Reply on RC1

Abstract. Climate change, rising CO2 concentration, and land use and land cover change (LULCC) are primary driving forces for terrestrial gross primary productivity (GPP), but their impacts on the temporal changes in GPP are uncertain. In this study, the effects of the three main factors on the interannual variation (IAV) and seasonal cycle amplitude (SCA) of GPP in China were investigated using 12 terrestrial biosphere models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project. The simulated ensemble mean value of China's GPP between 1981 and 2010, driven by common climate forcing, LULCC and CO2 data, was found to be 7.4±1.8 Pg C yr−1. In general, climate was the dominant control factor of the annual trends, IAV and seasonality of China's GPP. The overall rising CO2 led to enhanced plant photosynthesis, thus increasing annual mean and IAV of China's total GPP, especially in northeastern and southern China, where vegetation is dense. LULCC decreased the IAV of China's total GPP by ∼7 %, whereas rising CO2 induced an increase of 8 %. Compared to climate change and elevated CO2, LULCC showed less contributions to GPP's temporal variation, and its impact acted locally, mainly in southwestern China. Furthermore, this study also examined subregional contributions to the temporal changes in China's total GPP. Southern and southeastern China showed higher contributions to China's annual GPP, whereas southwestern and central parts of China explained larger fractions of the IAV in China's GPP.

Reliable modeling of carbon and water fluxes is essential for understanding the terrestrial carbon and water cycles and informing policy strategies aimed at constraining carbon emissions and improving water use efficiency. We used an assimilation framework (LPJ-Vegetation and soil moisture Joint Assimilation, or LPJ-VSJA) to improve gross primary production (GPP) and evapotranspiration (ET) estimates globally. The terrestrial biosphere model that we used is the integrated model – LPJ-PM coupled from the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) and a hydrology module (i.e., the updated Priestley–Taylor Jet Propulsion Laboratory model, PT-JPLSM). Satellite-based soil moisture products derived from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) and leaf area index (LAI) from the global Land and Ground satellite (GLASS) product were assimilated into LPJ-PM to improve GPP and ET simulations using a Proper Orthogonal Decomposition-based ensemble four-dimensional variational assimilation method (PODEn4DVar). The joint assimilation framework LPJ-VSJA achieved the best model performance (with an R2 of 0.91 and 0.81 and an RMSD reduced by 50.4 % and 38.4 % for GPP and ET, respectively, compared with those of LPJ-DGVM at the monthly scale). The assimilated GPP and ET demonstrated a better performance in the arid and semiarid regions (GPP: R2 = 0.73, ubRMSD = 1.05 g C m−2 d−1; ET: R2 = 0.73, ubRMSD = 0.61 mm d−1) than in the humid and sub-dry humid regions (GPP: R2 = 0.61, ubRMSD = 1.23 g C m−2 d−1; ET: R2 = 0.66; ubRMSD = 0.67 mm d−1). The ET simulated by LPJ-PM that assimilated SMAP or SMOS had a slight difference, and the ET that assimilated SMAP soil moisture data was more improved than that assimilated SMOS data. Our global simulation modeled by LPJ-VSJA was compared with several global GPP and ET products (e.g., GLASS GPP, GOSIF GPP, GLDAS ET, GLEAM ET) using the triple collocation (TC) method. Our products, especially ET, exhibited advantages in the overall error distribution (estimated error (μ): 3.4 mm month−1; estimated standard deviation of μ: 1.91 mm month−1). Our research showed that the assimilation of multiple datasets could reduce model uncertainties, while the model performance differed across regions and plant functional types. Our assimilation framework (LPJ-VSJA) can improve the model simulation performance of daily GPP and ET globally, especially in water-limited regions.

