Exploring the ecological meanings of temperature sensitivity of ecosystem respiration from different methods

Temperature sensitivity (Q10) of ecosystem respiration (Re) is a critical parameter for predicting global terrestrial carbon dynamics and its response to climate warming. However, the determination of Q10 has been controversial. In this study, we scrutinized the underpinnings of three mainstream methods to reveal their relationships in estimating Q10 for Re in the Heihe River Basin, northwest China. Specifically, these methods are Q10 estimated from the long-term method (Q10_long), short-term method (Q10_short), and the low-frequency (Q10_lf) and high-frequency (Q10_hf) signals decomposed by the singular spectrum analysis (SSA) method. We found that: 1) Q10_lf and Q10_long are affected by the confounding effects caused by non-temperature factors, and are 1.8 ± 0.3 and 1.7 ± 0.3, respectively. 2) The high-frequency signals of the SSA method and short-term method have consistent roles in removing the confounding effects. Both Q10_short and Q10_hf reflect the actual response of respiration to temperature. 3) Overall, Q10_long has a larger variability (1.7 ± 0.3) across different biomes, whereas Q10_short and Q10_hf show convergence (1.4 ± 0.2 and 1.3 ± 0.1, respectively). These results highlight the fact that Q10 can be overestimated by the long-term method, whereas the short-term method and high-frequency signals decomposed by the SSA method can obtain closer and convergent values after removing the confounding effects driven by non-temperature factors. Therefore, it is recommended to use the Q10 value estimated by the short-term method or high-frequency signals decomposed by the SSA method to predict carbon dynamics and its response to global warming in Earth system models.

Early spring onset increases carbon uptake more than late fall senescence: modeling future phenological change in a US northern deciduous forest

In deciduous forests, spring leaf development and fall leaf senescence regulate the timing and duration of photosynthesis and transpiration. Being able to model these dates is therefore critical to accurately representing ecosystem processes in biogeochemical models. Despite this, there has been relatively little effort to improve internal phenology predictions in widely used biogeochemical models. Here, we optimized the phenology algorithms in a regionally developed biogeochemical model (PnET-CN) using phenology data from eight mid-latitude PhenoCam sites in eastern North America. We then performed a sensitivity analysis to determine how the optimization affected future predictions of carbon, water, and nitrogen cycling at Bartlett Experimental Forest, New Hampshire. Compared to the original PnET-CN phenology models, our new spring and fall models resulted in shorter season lengths and more abrupt transitions that were more representative of observations. The new phenology models affected daily estimates and interannual variability of modeled carbon exchange, but they did not have a large influence on the magnitude or long-term trends of annual totals. Under future climate projections, our new phenology models predict larger shifts in season length in the fall (1.1-3.2 days decade^-1) compared to the spring (0.9-1.5 days decade^-1). However, for every day the season was longer, spring had twice the effect on annual carbon and water exchange totals compared to the fall. These findings highlight the importance of accurately modeling season length for future projections of carbon and water cycling.

Climate change impacts plant carbon balance, increasing mean future carbon use efficiency but decreasing total forest extent at dry range edges

Carbon use efficiency (CUE) represents how efficient a plant is at translating carbon gains through gross primary productivity (GPP) into net primary productivity (NPP) after respiratory costs (R_a ). CUE varies across space with climate and species composition, but how CUE will respond to climate change is largely unknown due to uncertainty in R_a at novel high temperatures. We use a plant physiological model validated against global CUE observations and LIDAR vegetation canopy height data and find that model-predicted decreases in CUE are diagnostic of transitions from forests to shrubland at dry range edges. Under future climate scenarios, we show mean growing season CUE increases in core forested areas, but forest extent decreases at dry range edges, with substantial uncertainty in absolute CUE due to uncertainty in R_a . Our results highlight that future forest resilience is nuanced and controlled by multiple competing mechanisms.

Simulation of dissolved organic carbon concentrations and fluxes in Chinese monsoon forest ecosystems using a modified TRIPLEX-DOC model

Dissolved organic carbon (DOC) plays an important role in global and regional carbon cycles. However, the quantification of DOC in forest ecosystems remains uncertain. Here, the processed-based biogeochemical model TRIPLEX-DOC was modified by optimizing the function of soil organic carbon distribution with increasing depths, as well as DOC sorption-desorption efficiency. The model was validated by field measurements of DOC concentration and flux at five forest sites and Beijiang River basin in monsoon regions of China. Model validation indicated that seasonal patterns of DOC concentration across climatic zones were different, and these differences were captured by our model. Importantly, the modified model performed better than the original model. Indeed, model efficiency of the modified model increased from -0.78 to 0.19 for O horizon predictions, and from -0.46 to 0.42 for the mineral soils predictions. Likewise, DOC fluxes were better simulated by the modified model. At the site scale, the simulated DOC fluxes were strongly correlated with the observed values (R² = 0.97, EF = 0.91). At the regional scale, the DOC flux predicted in the Beijiang River basin was 16.44 kg C/ha, which was close to the observed value of 17 kg C/ha. Using sensitivity analysis, we showed that temperature, precipitation and temperature sensitivity of DOC decomposition (Q₁₀) were the most sensitive parameters when predicting DOC concentrations and fluxes in forest soils. We also found that both the percentage of DOC flux to forest net ecosystem productivity, and the retention of DOC by mineral soil were highly correlated with the amount of precipitation. Overall, our model validations indicated that the modified TRIPLEX-DOC model is a useful tool for simulating the dynamics of DOC concentrations and fluxes in forest ecosystems. We highlight that more accurate estimates of parameter Q₁₀ in deep mineral soils can reduce model uncertainty, when simulating DOC concentrations and fluxes in forest soils.

