Comment on egusphere-2023-1057

<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

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).

Gross primary productivity (GPP) quantifies the rate at which plants convert atmospheric carbon dioxide into organic matter through photosynthesis, playing a vital role in the terrestrial carbon cycle. Machine learning (ML) techniques excel in handling spatiotemporally complex data, facilitating accurate spatial-scale inversion of forest GPP by integrating limited ground flux measurements with Remote Sensing (RS) observations. Enhancing ML algorithm performance for precise GPP estimation is a key research focus. This study introduces the Random Grid Search Algorithm (RGSA) for hyperparameters tuning to improve Random Forest (RF) and eXtreme Gradient Boosting (XGB) models across four major forest regions in China. Model optimization progressed through three stages: the Unoptimized (UO) XGB model achieved R2 = 0.77 and RMSE = 1.42 g Cm−2 d−1; the Hyperparameter Optimized (HO) XGB model using RGSA improved performance by 5.19% in R2 (0.81) and reduced RMSE by 9.15% (1.29 g Cm−2 d−1); the Hyperparameter and Variable Combination Optimized (HVCO) XGB model with selected variables (LAI, Temp, NR, VPD, and NDVI) further enhanced R2 to 0.83 and decreased RMSE to 1.23 g Cm−2 d−1. The optimized GPP estimates exhibited high spatial consistency with existing high-quality products like GOSIF GPP, GLASS GPP, and FLUXCOM GPP, validating the model’s reliability and effectiveness. This research provides crucial insights for improving GPP estimation accuracy and optimizing ML methodologies for forest ecosystems in China.

Estimation of crop gross primary production (GPP): I. impact of MODIS observation footprint and impact of vegetation BRDF characteristics

