Comment on gmd-2022-96

<strong class="journal-contentHeaderColor">Abstract.</strong> 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 (R_e), 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 R_e, 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 in representing the climate response of the terrestrial carbon cycle.

Post-fire co-stimulation of gross primary production and ecosystem respiration in a meadow grassland on the Tibetan Plateau

Boreal forests in the northern hemisphere provide important sinks for storing carbon dioxide (CO2). However, the size and distribution of these sinks remain uncertain. In particular, many remote-sensing models show a strong bias in the simulation of carbon fluxes for evergreen needleleaf forest. The objective of this study is to improve these predictive models for accurately quantifying temporal changes in the net ecosystem exchange (NEE) of conifer-dominated forest solely based on satellite remote sensing, including the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra daytime land-surface temperature (LST), night-time LST′, enhanced vegetation index (EVI), land–surface water index (LSWI), fraction of absorbed photosynthetically active radiation (FPAR), and leaf area index (LAI). Considering that the component fluxes, gross primary production (GPP), and ecosystem respiration (Re), are strongly influenced by vegetation phenology, seasonality information was extracted from time-series MODIS EVI data based on non-linear least-squares fits of asymmetric Gaussian model functions with a software package for analysing the time-series of satellite sensor data (TIMESAT). The results indicated that models directly incorporating phenological information failed to improve their performance for temperate deciduous forest. Instead, three methods to retrieve the component fluxes – GPP and Re – including direct estimates, models incorporating the phenological information, and models developed based on the threshold value (LST 273 K), were explored respectively. All methods improved NEE estimates markedly and models developed based on the threshold value performed best, and provided a future framework for accurate remote sensing of NEE in evergreen forest.

&amp;lt;p&amp;gt;The Taiga Plains ecozone in northwestern Canada is characterized by vast peat landscapes consisting of both mostly tree-less, permafrost-free and forested, permafrost-affected peat landscapes. In response to warming due to ongoing climate change, more frequent and severe wildfires and rapid permafrost thaw affect landscape composition, structure and functioning, whereas more and more ice-rich permafrost peat plateaus transform into water-saturated thermokarst wetlands or lakes. Collectively, these three agents of change, namely warming, wildfire, and thermokarst, could turn these boreal peat landscape from atmospheric carbon and nitrogen sinks into sources with potentially positive climate system feedbacks. We studied net ecosystem exchange (NEE) and its two component fluxes, i.e., gross primary productivity (GPP) and ecosystem respiration (ER), from three sites with five eddy covariance towers near the southern limit of permafrost in western Canada. Around the southernmost site Lutose, both footprint areas around the two towers have completely burned in wildfires in 2007 and 2019, respectively. We hypothesized that these two subsites would act as net CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; sources, because of the recent disturbance history. This has been confirmed by preliminary results. The two other sites mainly differed in permafrost extent, ranging from sporadic (Scotty Creek) to discontinuous (Smith Creek), and in peat plateau-to-wetland ratio and corresponding forest cover (Scotty Creek &amp;lt; Smith Creek). Between the two sites Scotty Creek and Smith Creek, we hypothesized that the overall landscape GPP and ER will be higher at Scotty Creek compared to the northernmost site Smith Creek, due to both more abundant thermokarst wetlands and higher GPP and ER of the peat plateau areas at this more southern site. We further hypothesized that the effects of warming on GPP are greater than on ER and thus that the warmer Scotty Creek site is a greater net CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; sink. Contrary to expectations, preliminary results have shown that there is no difference in NEE between Scotty Creek and Smith Creek, whereas both, the overall landscape GPP and ER, are actually higher at Smith Creek. To identify differences in the NEE, GPP, and ER between their peat plateaus and thermokarst wetlands, respectively, we move forward by applying footprint analyses for Smith Creek and Scotty Creek. Through these analyses we will be able to shed light on how each of the drivers, i.e., warming, wildfire, and thermokarst, alters magnitude and direction in greenhouse gas fluxes from rapidly thawing boreal peat landscapes.&amp;lt;/p&amp;gt;

Spatiotemporal dynamics in assimilated-LAI phenology and its impact on subtropical bamboo forest productivity

Seasonal variations and driving mechanisms of CO2 fluxes over a winter-wheat and summer-maize rotation cropland in the North China plain