Evaluating the Performance of Satellite-Derived Vegetation Indices for Estimating Gross Primary Productivity Using FLUXNET Observations across the Globe

Satellite-derived vegetation indices (VIs) have been widely used to approximate or estimate gross primary productivity (GPP). However, it remains unclear how the VI-GPP relationship varies with indices, biomes, timescales, and the bidirectional reflectance distribution function (BRDF) effect. We examined the relationship between VIs and GPP for 121 FLUXNET sites across the globe and assessed how the VI-GPP relationship varied among a variety of biomes at both monthly and annual timescales. We used three widely-used VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and 2-band EVI (EVI2) as well as a new VI - NIRV and used surface reflectance both with and without BRDF correction from the moderate resolution imaging spectroradiometer (MODIS) to calculate these indices. The resulting traditional (NDVI, EVI, EVI2, and NIRV) and BRDF-corrected (NDVIBRDF, EVIBRDF, EVI2BRDF, and NIRV, BRDF) VIs were used to examine the VI-GPP relationship. At the monthly scale, all VIs were moderate or strong predictors of GPP, and the BRDF correction improved their performance. EVI2BRDF and NIRV, BRDF had similar performance in capturing the variations in tower GPP as did the MODIS GPP product. The VIs explained lower variance in tower GPP at the annual scale than at the monthly scale. The BRDF-correction of surface reflectance did not improve the VI-GPP relationship at the annual scale. The VIs had similar capability in capturing the interannual variability in tower GPP as MODIS GPP. VIs were influenced by temperature and water stresses and were more sensitive to temperature stress than to water stress. VIs in combination with environmental factors could improve the prediction of GPP than VIs alone. Our findings can help us better understand how the VI-GPP relationship varies among indices, biomes, and timescales and how the BRDF effect influences the VI-GPP relationship.

Projections of water, carbon, and nitrogen dynamics under future climate change in an old-growth Douglas-fir forest in the western Cascade Range using a biogeochemical model

Statistically downscaled climate change scenarios from four General Circulation Models for two Representative Concentration Pathways (RCP) were applied as inputs to a biogeochemical model, PnET-BGC, to examine potential future dynamics of water, carbon, and nitrogen in an old-growth Douglas-fir forest in the western Cascade Range. Projections show 56% to 77% increases in stomatal conductance throughout the year from 1986-2010 to 2076-2100, and 65% to 104% increases in leaf carbon assimilation between October and June over the same period. However, future dynamics of water and carbon under the RCP scenarios are affected by a 49% to 86% reduction in foliar biomass resulting from severe air temperature and humidity stress to the forest in summer. Important implications of future decreases in foliar biomass include 1) 20% to 71% decreases in annual transpiration which increase soil moisture by 7% to 15% in summer and fall; 2) decreases in photosynthesis by 77% and soil organic matter by 62% under the high radiative forcing scenario; and 3) altered foliar and soil carbon to nitrogen stoichiometry. Potential carbon dioxide fertilization effects on vegetation are projected to 1) amplify decreases in transpiration by 4% to 9% and increases in soil moisture in summer and fall by 1% to 2%; and 2) alleviate decreases in photosynthesis by 4%; while 3) having negligible effects on the dynamics of nitrogen. Our projections suggest that future decrease in transpiration and moderate water holding capacity may mitigate soil moisture stress to the old-growth Douglas-fir forest. Future increases in nitrogen concentration in soil organic matter are projected to alleviate the decrease in net nitrogen mineralization despite a reduction in decomposition of soil organic matter by the end of the century.

Solar-induced chlorophyll fluorescence is strongly correlated with terrestrial photosynthesis for a wide variety of biomes: First global analysis based on OCO-2 and flux tower observations

Solar-induced chlorophyll fluorescence (SIF) has been increasingly used as a proxy for terrestrial gross primary productivity (GPP). Previous work mainly evaluated the relationship between satellite-observed SIF and gridded GPP products both based on coarse spatial resolutions. Finer resolution SIF (1.3 km × 2.25 km) measured from the Orbiting Carbon Observatory-2 (OCO-2) provides the first opportunity to examine the SIF-GPP relationship at the ecosystem scale using flux tower GPP data. However, it remains unclear how strong the relationship is for each biome and whether a robust, universal relationship exists across a variety of biomes. Here we conducted the first global analysis of the relationship between OCO-2 SIF and tower GPP for a total of 64 flux sites across the globe encompassing eight major biomes. OCO-2 SIF showed strong correlations with tower GPP at both midday and daily timescales, with the strongest relationship observed for daily SIF at the 757 nm (R²  = 0.72, p < 0.0001). Strong linear relationships between SIF and GPP were consistently found for all biomes (R²  = 0.57-0.79, p < 0.0001) except evergreen broadleaf forests (R²  = 0.16, p < 0.05) at the daily timescale. A higher slope was found for C₄ grasslands and croplands than for C₃ ecosystems. The generally consistent slope of the relationship among biomes suggests a nearly universal rather than biome-specific SIF-GPP relationship, and this finding is an important distinction and simplification compared to previous results. SIF was mainly driven by absorbed photosynthetically active radiation and was also influenced by environmental stresses (temperature and water stresses) that determine photosynthetic light use efficiency. OCO-2 SIF generally had a better performance for predicting GPP than satellite-derived vegetation indices and a light use efficiency model. The universal SIF-GPP relationship can potentially lead to more accurate GPP estimates regionally or globally. Our findings revealed the remarkable ability of finer resolution SIF observations from OCO-2 and other new or future missions (e.g., TROPOMI, FLEX) for estimating terrestrial photosynthesis across a wide variety of biomes and identified their potential and limitations for ecosystem functioning and carbon cycle studies.