Climate-Driven Variability and Trends in Plant Productivity Over Recent Decades Based on Three Global Products

Land use change-induced abrupt changes in vegetation and the role of climate factors

Land use change (LUC) and climate are major factors constraining terrestrial ecosystem. As these impacts are expected to intensify in the future, it is necessary to quantify the effects of anthropogenic LUC on the terrestrial biosphere and its interactions with regional climate. Here, we identify abrupt decreases in the Leaf Area Index (LAI) from 2015 to 2100, primarily driven by LUC through cropland expansion. We further examine the role of climate change in the identified LAI reductions. In areas experiencing LUC, a negative relationship between LAI and surface temperature leads to a positive feedback, which reinforces decreasing LAI and increasing warming. Moreover, prolonged regional dry conditions and a shift towards drier climate conditions further exacerbate LAI reductions. These climate change impacts have a large spatial variation and cause western South America to show the most pronounced LAI sensitivity to LUC under the shared socioeconomic pathways (SSP) 3-7.0 scenario. These results highlight the combined effects of LUC and climate change, leading to future abrupt decreases in vegetation cover.

Decline in Coupling Between Vegetation Photosynthesis and Greening in Northern Ecosystems During the Photosynthesis-Up Period

The maximum seasonal vegetation photosynthesis (Phomax) is crucial to regulating the global carbon dynamics. Of particular importance are the seasonal increments in vegetation photosynthesis (ΔPho), which provide key insights into understanding Phomax. However, the interannual variability of ΔPho within the photosynthesis-up period (PUP) and its influencing factors remain unclear. To address this gap, we identified PUP and quantified the multi-year characteristics of ΔPho using satellite-derived solar-induced chlorophyll fluorescence. We further investigated the response of ΔPho in northern ecosystems to climate change and vegetation greening by integrating climate data and the normalized difference vegetation index. In the northern ecosystems, longer PUP often spatially correlated with a higher ΔPho. An increasing trend was evident regarding the multi-year variations in ΔPho, suggesting enhanced vegetation photosynthesis within the PUP. This phenomenon is primarily driven by increased solar radiation and intensified vegetation greening. Additionally, based on the results derived from satellite data, we found three pieces of evidence for the decoupling trend between vegetation photosynthesis and greening under the influence of climate change: first, the inconsistent trends between ΔPho and greening; second, the declining moving trend in the correlation coefficient between ΔPho and greening, approximately 9.17 × 10-4; and third, the weakened dominant role of greening on ΔPho. These findings were further supported by results from ecosystem model simulations. In summary, this study provides insights into the interannual variability of ΔPho and its influencing factors and indicates that vegetation dynamics and terrestrial carbon cycle are likely to become more complex under future climate change scenarios.

Warming inhibits increases in vegetation net primary productivity despite greening in India

India is the second-highest contributor to the post-2000 global greening. However, with satellite data, here we show that this 18.51% increase in Leaf Area Index (LAI) during 2001-2019 fails to translate into increased carbon uptake due to warming constraints. Our analysis further shows 6.19% decrease in Net Primary Productivity (NPP) during 2001-2019 over the temporally consistent forests in India despite 6.75% increase in LAI. We identify hotspots of statistically significant decreasing trends in NPP over the key forested regions of Northeast India, Peninsular India, and the Western Ghats. Together, these areas contribute to more than 31% of the NPP of India (1274.8 TgC.year-1). These three regions are also the warming hotspots in India. Granger Causality analysis confirms that temperature causes the changes in net-photosynthesis of vegetation. Decreasing photosynthesis and stable respiration, above a threshold temperature, over these regions, as seen in observations, are the key reasons behind the declining NPP. Our analysis shows that warming has already started affecting carbon uptake in Indian forests and calls for improved climate resilient forest management practices in a warming world.