Abstract. Improving the accuracy of monitoring cropland CO2 exchange at heterogeneous spatial scales is of great importance for reducing spatial and temporal uncertainty in estimating terrestrial carbon (C) dynamics. In this study, an approach to estimate daily cropland C fluxes is developed and tested by combining time series of field-scale eddy covariance (EC) CO2 flux data and Sentinel-2 satellite-based vegetation indices (VIs) after appropriately accounting for the spatial alignment between the two time series datasets. The study was carried out for an agricultural field (118 ha) in the lowlands of northeastern Germany. The ability of different VIs to estimate daily net ecosystem exchange (NEE) and gross primary productivity (GPP) based on linear regression models was assessed. Most VIs showed high (&gt;0.9) and statistically significant (p&lt;0.001) correlations with GPP and NEE, although some VIs deviated from the seasonal pattern of CO2 exchange. By contrast, correlations between ecosystem respiration (Reco) and VIs were weak and not statistically significant, and no attempt was made to estimate Reco from VIs. Linear regression models explained generally more than 80 % and 70 % of the variability in NEE and GPP, respectively, with high variability among the individual VIs. The performance in estimating daily C fluxes varied among VIs depending on the C flux component (NEE or GPP) and observation period. Root mean square error (RMSE) values ranged from 1.35 g C m−2 d−1 using the green normalized difference vegetation index (GNDVI) for NEE to 5 g C m−2 d−1 using the simple ratio (SR) for GPP. This equated to an underestimated net C uptake of only 41 g C m−2 (18 %) and an overestimation of gross C uptake of 854 g C m−2 (73 %). Differences between the measured and estimated C fluxes were mainly explained by the diversion of the C flux and VI signal during winter when C uptake remained low, while VI values indicated an increased C uptake due to relatively high crop leaf area. Overall, the results exhibited similar error margins to mechanistic crop models. Thus, they indicated the suitability and expandability of the proposed approach for monitoring cropland C exchange with satellite-derived VIs.

Ecosystem process modeling is vital for a broad range of applications. It requires a key metric to characterize vegetation canopies: the leaf area index (LAI). Many land surface models utilize satellite-derived LAI for modeling ecosystem processes. The use of satellite-derived LAI constrains the models for predicting ecosystem processes when observational data are unavailable. Enriching prognostic models for predicting the LAI is favorable to land surface studies for forecasting vegetation-related processes. An attention-enhanced long and short memory (AELSTM) model was developed to predict the vegetation LAI time series based on climatic data. The AELSTM outperformed comparative machine learning models when assessed using satellite and flux tower data. The LAI predicted by AELSTM are temporally and spatially consistent with satellite-derived LAI. The R2 values attained 0.86–0.93 across biomes. AELSTM can be coupled with the land surface models to predict gross primary productivity. Our proposed model was demonstrated to be effective in predicting the LAI time series under different Shared Socioeconomic Pathways in CMIP6. Our study demonstrated that deep learning approaches are capable of modeling and characterizing the spatial and temporal patterns of key vegetation variables such as LAI. Moreover, the study has provided references for vegetation processes in land surface studies.

Based on the model–data comparison at the eddy-covariance observation sites from CarboEastAsia datasets, we report the current status of the terrestrial carbon cycle modeling in monsoon Asia. In order to assess the modeling performance and discuss future requirements for both modeling and observation efforts in Asia, we ran eight terrestrial biosphere models at 24 sites from 1901 to 2010. By analyzing the modeled carbon fluxes against the CarboEastAsia datasets, the strengths and weaknesses of terrestrial biosphere modeling over Asia were evaluated. In terms of pattern and magnitude, the carbon fluxes (i.e., gross primary productivity, ecosystem respiration, and net ecosystem exchange) at the temperate and boreal forest sites were simulated best, whereas the simulation results from the tropical forest, cropland, and disturbed sites were poor. The multi-model ensemble mean values showed lower root mean square errors and higher correlations, suggesting that composition of multiple terrestrial biosphere models would be preferable for terrestrial carbon budget assessments in Asia. These results indicate that the current model-based estimation of terrestrial carbon budget has large uncertainties, and future research should further refine the models to permit re-evaluation of the terrestrial carbon budget.

&amp;lt;p&amp;gt;Drought is not only a multiscale (e.g., temporal, spatial) but also a multidimensional (e.g., onset, offset, duration, frequency, magnitude, intensity) phenomenon, and ecosystem production and respiration may respond to each drought dimension differently. &amp;amp;#160;Although multiple reports exist in literature on the drought impact on ecosystem productivity, it remains unclear how each component of drought impacts ecosystem gross primary production (GPP), ecosystem respiration (R&amp;lt;sub&amp;gt;ECO&amp;lt;/sub&amp;gt;), and net ecosystem exchange (NEE) and how the different drought dimensions interacted with each other on their impacts. In this study, we conducted a comprehensive drought impact assessment on forest GPP, NEE, and R&amp;lt;sub&amp;gt;ECO&amp;lt;/sub&amp;gt; including all the drought dimensions using FLUXNET observations and multiple time-scales of Standardized Precipitation-Evapotranspiration Index (SPEI). Our results indicated that while most earlier drought studies focused on simultaneous and post-drought conditions, the cumulative drought impacts and drought timing are more significantly impacting forest carbon uptake than simultaneous drought severity. Temporal standardization based meteorological drought indices could be used to accurately reflect plant water stress if antecedent and cumulative drought conditions are considered.&amp;lt;/p&amp;gt;