Understanding the changes in gross primary production (GPP), which is the total carbon fixation by terrestrial ecosystems through vegetation photosynthesis, due to land use conversion in a tourism city is important for carbon cycle studies. Satellite data from Landsat 5, Landsat 7 and Landsat 8 and meteorological data are used to calculate annual GPP for 1995, 2003 and 2014, respectively, using the vegetation production model (VPM) in the tourism city Denpasar, Bali, Indonesia. Five land use types generated from topographic maps in three different years over the past two decades are used to quantify the impacts of land use changes on GPP estimation values. Analysis was performed for two periods to determine changes in land use and GPP value as well as their speed. The results demonstrated that urban land development, namely, the increase of settlement areas due to tourism activity, had overall negative effects on terrestrial GPP. The total GPP of the whole area decreased by 7793.96 tC year−1 (12.65%) during the study period. The decline is due to the conversion of agriculture and grassland area into settlements, which caused the city to lose half of its ability to uptake carbon through vegetation. However, although forest area is declining, forest maintenance and restoration by making them protection areas has been helpful in preventing a drastic decline in GPP value over the past two decades. This study provides information that is useful for carbon resource management, tourism, policy making and scholars concerned about carbon reduction in a tourism city.

Contrasting changes in gross primary productivity of different regions of North America as affected by warming in recent decades

Attributions and predictions of gross primary productivity (GPP) under climate change is of great significance for facilitating a deeper understanding of the global and regional terrestrial carbon cycle and assessing ecosystem health. In this study, we have designed a novel approach to simulate GPP based on the satellite and meteorological data compiling the advantages of the light use efficiency model with regression methods (LUE-RE model), which overcomes the limitation of the satellite-based method in GPP simulation and projection in the future time without satellite data. Based on the proposed method, results show that GPP in the Yangtze River Basin shows a significant increase trend in the historical period. Elevated CO2 dominates the changes of GPP in the Yangtze River Basin. In the future, with the increase in elevated CO2 and climate change, the trend of GPP growth is more obvious. The growth slopes under different scenarios are 2.65 gCm−2year−1a−1, 12.34 gCm−2year−1a−1, 24.91 gCm−2year−1a−1, and 39.62 gCm−2year−1a−1. There are obvious seasonal differences in the future changes of GPP in the Yangtze River Basin, of which the GPP changes mostly in spring. The spatial patterns show that higher GPP is concentrated in the upper stream, while the low values are mainly concentrated in the middle reaches. This study contributes a new method to project GPP and highlights that stakeholders should pay more attention to the significant GPP increases in spring in the future.

Consistency between sun-induced chlorophyll fluorescence and gross primary production of vegetation in North America

Terrestrial carbon cycle plays an important role in global climate change. As a key component of terrestrial carbon cycle, gross primary productivity (GPP) is a major determinant of the exchange of carbon between the atmosphere and terrestrial ecosystems. 8-day global GPP estimated from ground meteorological data and remotely sensed fraction of photosynthetic active radiation (fPAR) by MODIS using the light use efficiency approach is currently provided as MOD17 product. Previous studies indicated that MODIS GPP has large uncertainties in some ecosystems. In this study, GPP of a subtropical coniferous plantation at Qianyanzhou Experimental Station in southern China was firstly calculated using the MODIS GPP algorithm (MOD17 algorithm) driven by MODIS fPAR and measured meteorological data. Calculated GPP was validated using GPP measured during 2003 and 2004 with the eddy covariance technique. Then the potential to better MODIS GPP was investigated through comparing GPP calculated using the MOD17 algorithm and improved fPAR or/and maximum light use efficiency (ε <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> ) calibrated with measured GPP. The results indicated that the MODIS GPP product significantly underestimated measured GPP at this planted forest. The R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of MODIS GPP with measured GPP was 0.72 and 0.67 in 2003 and 2004, respectively. And the calculated annual GPP was 33% and 47% lower than measured values in these two years. The improvement on fPAR through using LAI data estimated with photosynthetic active radiation (PAR) measured above and below canopy can definitely remedy underestimation of annual GPP. The application of ε <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> determined through model calibration improved annual GPP more significantly, indicating that the errors in MODIS GPP at this site can be mainly attributed to the underestimation of fPAR and ε <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> . When the improved fPAR and ε <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> were used, the agreement between calculated and measured 8-day GPP improved significantly, with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> equals to 0.78 and 0.85 for years 2003 and 2004, respectively. And the calculated annual GPP was only 3.5% lower and 1.3% higher than measured values in these two years. Through this study, it can be concluded that accurate ε <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> and LAI from which fPAR is calculated are required for reliably calculating regional/global GPP with the MOD17 algorithm. The fusion of flux data with remote sensing data can provide the accurate estimate of ε <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> andhas a great potential to control uncertainties in calculated regional/global GPP.