HadGEM2-CC 모델에서의 <TEX>$CO_2$</TEX> 농도증가에 대한 RCP 시나리오의 결과는 21세기 말 전 지구 연평균 기온과 강수 증가가 전망되고 이에 따라 식물의 생산량 및 호흡량 증가가 전망된다. 20세기 말 일차생산량(GPP와 NPP), 호흡량, LAI가 21세기 말 기온 증가에 따라 증가하는 점은 기존의 Shao et al. (2013)와 유사하였다. 특히 이전 연구와 유사하게 21세기 말 일차생산량과 호흡량은 고위도보다 열대 저위도 지역에서 증가량이 더 컸다. 기온이 상승하고 강수량이 증가하면서 식생이 자라지 않던 나지 면적이 감소하였고, 이에 따른 식생 면적 증가는 식생의 생산량(GPP, NPP) 증가로 나타났다. 특히, 본 연구에서는 C3 초지, 활엽수의 면적 증가가 뚜렷하였다. 이는 Beck and Goetz (2011)에서 언급한 대로 온난화에 따른 식생 면적 증가가 식생 생산성과 연관되어 <TEX>$CO_2$</TEX> 흡수작용을 강화하는 데 기여할 수 있음을 의미한다. Shao et al. (2013)에 따르면 21세기 말 누적 NEE는 증가가 전망되고 이는 특히 열대와 고위도 지역이 주요 흡수원으로 작용하였기 때문으로 그 원인을 설명하였다. 본 연구에서 사용된 HadGEM2-CC에서는 전 지구 평균적으로 NEE 흡수가 증가하는 경향은 동일하게 전망하며, 이는 열대보다는 북반구 고위도지역인 유라시아와 북미 대륙에서 증가한 흡수가 그 원인으로 분석되었다(Fig. 8). 앞서 Mynenl et al. (1997)에 따르면 기온상승에 따라 식생의 광합성활동, 생장시기 길이와 시작시기의 당겨짐을 보고하였다. 본 연구 실험에서도 이와 유사하게 미래에 기온 상승에 따라 식생 성장 기간이 길어지고 LAI도 증가하며 식생 지대가 점차 고위도로 북상할 것을 전망하였다(Figs. 5, 12b). 이에 따라 육상 생태계의 <TEX>$CO_2$</TEX> 흡수량은 20세기 말보다 21세기 말에 증가하였고 우리나라가 속해있는 동아시아지역(<TEX>$90^{\circ}E{\sim}140^{\circ}E$</TEX>, <TEX>$20^{\circ}N{\sim}60^{\circ}N$</TEX>)은 기온, 강수뿐 아니라 <TEX>$CO_2$</TEX> 흡수량도 같은 위도대의 전 지구 동서평균보다 크게 모의되었다. RCPs에 따른 흡수율은 21세기 중반까지는 대기 중 이산화탄소 농도 변화율과 유사한 경향을 보이는데 RCP 8.5에서는 21세기 후반에 흡수 증가율이 감소하며 이는 Liddicoat et al. (2013) 에서 보인것과 유사하다. 하지만 대기 중 <TEX>$CO_2$</TEX>의 증가와 식생분포 지역의 확대에도 불구하고 21세기 말 육상생태계의 순생태계흡수량은 크게 증가하지 않음을 확인할 수 있었다. 이는 기온 상승이 크게 일어난 21세기 후반부터 토양 호흡의 급격한 증가로 인하여 육상생태계의 이산화탄소 흡수 능력은 감소한 것에 기인하였다. 향후 본 연구결과의 유의성을 확보하기 위해 다양한 모델의 자료를 추가할 필요가 있다. Shao et al. (2013)에 따르면 미래 탄소 흡수 전망에 있어 전 지구 및 위도별 모의 결과가 모델마다 매우 다양한데 이는 지면생태모형 간의 식생역학, 물리과정의 차이로 해석된다. 미래 육상생태계의 이산화탄소 흡수 능력의 변화와 기후변화를 보다 정확하게 예측하기 위해서는 다른 모델의 자료를 이용한 불확실성을 정량화 하는 것이 필요하며 이는 전 지구 및 지역별 탄소 순환 이해를 높이는 데 기여할 것이다. Terrestrial ecosystem plays the important role as carbon sink in the global carbon cycle. Understanding of interactions of terrestrial carbon cycle with climate is important for better prediction of future climate change. In this paper, terrestrial carbon cycle is investigated by Hadley Centre Global Environmental Model, version 2, Carbon Cycle (HadGEM2-CC) that considers vegetation dynamics and an interactive carbon cycle with climate. The simulation for future projection is based on the three (8.5/4.5/2.6) representative concentration pathways (RCPs) from 2006 to 2100 and compared with historical land carbon uptake from 1979 to 2005. Projected changes in ecological features such as production, respiration, net ecosystem exchange and climate condition show similar pattern in three RCPs, while the response amplitude in each RCPs are different. For all RCP scenarios, temperature and precipitation increase with rising of the atmospheric <TEX>$CO_2$</TEX>. Such climate conditions are favorable for vegetation growth and extension, causing future increase of terrestrial carbon uptakes in all RCPs. At the end of 21st century, the global average of gross and net primary productions and respiration increase in all RCPs and terrestrial ecosystem remains as carbon sink. This enhancement of land <TEX>$CO_2$</TEX> uptake is attributed by the vegetated area expansion, increasing LAI, and early onset of growing season. After mid-21st century, temperature rising leads to excessive increase of soil respiration than net primary production and thus the terrestrial carbon uptake begins to fall since that time. Regionally the NEE average value of East-Asia (<TEX>$90^{\circ}E-140^{\circ}E$</TEX>, <TEX>$20^{\circ}N{\sim}60^{\circ}N$</TEX>) area is bigger than that of the same latitude band. In the end-<TEX>$21^{st}$</TEX> the NEE mean values in East-Asia area are <TEX>$-2.09PgC\;yr^{-1}$</TEX>, <TEX>$-1.12PgC\;yr^{-1}$</TEX>, <TEX>$-0.47PgC\;yr^{-1}$</TEX> and zonal mean NEEs of the same latitude region are <TEX>$-1.12PgC\;yr^{-1}$</TEX>, <TEX>$-0.55PgC\;yr^{-1}$</TEX>, <TEX>$-0.17PgC\;yr^{-1}$</TEX> for RCP 8.5, 4.5, 2.6.

Simultaneous assimilation of SMOS soil moisture and atmospheric CO2 in-situ observations to constrain the global terrestrial carbon cycle

The measurement of carbon exchange between vegetation and the atmosphere is vital to quantify the impact of environmental variables on the carbon sequestration capacity of forests, and to predict how they will respond to future climate. In this study we use proximal remote sensing, defined as observations made from non-contact radiometric or imaging sensors in close proximity to the forest canopy (10–20 m), as an intermediate upscaling tool between direct measurements of carbon fluxes and satellite-derived estimations of primary productivity in a tropical dry forest (TDF) in Jalisco, Mexico. Two broad-band vegetation indices (VIs), the normalized difference VI and the enhanced vegetation index 2 (EVI2), were calculated from proximally sensed canopy properties, validated with field estimates of the fraction of absorbed photosynthetically active radiation by photosynthetic tissue (fAPARgreen), and compared to estimates of gross primary productivity (GPP) and net ecosystem exchange of CO2, measured from a flux tower. The VIs captured the phenology of the TDF, both under typical summer rainfall and during an atypically-dry wet season in El Niño of 2009. The VIs also tracked a secondary leaf-flushing in the dry season of 2010. Our study suggests that (1) VIs are the best predictors of gross carbon uptake, able to explain up to 86% of variations in GPP; (2) VIs are accurate predictors of the photosynthetic capacity of green tissue, able to explain up to 99% of fAPARgreen variation; and (3) VIs and soil water content can be used to develop an empirical model that captures the seasonal trajectory of GPP from high respiration after the rain pulses, to rapid leaf development, and finally to slow senescence as the soil dries out. Proximal remote sensing constitutes a useful tool to link field-base measurements of carbon fluxes to satellite- or airborne-derived estimates of carbon exchange.