High-elevation montane forests are disproportionately important to carbon sequestration in semiarid climates where low elevations are dry and characterized by low carbon density ecosystems. However, these ecosystems are increasingly threatened by climate change with seasonal implications for photosynthesis and forest growth. As a result, we leveraged eddy covariance data from six evergreen conifer forest sites in the semiarid western United States to extrapolate the status of carbon sequestration within a framework of projected warming and drying. At colder locations, the seasonal evolution of gross primary productivity (GPP) was characterized by a single broad maximum during the summer that corresponded to snow melt-derived moisture and a transition from winter dormancy to spring activity. Conversely, winter dormancy was transient at warmer locations, and GPP was responsive to both winter and summer precipitation such that two distinct GPP maxima were separated by a period of foresummer drought. This resulted in a predictable sequence of primary limiting factors to GPP beginning with air temperature in winter and proceeding to moisture and leaf area during the summer. Due to counteracting winter (positive) and summer (negative) GPP responses to warming, leaf area index and moisture availability were the best predictors of annual GPP differences across sites. Overall, mean annual GPP was greatest at the warmest site due to persistent vegetation photosynthetic activity throughout the winter. These results indicate that the trajectory of this region's carbon sequestration will be sensitive to reduced or delayed summer precipitation, especially if coupled to snow drought and earlier soil moisture recession, but summer precipitation changes remain highly uncertain. Given the demonstrated potential for seasonally offsetting responses to warming, we project that decadal semiarid montane forest carbon sequestration will remain relatively stable in the absence of severe disturbance.

Recent remote-sensing-based global carbon, water and energy budgets over land still include considerable uncertainties. Most existing flux products of terrestrial carbon, water and energy components were developed individually, despite the inherently coupled processes among them. In this study, we present a new set of global daily surface downwelling shortwave radiation (SW), net radiation (Rnet), evapotranspiration (ET), gross primary productivity (GPP), terrestrial ecosystem respiration (TER) and net ecosystem exchange (NEE) datasets at 0.05° resolutions from 1982 to 2019, by improving a satellite-based and coupled-process model—the Breathing Earth System Simulator (BESS). The new version of BESS (v2.0) integrated a newly developed ecosystem respiration module, an optimality-based maximum carboxylation rate (Vcmax) model, and extended the temporal coverage of flux datasets from 1982 to 2019. We evaluated BESS products against the FLUXNET2015 dataset at the site scale, and against several remote sensing and/or machine learning products on a global scale. At the site scale, BESS products agreed well with FLUXNET measurements, capturing 84%, 53%, 65%, 51% and 31% of daily variation in Rnet, ET, GPP, TER and NEE, respectively. Interannual variation in BESS NEE showed relatively low consistency with the FLUXNET measurements, while the rest fluxes explained approximately half of the interannual variation. On a global scale, we found marked discrepancies in spatio-temporal patterns between BESS and several benchmark products. Over the period 1982–2019, BESSv2.0 estimated the mean annual global Rnet, ET, GPP, TER and NEE to be 340.97 ± 5.22 ZJ yr−1 (mean ± 1 SD), 67.67 ± 0.71 103 km3 yr−1, 125.74 ± 5.95 Pg C yr−1, 109.30 ± 3.16 Pg C yr−1, and − 16.28 ± 2.95 Pg C yr−1, respectively, with significant annual linear trends (P < 0.01) by −0.05 103 km3 yr−2 for ET, by 0.52 Pg C yr−2 for GPP, by 0.28 Pg C yr−2 for TER, and by −0.25 Pg C yr−2 for NEE. We further evaluated various coupled processes derived by BESS in terms of functional properties (i.e., Budyko relation, carbon-use efficiency, water-use efficiency, and light-use efficiency), which agreed well with FLUXNET observations, unlike the benchmark products. Overall, BESSv2.0 can serve as a set of reliable and independent products from other global satellite products, facilitating studies related to global carbon, water and energy budgets in a coupled and comprehensive manner.