Drought has the potential to significantly decrease net primary production (NPP). However, the drought sensitivity of NPP remains unclear globally. Here we investigated the response of NPP to drought for the period 1982–2011 using NPP derived from satellite models, eight dynamic global vegetation models (DGVMs), and several drought indices. Our results indicate that the sign of drought response of NPP is consistent across satellites and DGVMs globally. Both the model and satellite data show a unimodal distribution of drought sensitivity across climatic gradients and indicate that semiarid regions are the most drought‐sensitive areas. In addition, a decreasing drought trend was significantly correlated with an increasing trend in satellite‐derived NPP or DGVM‐derived NPP globally. However, the DGVM‐derived NPP is more sensitive to drought than the satellite‐derived NPP over most vegetated land areas and presents a stronger correlation with drought, higher drought sensitivity, and larger slopes between NPP and drought trends. The overresponse of the DGVM‐derived NPP to drought relative to the satellite‐derived NPP could be attributed to the higher drought sensitivity of the simulated leaf area index and overresponse of the DGVM‐derived NPP to the vapor pressure deficit. Overall, large uncertainties remain in the response of NPP to drought and additional long‐term observations are required to better represent NPP responses to water stress in DGVM models.

Abstract. Systematic evaluation of the carbon cycle physical and biological variables simulated in Earth System Model (ESM) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP 6) is fundamental to the understanding of terrestrial ecosystems, as well as to future projections. Leaf Area Index (LAI), Gross Primary Productivity (GPP), Net Primary Productivity (NPP), Net Ecosystem Productivity (NEP) and Land Surface Temperature (LST) as key indicators of carbon cycle performance in ESM outputs, play a critical role in evaluating ecosystem functions. Assessing these metrics can provide valuable insights into the biases in model-simulated ecosystems and offer guidance for model improvement. In this study, we assessed the interannual trends performance of LAI, GPP, NPP, NEP and LST simulated by 12 CMIP6 ESMs during the historical period by using satellite LAI, NPP, NEP, LST and CSIF data as observations. The findings indicate that: (1) There are significant uncertainties in the overall trends and interannual variability in LAI, NPP, and LST captured by the CMIP6 ESM. Meanwhile, simulated GPP and NEP trends were lower than observations with discrepancies reaching 0.03 yr−1 for GPP and 2.46 gCm-2yr-1 for NEP. (2) Spatially, CMIP6 ESMs exhibited widespread underestimation of trends in LAI, GPP, NPP, and NEP across China. The MME underestimated these variables in 46.29 % (LAI), 43.47 % (GPP), 49.81 % (NPP), and 61.34 % (NEP) of the study area. Meanwhile, the simulated LST trend is underestimated in northern China, while its overestimations in western and southern China. (3) ESMs inadequate responsiveness to anthropogenic and environmental forcing and incomplete mechanistic representation of plant respiration pathways struggled accurate simulation of trends in LAI, GPP, NPP, NEP and LST.

