Converging Climate Sensitivities of European Forests Between Observed Radial Tree Growth and Vegetation Models

Increasing atmospheric dryness reduces boreal forest tree growth

Rising atmospheric vapour pressure deficit (VPD) associated with climate change affects boreal forest growth via stomatal closure and soil dryness. However, the relationship between VPD and forest growth depends on the climatic context. Here we assess Canadian boreal forest responses to VPD changes from 1951-2018 using a well-replicated tree-growth increment network with approximately 5,000 species-site combinations. Of the 3,559 successful growth models, we observed a relationship between growth and concurrent summer VPD in one-third of the species-site combinations, and between growth and prior summer VPD in almost half of those combinations. The relationship between previous year VPD and current year growth was almost exclusively negative, while current year VPD also tended to reduce growth. Tree species, age, annual temperature, and soil moisture primarily determined tree VPD responses. Younger trees and species like white spruce and Douglas fir exhibited higher VPD sensitivity, as did areas with high annual temperature and low soil moisture. Since 1951, summer VPD increases in Canada have paralleled tree growth decreases, particularly in spruce species. Accelerating atmospheric dryness in the decades ahead will impair carbon storage and societal-economic services.

New tree-ring data from Canadian boreal and hemi-boreal forests provide insight for improving the climate sensitivity of terrestrial biosphere models

Understanding boreal/hemi-boreal forest growth sensitivity to seasonal variations in temperature and water availability provides important basis for projecting the potential impacts of climate change on the productivity of these ecosystems. Our best available information currently comes from a limited number of field experiments and terrestrial biosphere model (TBM) simulations of varying predictive accuracy. Here, we assessed the sensitivity of annual boreal/hemi-boreal forest growth in Canada to yearly fluctuations in seasonal climate variables using a large tree-ring dataset and compared this to the climate sensitivity of annual net primary productivity (NPP) estimates obtained from fourteen TBMs. We found that boreal/hemi-boreal forest growth sensitivity to fluctuations in seasonal temperature and precipitation variables changed along a southwestern to northeastern gradient, with growth limited almost entirely by temperature in the northeast and west and by water availability in the southwest. We also found a lag in growth climate sensitivity, with growth largely determined by the climate during the summer prior to ring formation. Analyses of NPP sensitivity to the same climate variables produced a similar southwest to northeast gradient in growth climate sensitivity for NPP estimates from all but three TBMs. However, analyses of growth from tree-ring data and analyses of NPP from TBMs produced contrasting evidence concerning the key climate variables limiting growth. While analyses of NPP primarily indicated a positive relationship between growth and seasonal temperature, tree-ring analyses indicated negative growth relationships to temperature. Also, the positive effect of precipitation on NPP derived from most TBMs was weaker than the positive effect of precipitation on tree-ring based growth: temperature had a more important limiting effect on NPP than tree-ring data indicated. These mismatches regarding the key climate variables limiting growth suggested that characterization of tree growth in TBMs might need revision, particularly regarding the effects of stomatal conductance and carbohydrate reserve dynamics.

Run to the hills: Forest growth responsiveness to drought increased at higher elevation during the late 20th century

Climate warming is expected to enhance forest growth in cold-limited biomes while triggering reductions in drought-limited biomes. However, as temperature raises, it is unclear how temperature- and drought-growth couplings shift across elevation gradients in different biomes. We still lack comprehensive analyses on how altitude modulates the influence of temperature and drought on tree growth during the second half of the 20th century when climate warming accelerated. We compared the worldwide responses of tree growth (RWI, ring-width indices) to two of its major climatic constraints, growing-season minimum temperatures and drought (SPEI index), across biomes and elevation gradients during two periods with different warming rates (1960-1980 vs. 1980-2000). We found a decrease in the correlations of minimum temperatures with growth, but a strengthening of drought-growth relationships. However, these patterns varied across biomes because correlations between growth and temperature decreased in temperate forests and woodland shrubland, while correlations between growth and SPEI increased in boreal forests and decreased in temperate forests. Differences in growth responsiveness to climate between the two periods were more marked for mid-latitude forests situated between 1200 and 1600 m. The slopes of the relationships between growth-temperature correlations and elevation decreased in late spring and midsummer. The slopes of the relationships between growth-drought correlations and elevation increased in temperate forests and woodland shrubland suggesting that drought impacts are "climbing" in these biomes. Temperature controls on forest growth are relaxing as the climate warms, while drought is becoming a more significant constraint for tree growth, particularly for mid-elevation forests and in drought-prone woodland and shrubland. The strengthening of drought-growth coupling should be considered in vegetation models to reduce the uncertainty on forest climate mitigation.