Abstract. The eddy covariance (EC) technique is currently the most widely used method for measuring carbon exchange between terrestrial ecosystems and the atmosphere at the ecosystem scale. Using this technique, a regional carbon flux network comprising a total of 34 sites has been established in the Heihe River basin (HRB) in northwest China. This network has been measuring the net ecosystem exchange (NEE) of CO2 for a variety of vegetation types. In this study, we have compiled and post-processed half-hourly flux data from these 34 EC flux sites in the HRB to create a continuous, homogenized time series dataset. We employ standardized processing procedures to fill data gaps in meteorological and NEE measurements at half-hourly intervals. NEE measurements are also partitioned into gross primary production (GPP) and ecosystem respiration (Reco). Furthermore, half-hourly meteorological and NEE data are aggregated into daily, weekly, monthly, and yearly timescales. As a result, we produced a continuous carbon flux and auxiliary meteorological dataset, which includes 18 sites with continuous multi-year observations during 2008–2022 and 16 sites observed only during the 2012 growing season, amounting to a total of 1513 site months. Evapotranspiration and energy flux measurements are also included. Using the post-processed dataset, we explored the temporal and spatial characteristics of carbon exchange in the HRB. In the diurnal variation curve, GPP, net carbon uptake, and Reco peak later for ecosystems in the artificial oasis (cropland and wetlands) compared to those outside the artificial oasis (grassland, forest, woodland, and Gobi/desert). Seasonal net carbon uptake, GPP, and Reco peak in early July for grassland, forest, woodland, and cropland but remain close to zero throughout the year for Gobi/desert. In the last decade, net carbon uptake of wetlands has significantly increased, while NEE for other ecosystems has not exhibited significant trends. Annual net carbon uptake, GPP, and Reco are significantly higher for sites inside the artificial/natural oasis compared to those outside the oasis. This post-processed carbon flux dataset has numerous applications, including exploring the carbon exchange characteristics of alpine and arid ecosystems, analyzing ecosystem responses to climate extremes, conducting cross-site synthesis from regional to global scales, supporting regional and global upscaling studies, interpreting and calibrating remote sensing products, and evaluating and calibrating carbon cycle models. The dataset can be accessed at https://doi.org/10.11888/Terre.tpdc.301321 (Wang et al., 2024).

Abstract. The terrestrial carbon cycle plays a critical role in modulating the interactions of climate with the Earth system, but different models often make vastly different predictions of its behavior. Efforts to reduce model uncertainty have commonly focused on model structure, namely by introducing additional processes and increasing structural complexity. However, the extent to which increased structural complexity can directly improve predictive skill is unclear. While adding processes may improve realism, the resulting models are often encumbered by a greater number of poorly determined or over-generalized parameters. To guide efficient model development, here we map the theoretical relationship between model complexity and predictive skill. To do so, we developed 16 structurally distinct carbon cycle models spanning an axis of complexity and incorporated them into a model–data fusion system. We calibrated each model at six globally distributed eddy covariance sites with long observation time series and under 42 data scenarios that resulted in different degrees of parameter uncertainty. For each combination of site, data scenario, and model, we then predicted net ecosystem exchange (NEE) and leaf area index (LAI) for validation against independent local site data. Though the maximum model complexity we evaluated is lower than most traditional terrestrial biosphere models, the complexity range we explored provides universal insight into the inter-relationship between structural uncertainty, parametric uncertainty, and model forecast skill. Specifically, increased complexity only improves forecast skill if parameters are adequately informed (e.g., when NEE observations are used for calibration). Otherwise, increased complexity can degrade skill and an intermediate-complexity model is optimal. This finding remains consistent regardless of whether NEE or LAI is predicted. Our COMPLexity EXperiment (COMPLEX) highlights the importance of robust observation-based parameterization for land surface modeling and suggests that data characterizing net carbon fluxes will be key to improving decadal predictions of high-dimensional terrestrial biosphere models.

Understanding how net ecosystem exchange (NEE) changes with temperature is central to the debate on climate change‐carbon cycle feedbacks, but still remains unclear. Here, we used eddy covariance measurements of NEE from 20 FLUXNET sites (203 site‐years of data) in mid‐ and high‐latitude forests to investigate the temperature response of NEE. Years were divided into two half thermal years (increasing temperature in spring and decreasing temperature in autumn) using the maximum daily mean temperature. We observed a parabolic‐like pattern of NEE in response to temperature change in both the spring and autumn half thermal years. However, at similar temperatures, NEE was considerably depressed during the decreasing temperature season as compared with the increasing temperature season, inducing a counter‐clockwise hysteresis pattern in the NEE–temperature relation at most sites. The magnitude of this hysteresis was attributable mostly (68%) to gross primary production (GPP) differences but little (8%) to ecosystem respiration (ER) differences between the two half thermal years. The main environmental factors contributing to the hysteresis responses of NEE and GPP were daily accumulated radiation. Soil water content (SWC) also contributed to the hysteresis response of GPP but only at some sites. Shorter day length, lower light intensity, lower SWC and reduced photosynthetic capacity may all have contributed to the depressed GPP and net carbon uptake during the decreasing temperature seasons. The resultant hysteresis loop is an important indicator of the existence of limiting factors. As such, the role of radiation, LAI and SWC should be considered when modeling the dynamics of carbon cycling in response to temperature change.