In this study, we investigated the impact of human settlement growth on vegetation carbon uptake in the eastern United States between 1992/1993 and 2001. Human settlement growth was measured by changes in the density of housing units. Vegetation carbon uptake was estimated with gross primary production (GPP) based on the light-use efficiency approach applied to satellite imagery. Annual GPP was found to increase by approximately 140 g C m−2 on average for the entire study area in 2001 compared to 1992/1993, accompanied by region-wide increases in downward shortwave radiation and minimum daily temperature. Changes in GPP, however, varied significantly by different types of settlement growth. Exurbanized areas, where the rural settlement (less than 0.025 units per acre) converted to exurbs (0.025–0.6 units per acre), were associated with approximately 157 g C m−2 increase in GPP due to high vegetation proportions. Suburbanization, the conversion from exurban settlement to suburbs (0.6–4 units per acre), was related with a decline of GPP by 152 g C m−2 due to progressive development of built-up land cover. Results help to understand the potential of carbon mitigation in the human-dominated landscapes using vegetation as a natural store of carbon dioxide. This in turn has implications for the low-carbon development planning along the gradient of human settlement densities.

Changbai Mountain Nature Reserve (CNR) is a typical temperate forest ecosystem, and gross primary production (GPP) of which is closely related to topography and climate change. Research on the spatio-temporal variations and influencing factors of GPP in the CNR is of great significance for assessing growth status of vegetation and the quality of ecological environment. We calculated GPP in CNR using the vegetation photosynthesis model (VPM), and analyzed the influences of slope, altitude, temperature, precipitation, and total radiation. The results showed that the range of annual average GPP in CNR was 63-1706 g C·m-2·a-1 from 2000 to 2020 and that GPP decreased with the increases of altitude. Temperature played the most important role in driving the spatial varia-tion of GPP, with a significant positive correlation with GPP. During the study period, the overall annual GPP showed a significant increase trend in CNR, with an average annual increase of 13 g C·m-2·a-1. The areas with increase of annual GPP accounted for 79.9% of the total area, and the area proportion of annual GPP increase differed in each plant functional type. Annual precipitation was significantly negatively correlated with GPP in 43.2% of CNR, while annual mean temperature and annual total radiation were significantly positively correlated with GPP in 47.2% and 82.4% of CNR. GPP would increase continuously in CNR under the scenario of future global warming.

Estimating carbon dioxide exchange rates at contrasting northern peatlands using MODIS satellite data

Combined MODIS land surface temperature and greenness data for modeling vegetation phenology, physiology, and gross primary production in terrestrial ecosystems

This study tests the ability of five Dynamic Global Vegetation Models (DGVMs), forced with observed climatology and atmospheric CO2, to model the contemporary global carbon cycle. The DGVMs are also coupled to a fast ‘climate analogue model’, based on the Hadley Centre General Circulation Model (GCM), and run into the future for four Special Report Emission Scenarios (SRES): A1FI, A2, B1, B2. Results show that all DGVMs are consistent with the contemporary global land carbon budget. Under the more extreme projections of future environmental change, the responses of the DGVMs diverge markedly. In particular, large uncertainties are associated with the response of tropical vegetation to drought and boreal ecosystems to elevated temperatures and changing soil moisture status. The DGVMs show more divergence in their response to regional changes in climate than to increases in atmospheric CO2 content. All models simulate a release of land carbon in response to climate, when physiological effects of elevated atmospheric CO2 on plant production are not considered, implying a positive terrestrial climate‐carbon cycle feedback. All DGVMs simulate a reduction in global net primary production (NPP) and a decrease in soil residence time in the tropics and extra‐tropics in response to future climate. When both counteracting effects of climate and atmospheric CO2 on ecosystem function are considered, all the DGVMs simulate cumulative net land carbon uptake over the 21st century for the four SRES emission scenarios. However, for the most extreme A1FI emissions scenario, three out of five DGVMs simulate an annual net source of CO2 from the land to the atmosphere in the final decades of the 21st century. For this scenario, cumulative land uptake differs by 494 Pg C among DGVMs over the 21st century. This uncertainty is equivalent to over 50 years of anthropogenic emissions at current levels.

Modeling the interannual variability and trends in gross and net primary productivity of tropical forests from 1982 to 1999