Excessive positive response of model-simulated land net primary production to climate changes over circumboreal forests

Land carbon cycle components in an Earth system model (ESM) play a crucial role in the projections of forest ecosystem responses to climate/environmental changes. Evaluating models from the viewpoint of observations is essential for an improved understanding of model performance and for identifying uncertainties in their outputs. Herein, we evaluated the land net primary production (NPP) for circumboreal forests simulated with 10 ESMs in Phase 5 of the Coupled Model Intercomparison Project by comparisons with observation-based indexes for forest productivity, namely, the composite version 3G of the normalized difference vegetation index (NDVI3g) and tree-ring width index (RWI). These indexes show similar patterns in response to past climate change over the forests, i.e., a one-year time lag response and smaller positive responses to past climate changes in comparison with the land NPP simulated by the ESMs. The latter showed overly positive responses to past temperature and/or precipitation changes in comparison with the NDVI3g and RWI. These results indicate that ESMs may overestimate the future forest NPP of circumboreal forests (particularly for inland dry regions, such as inner Alaska and Canada, and eastern Siberia, and for hotter, southern regions, such as central Europe) under the expected increases in both average global temperature and precipitation, which are common to all current ESMs.

Can more carbon be captured by grasslands? A case study of Inner Mongolia, China

Grasslands cover a large part of the Earth's surface and play an important role in the global carbon cycle. Previous studies have indicated that nearly half of the grassland vegetation cover has experienced degradation on a global scale; if this degradation is reversed, grasslands can act as potential carbon sinks. However, the question of how much more carbon (carbon gap) could be sequestrated by grassland vegetation by regulating human activities remains unanswered. Here, we present an innovative approach to assess the achievable carbon gap through focal analysis of long-term Moderate Resolution Imaging Spectroradiometer (MODIS) Net Primary Production (NPP) dataset or observed NPP (ONPP). In focal analysis, region segmentation was done to produce spatially homogeneous patches of the same types of soil, topography, and vegetation, referred to as S-T-V units, to minimize the variation in environmental conditions and their impacts on the NPP. Then, the ONPP within each S-T-V unit was rectified by offsetting the variations in potential NPP determined by the climate-oriented Miami NPP model. Hence, spatial variations in the climate-rectified ONPP (ONPP_CR) in an S-T-V unit were solely determined by different human activities across locations. In a case study of the Inner Mongolia grassland of China, three focal statistics, namely mean (Mean), 95% percentile threshold (95%PCT), and maximum (Max) within each S-T-V unit were computed for ONPP_CR for each year from 2000 to 2014 to assess the annual carbon uptake that was achievable by updating grassland management practices. The carbon gaps were assessed to be 11.8, 58.9, and 74.6 gC/m² per year based on Mean, 95%PCT, and Max, respectively, compared to 65.0 gC/m² per year based on the traditional pixel-based approach. We conclude that the carbon gap patterns identified from focal analysis are practically achievable and are more valuable in formulating policy-related decisions for grassland management. Implementing sustainable management practices that are currently being practiced at locations with high ONPP_CR in neighboring degraded areas is expected to increase the carbon sequestration by grassland vegetation by one-third.

Towards better representations of carbon allocation in vegetation: a conceptual framework and mathematical tool

The representation of carbon allocation (CA) in ecosystem differs tremendously among models, resulting in diverse responses of carbon cycling and storage to global change. Several studies have highlighted discrepancies between empirical observations and model predictions, attributing these differences to problems of model structure. We analyzed the mathematical representation of CA in models using concepts from dynamical systems theory; we reviewed a representative sample of models of CA in vegetation and developed a model database within the Python package bgc-md. We asked whether these representations can be generalized as a linear system, or whether a more general framework is needed to accommodate nonlinearities. Some of the vegetation systems simulated with the reviewed models have a fixed partitioning of photosynthetic products, independent of environmental forcing. Vegetation is often represented as a linear system without storage compartments. Yet, other structures with nonlinearities have also been proposed, with important consequences on the temporal trajectories of ecosystem carbon compartments. The proposed mathematical framework unifies the representation of alternative CA schemes, facilitating their classification according to mathematical properties as well as their potential temporal behaviour. It can represent complex processes in a compact form, which can potentially facilitate dialog among empiricists, theoreticians, and modellers.

