR), air temperature (Ta), vapor pressure deficit (VPD), and soil water content (at 30-cm depth, SWC30) modulate the variability of NEE in the time-frequency domain. CWT revealed that NEE not only had clear daily and annual periodicities, but also oscillated strongly at intermediate scales (days, weeks to months). At the 1-day period, NEE showed significant WTC with PAR, Ta, and VPD during growing seasons, with NEE leading PAR by about 1.0 h and leading Ta and VPD by over 3.5 h. At the 1-year period, NEE also showed strong WTC with PAR, Ta, and VPD throughout time, with NEE lagging Ta by 19 days and lagging PAR and VPD by about 40 days. At intermediate periods, non-continuous areas of significant WTC were observed between NEE and environmental factors, notably between NEE and PAR at 4–32-day periods during growing seasons. PWC revealed a greater modulating effect of PAR than that of Ta on NEE at intermediate periods. However, the intermediate-scale PAR effects were largely weakened during spring or summer drought periods (i.e., low SWC30). In addition, drought events were identified as hotspots of WTC between NEE and SWC30 at monthly or longer timescales. This study highlights the need for a multi-scale approach to understanding the temporal dynamics of NEE. Modeling efforts should take into account these multi-temporal correlations between NEE and environmental factors in order to improve model-data agreement across timescales.

Sources of Uncertainty in Modeled Land Carbon Storage within and across Three MIPs: Diagnosis with Three New Techniques

Terrestrial carbon cycle models have incorporated increasingly more processes as a means to achieve more-realistic representations of ecosystem carbon cycling. Despite this, there are large across-model variations in the simulation and projection of carbon cycling. Several model intercomparison projects (MIPs), for example, the fifth phase of the Coupled Model Intercomparison Project (CMIP5) (historical simulations), Trends in Net Land–Atmosphere Carbon Exchange (TRENDY), and Multiscale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), have sought to understand intermodel differences. In this study, the authors developed a suite of new techniques to conduct post-MIP analysis to gain insights into uncertainty sources across 25 models in the three MIPs. First, terrestrial carbon storage dynamics were characterized by a three-dimensional (3D) model output space with coordinates of carbon residence time, net primary productivity (NPP), and carbon storage potential. The latter represents the potential of an ecosystem to lose or gain carbon. This space can be used to measure how and why model output differs. Models with a nitrogen cycle generally exhibit lower annual NPP in comparison with other models, and mostly negative carbon storage potential. Second, a transient traceability framework was used to decompose any given carbon cycle model into traceable components and identify the sources of model differences. The carbon residence time (or NPP) was traced to baseline carbon residence time (or baseline NPP related to the maximum carbon input), environmental scalars, and climate forcing. Third, by applying a variance decomposition method, the authors show that the intermodel differences in carbon storage can be mainly attributed to the baseline carbon residence time and baseline NPP (&gt;90% in the three MIPs). The three techniques developed in this study offer a novel approach to gain more insight from existing MIPs and can point out directions for future MIPs. Since this study is conducted at the global scale for an overview on intermodel differences, future studies should focus more on regional analysis to identify the sources of uncertainties and improve models at the specified mechanism level.

Disentangling the mechanisms behind winter snow impact on vegetation activity in northern ecosystems.

Although seasonal snow is recognized as an important component in the global climate system, the ability of snow to affect plant production remains an important unknown for assessing climate change impacts on vegetation dynamics at high-latitude ecosystems. Here, we compile data on satellite observation of vegetation greenness and spring onset date, satellite-based soil moisture, passive microwave snow water equivalent (SWE) and climate data to show that winter SWE can significantly influence vegetation greenness during the early growing season (the period between spring onset date and peak photosynthesis timing) over nearly one-fifth of the land surface in the region north of 30 degrees, but the magnitude and sign of correlation exhibits large spatial heterogeneity. We then apply an assembled path model to disentangle the two main processes (via changing early growing-season soil moisture, and via changing the growth period) in controlling the impact of winter SWE on vegetation greenness, and suggest that the "moisture" and "growth period" effect, to a larger extent, result in positive and negative snow-productivity associations, respectively. The magnitude and sign of snow-productivity association is then dependent upon the relative dominance of these two processes, with the "moisture" effect and positive association predominating in Central, western North America and Greater Himalaya, and the "growth period" effect and negative association in Central Europe. We also indicate that current state-of-the-art models in general reproduce satellite-based snow-productivity relationship in the region north of 30 degrees, and do a relatively better job of capturing the "moisture" effect than the "growth period" effect. Our results therefore work towards an improved understanding of winter snow impact on vegetation greenness in northern ecosystems, and provide a mechanistic basis for more realistic terrestrial carbon cycle models that consider the impacts of winter snow processes.