Global‐scale estimates of carbon fluxes from satellite data‐driven models are constrained by considerable uncertainties regarding Gross Primary Production (GPP) and the lack of the watershed‐scale measurements required for model calibration. Recently conducted global modelling efforts indicate that semiarid ecosystems dominate the increasing trends and inter‐annual variation of net CO2 exchange with the atmosphere, but semi‐arid regions have received little attention with regard to GPP estimation. In this study, we used the distinct isotope effect of transpiration and evaporation to calculate transpiration losses and subsequently CO2 uptake by terrestrial vegetation through the water and carbon cycle using the water use efficiency of plants. By studying two Mediterranean watersheds with contrasted environmental conditions over several hydrological years, we found a strong dependence of GPP on annual and seasonal water availability. The results demonstrated that when compared to GPP values obtained in worldwide biomes using biological methods, our isotope approach was validated, highlighting the limitations of satellite‐data‐driven models like MODIS in capturing the impact of water stress on photosynthesis and GPP estimates. These results encourage investigation of GPP by the isotope mass balance approach where direct carbon flux measurements are rare or absent in order to help to substantiate, modify or shed doubt on interpolated GPP for those regions and achieve consensus on global GPP estimates. Given the relevant role of semi‐arid ecosystems in the global carbon balance as well as the limitation of existing data sets, our improved method based on the isotope mass balance approach helps to obtain rapid and affordable estimates of GPP for semi‐arid ecosystems.

Improving estimations of ecosystem respiration with asymmetric daytime and nighttime temperature sensitivity and relative humidity

&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;

Vegetation primary production estimation at maize and alpine meadow over the Heihe River Basin, China

Gross primary productivity (GPP) is a significant indicator of terrestrial system carbon cycle and is one of the most important parameters of global climate change. Accurate modeling of GPP is important for understanding the changes of carbon dioxide in the atmosphere. Although many studies have been conducted to explore methods for estimating GPP, large uncertainties still exist among different models. The combination of remote sensing data with flux data is a feasible approach to determine site-based GPP. Using multisource remote sensing data, meteorological data obtained from Google Earth Engine, and GPP estimates from eddy covariance measurement, we present a model, based on the random forest regression (RFR) algorithm, to predict site-scale GPP. First, we train and tune the RFR model for 16 global deciduous broadleaf forest (DBF) flux sites by remote sensing-based drivers. Then we evaluate the RFR model's performance by predicting GPP for the other eight DBF sites. Our results show that, compared with the MOD17A2H GPP product, this GPP prediction method improves modeling of GPP, with an <italic>R</italic>² of 0.82 and an RMSE of 1.93 g C m^{− 2} d^{− 1}. Contribution analysis shows that the enhanced vegetation index is the most important driver among input variables. Our study may provide a reliable method to predict GPP in global DBF, which is promising for different-scale evaluations of carbon sequestration.

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.

Investigating the carbon flux in orchard ecosystems is crucial for assessing agroecosystem productivity and optimizing management practices. We measured and estimated carbon fluxes (gross primary productivity, GPP; ecosystem respiration, Re; and net ecosystem exchange, NEE) and environmental variables in a seven-year-old kiwifruit orchard over two years. Our results showed that diurnal carbon fluxes exhibited bell-shaped patterns, peaking between 12:30 and 15:30. Daily carbon fluxes exhibited a seasonal trend, characterized by an increase followed by a decrease. The average daily GPP, Re, and NEE values were 6.77, 4.99, and −1.79 g C m−2 d−1 in 2018, and 5.88, 4.78, and −1.10 g C m−2 d−1 in 2019, respectively. The orchard sequestered −444.25 g C m−2 in 2018 and −285.77 g C m−2 in 2019, which accounted for 26.4% and 18.6% of GPP, respectively. Diurnal GPP and NEE were significantly influenced by photosynthetically active radiation (PAR), with direct path coefficients of 0.75 and 0.88 (p &lt; 0.01), while air temperature (Ta) significantly affected GPP and NEE through PAR, with an indirect path coefficient of 1.12 for both. PAR had a similar effect on daily GPP and NEE, while both were indirectly influenced by soil temperature (Ts) at a 5 cm depth and vapor pressure deficit (VPD). Re was primarily impacted by VPD, with a direct path coefficient of 0.64 (p &lt; 0.01), while Ta and the concentration of air carbon dioxide (CCO2) significantly affected GPP through VPD, with indirect path coefficients of 0.82 and −0.80. The leaf area index (LAI) and soil water content (SWC) at a 20 cm depth exhibited a significant correlation with carbon fluxes during the vigorous growing period.