Deeper topsoils enhance ecosystem productivity and climate resilience in arid regions, but not in humid regions

Understanding the controlling mechanisms of soil properties on ecosystem productivity is essential for sustaining productivity and increasing resilience under a changing climate. Here we investigate the control of topsoil depth (e.g., A horizons) on long-term ecosystem productivity. We used nationwide observations (n = 2401) of topsoil depth and multiple scaled datasets of gross primary productivity (GPP) for five ecosystems (cropland, forest, grassland, pasture, shrubland) over 36 years (1986-2021) across the conterminous USA. The relationship between topsoil depth and GPP is primarily associated with water availability, which is particularly significant in arid regions under grassland, shrubland, and cropland (r = .37, .32, .15, respectively, p < .0001). For every 10 cm increase in topsoil depth, the GPP increased by 114 to 128 g C m-2  year-1 in arid regions (r = .33 and .45, p < .0001). Paired comparison of relatively shallow and deep topsoils while holding other variables (climate, vegetation, parent material, soil type) constant showed that the positive control of topsoil depth on GPP occurred primarily in cropland (0.73, confidence interval of 0.57-0.84) and shrubland (0.75, confidence interval of 0.40-0.94). The GPP difference between deep and shallow topsoils was small and not statistically significant. Despite the positive control of topsoil depth on productivity in arid regions, its contribution (coefficients: .09-.33) was similar to that of heat (coefficients: .06-.39) but less than that of water (coefficients: .07-.87). The resilience of ecosystem productivity to climate extremes varied in different ecosystems and climatic regions. Deeper topsoils increased stability and decreased the variability of GPP under climate extremes in most ecosystems, especially in shrubland and grassland. The conservation of topsoil in arid regions and improvements of soil depth representation and moisture-retention mechanisms are critical for carbon-sequestration ecosystem services under a changing climate. These findings and relationships should also be included in Earth system models.

Improved gross primary production estimation in rice fields through integrated multi-scale methodologies

Understanding productivity in agricultural ecosystems is important, as it plays a significant role in modifying regional carbon balances and capturing carbon in the form of agricultural yield. This study in particular combines information from flux determinations using the eddy covariance (EC) methodology, process-based modeling of carbon gain, remotely (satellite) sensed vegetation indices (VIs), and field surveys to assess the gross primary production (GPP) of rice, which is a primary food crop worldwide. This study relates two major variables determining GPP. The first is leaf area index (LAI) and carboxylation capacity of the rice canopy (Vcuptake), and the second being MODIS remotely sensed vegetation indices (VIs). Success in applying such derived relationships has allowed GPP to be remotely determined over the seasonal course of rice development. The relationship to VIs of both LAI and Vcuptake was analyzed first by using the regression approaches commonly applied in remote sensing studies. However, the resultant GPP estimations derived from these generic models were not consistently accurate and led to a large proportion of underestimations. The new, alternative approach developed to estimate LAI and Vcuptake uses consistent development curves for rice (i.e., relies on consistent biological regulations of plant development). The modeled GPP based on this consistent development curve for both LAI and Vcuptake agreed with R 2 from 0.76 to 0.92 (within the 95% confidence interval). The results of this study demonstrate that improved linkages between ground-based survey data, eddy flux measurements, process-based models, and remote sensing data can be constructed to estimate GPP in rice paddies. This study suggests further that the conceptual application of the consistent development curve, such as the combining of different scale measurements, has the potential to predict GPP better than the common practice of utilizing simple linear models, when seeking to estimate the critical parameters that influence carbon gain and agricultural yields.