Assessing the response of forest productivity to climate extremes in Switzerland using model-data fusion

The response of forest productivity to climate extremes strongly depends on ambient environmental and site conditions. To better understand these relationships at a regional scale, we used nearly 800 observation years from 271 permanent long-term forest monitoring plots across Switzerland, obtained between 1980 and 2017. We assimilated these data into the 3-PG forest ecosystem model using Bayesian inference, reducing the bias of model predictions from 14% to 5% for forest stem carbon stocks and from 45% to 9% for stem carbon stock changes. We then estimated the productivity of forests dominated by Picea abies and Fagus sylvatica for the period of 1960-2018, and tested for productivity shifts in response to climate along elevational gradient and in extreme years. Simulated net primary productivity (NPP) decreased with elevation (2.86 ± 0.006 Mg C ha-1  year-1  km-1 for P. abies and 0.93 ± 0.010 Mg C ha-1  year-1  km-1 for F. sylvatica). During warm-dry extremes, simulated NPP for both species increased at higher and decreased at lower elevations, with reductions in NPP of more than 25% for up to 21% of the potential species distribution range in Switzerland. Reduced plant water availability had a stronger effect on NPP than temperature during warm-dry extremes. Importantly, cold-dry extremes had negative impacts on regional forest NPP comparable to warm-dry extremes. Overall, our calibrated model suggests that the response of forest productivity to climate extremes is more complex than simple shift toward higher elevation. Such robust estimates of NPP are key for increasing our understanding of forests ecosystems carbon dynamics under climate extremes.

Land carbon models underestimate the severity and duration of drought's impact on plant productivity

The ability to accurately predict ecosystem drought response and recovery is necessary to produce reliable forecasts of land carbon uptake and future climate. Using a suite of models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), we assessed modeled net primary productivity (NPP) response to, and recovery from, drought events against a benchmark derived from tree ring observations between 1948 and 2008 across forested regions of the US and Europe. We find short lag times (0-6 months) between climate anomalies and modeled NPP response. Although models accurately simulate the direction of drought legacy effects (i.e. NPP decreases), projected effects are approximately four times shorter and four times weaker than observations suggest. This discrepancy between observed and simulated vegetation recovery from drought reveals a potential critical model deficiency. Since productivity is a crucial component of the land carbon balance, models that underestimate drought recovery time could overestimate predictions of future land carbon sink strength and, consequently, underestimate forecasts of atmospheric CO₂.

Twentieth century redistribution in climatic drivers of global tree growth

Energy and water limitations of tree growth remain insufficiently understood at large spatiotemporal scales, hindering model representation of interannual or longer-term ecosystem processes. By assessing and statistically scaling the climatic drivers from 2710 tree-ring sites, we identified the boreal and temperate land areas where tree growth during 1930-1960 CE responded positively to temperature (20.8 ± 3.7 Mio km²; 25.9 ± 4.6%), precipitation (77.5 ± 3.3 Mio km²; 96.4 ± 4.1%), and other parameters. The spatial manifestation of this climate response is determined by latitudinal and altitudinal temperature gradients, indicating that warming leads to geographic shifts in growth limitations. We observed a significant (P < 0.001) decrease in temperature response at cold-dry sites between 1930-1960 and 1960-1990 CE, and the total temperature-limited area shrunk by -8.7 ± 0.6 Mio km². Simultaneously, trees became more limited by atmospheric water demand almost worldwide. These changes occurred under mild warming, and we expect that continued climate change will trigger a major redistribution in growth responses to climate.

A Wood Biology Agenda to Support Global Vegetation Modelling

Realistic forecasting of forest responses to climate change critically depends on key advancements in global vegetation modelling. Compared with traditional 'big-leaf' models that simulate forest stands, 'next-generation' vegetation models aim to track carbon-, light-, water-, and nutrient-limited growth of individual trees. Wood biology can play an important role in delivering the required knowledge at tissue-to-individual levels, at minute-to-century scales and for model parameterization and benchmarking. We propose a wood biology research agenda that contributes to filling six knowledge gaps: sink versus source limitation, drivers of intra-annual growth, drought impacts, functional wood traits, dynamic biomass allocation, and nutrient cycling. Executing this agenda will expedite model development and increase the ability of models to forecast global change impact on forest dynamics.