Photosynthetic capacity dominates the interannual variation of annual gross primary productivity in the Northern Hemisphere

Annual gross primary productivity (AGPP) of terrestrial ecosystems is the largest carbon flux component in ecosystems; however, it's unclear whether photosynthetic capacity or phenology dominates interannual variation of AGPP, and a better understanding of this could contribute to estimation of carbon sinks and their interactions with climate change. In this study, observed GPP data of 494 site-years from 39 eddy covariance sites in Northern Hemisphere were used to investigate mechanisms of interannual variation of AGPP. This study first decomposed AGPP into three seasonal dynamic attribute parameters (growing season length (CUP), maximum daily GPP (GPP_max), and the ratio of mean daily GPP to GPP_max (α_GPP)), and then decomposed AGPP into mean leaf area index (LAI_m) and annual photosynthetic capacity per leaf area (AGPP_lm). Furthermore, GPP_max was decomposed into leaf area index of DOY_max (the day when GPP_max appeared) (LAI_max) and photosynthesis per leaf area of DOY_max (GPP_lmax). Relative contributions of parameters to AGPP and GPP_max were then calculated. Finally, environmental variables of DOY_max were extracted to analyze factors influencing interannual variation of GPP_lmax. Trends of AGPP in 39 ecosystems varied from -65.23 to 53.05 g C m^-2 yr^-2, with the mean value of 6.32 g C m^-2 yr^-2. Photosynthetic capacity (GPP_max and AGPP_lm), not CUP or LAI, was the main factor dominating interannual variation of AGPP. GPP_lmax determined the interannual variation of GPP_max, and temperature, water, and radiation conditions of DOY_max affected the interannual variation of GPP_lmax. This study used the cascade relationship of "environmental variables-GPP_lmax-GPP_max-AGPP" to explain the mechanism of interannual variation of AGPP, which can provide new ideas for the AGPP estimation based on seasonal dynamic of GPP.

Process-oriented analysis of dominant sources of uncertainty in the land carbon sink

The observed global net land carbon sink is captured by current land models. All models agree that atmospheric CO₂ and nitrogen deposition driven gains in carbon stocks are partially offset by climate and land-use and land-cover change (LULCC) losses. However, there is a lack of consensus in the partitioning of the sink between vegetation and soil, where models do not even agree on the direction of change in carbon stocks over the past 60 years. This uncertainty is driven by plant productivity, allocation, and turnover response to atmospheric CO₂ (and to a smaller extent to LULCC), and the response of soil to LULCC (and to a lesser extent climate). Overall, differences in turnover explain ~70% of model spread in both vegetation and soil carbon changes. Further analysis of internal plant and soil (individual pools) cycling is needed to reduce uncertainty in the controlling processes behind the global land carbon sink.

The global net land sink is relatively well constrained. However, the responsible drivers and above/below-ground partitioning are highly uncertain. Model issues regarding turnover of individual plant and soil components are responsible.

Interannual and Seasonal Drivers of Carbon Cycle Variability Represented by the Community Earth System Model (CESM2)

Earth system models are intended to make long-term projections, but they can be evaluated at interannual and seasonal time scales. Although the Community Earth System Model (CESM2) showed improvements in a number of terrestrial carbon cycle benchmarks, relative to its predecessor, our analysis suggests that the interannual variability (IAV) in net terrestrial carbon fluxes did not show similar improvements. The model simulated low IAV of net ecosystem production (NEP), resulting in a weaker than observed sensitivity of the carbon cycle to climate variability. Low IAV in net fluxes likely resulted from low variability in gross primary productivity (GPP)-especially in the tropics-and a high covariation between GPP and ecosystem respiration. Although lower than observed, the IAV of NEP had significant climate sensitivities, with positive NEP anomalies associated with warmer and drier conditions in high latitudes, and with wetter and cooler conditions in mid and low latitudes. We identified two dominant modes of seasonal variability in carbon cycle flux anomalies in our fully coupled CESM2 simulations that are characterized by seasonal amplification and redistribution of ecosystem fluxes. Seasonal amplification of net and gross carbon fluxes showed climate sensitivities mirroring those of annual fluxes. Seasonal redistribution of carbon fluxes is initiated by springtime temperature anomalies, but subsequently negative feedbacks in soil moisture during the summer and fall result in net annual carbon losses from land. These modes of variability are also seen in satellite proxies of GPP, suggesting that CESM2 appropriately represents regional sensitivities of photosynthesis to climate variability on seasonal time scales.