Ecosystem Simulation Model Research Articles

Making the most of available monitoring data: A grid-summarization method to allow for the combined use of monitoring data collected at random and fixed sampling stations

Statistical habitat models, such as spatial generalized linear mixed models (GLMMs) and spatial generalized additive models (GAMs), provide valuable products to habitat assessments and ecosystem-based fisheries management (EBFM) efforts. In particular, derived spatial distribution maps and quantitative relationships of marine organisms to environmental variables (e.g., suitability indices) can be employed to develop or validate ecosystem simulation models. Recent studies fitted spatial GLMMs and spatial GAMs to encounter/non-encounter data collected by different regional monitoring programs that use random sampling designs, so as to enable the production of distribution maps and suitability indices in bulk. However, despite these large efforts, it was not possible to obtain distribution maps for a number of species and life stage combinations, including the juvenile stages of coastal fish species such as croaker (Micropogonias undulatus). In this study, we introduce a grid-summarization method that allows for the combined use of encounter/non-encounter data collected by multiple monitoring programs at random and fixed sampling stations. We demonstrate our grid-summarization method for contrasting species of the western U.S. Gulf of Mexico: red snapper (Lutjanus campechanus), for which data delivered by monitoring programs employing random sampling designs have a satisfactory spatial coverage and the use of monitoring data collected at fixed sampling stations is not necessary; and croaker and brown shrimp (Farfantepenaeus aztecus), for which the combined use of monitoring data collected at random and fixed sampling stations enables or improves the generation of distribution maps. We compare spatial GLMMs and spatial GAMs that rely on the grid-summarization method (“new models”) to spatial GLMMs and spatial GAMs that do not rely on the grid-summarization method (“status-quo models”). We found that the grid-summarization method that allows for the combined use of monitoring data collected at random and fixed sampling stations results in reasonable seasonal distribution maps and suitability indices for the species and life stage combinations (e.g., croaker early juveniles, small brown shrimps) that are undersampled by the monitoring programs that employ random sampling schemes. We also found that the grid-summarization method provides reasonable seasonal distribution maps and suitability relationships for species and life stage combinations (e.g., red snapper adults) for which the status-quo method already provided reasonable results and the combined use of monitoring data collected at random and fixed sampling stations is not necessary. For these species and life stage combinations for which the status-quo method worked well, the choice of the grid-summarization method over the status-quo method depends on whether the fisheries analysts wish to produce smoother distribution maps and whether they target higher predictive accuracy at the expense of lower discrimination accuracy when working with spatial GAMs. Our results suggest that additional monitoring datasets that were previously excluded can be employed by statistical habitat models, thereby enabling generation of distribution maps and suitability indices for a wider range of species, life stage and season combinations.

Direct and Lagged Effects of Spring Phenology on Net Primary Productivity in the Alpine Grasslands on the Tibetan Plateau

As a key biotic factor, phenology exerts fundamental influences on ecosystem carbon sequestration. However, whether spring phenology affects the subsequent seasonal ecosystem productivity and the underlying resource limitation mechanism remains unclear for the alpine grasslands of the Tibetan Plateau (TP). In this study, we investigated the direct and lagged seasonal responses of net primary productivity (NPP) to the beginning of growing season (BGS) along a precipitation gradient by integrating field observations, remote sensing monitoring and ecosystem model simulations. The results revealed distinct response patterns of seasonal NPP to BGS. Specifically, the BGS showed a significant and negative correlation with spring NPP (R = −0.73, p < 0.01), as evidenced by the direct boosting effects of earlier BGS on spring NPP. Moreover, spring NPP was more responsive to BGS in areas with more annual precipitation. The boosting effects of earlier BGS on NPP tended to weaken in summer compared with that in spring. Sequentially, BGS exhibited stronger positive correlation with autumn NPP in areas with less annual precipitation, which suggested the enhanced lagged suppressing effects of earlier spring phenology on ecosystem carbon assimilation during the later growing season under aggravated water stress. Overall, the strengthened NPP in spring was offset by its decrement in autumn, resulting in no obvious relationship between BGS and annual NPP (R = −0.34, p > 0.05) for the entire grasslands on the TP. The findings of this study imply that the lagged effects of phenology on the ecosystem productivity during the subsequent seasons should not be neglected in the future studies.

Effects of using certain tree species in forest regeneration on regional wind damage risks in Finnish boreal forests under different CMIP5 projections

We studied how the use of certain tree species in forest regeneration affected the regional wind damage risks to Finnish boreal forests under the current climate (1981–2010) and recent-generation global climate model (GCM) predictions (i.e., 10 GCMs of CMIP5, with wide variations in temperature and precipitation), using the representative concentration pathways RCP4.5 and RCP8.5 over the period 2010–2099. The study employed forest ecosystem and mechanistic wind damage risk model simulations on upland national forest inventory plots throughout Finland. The amount of wind damage was estimated based on the predicted critical wind speeds for uprooting trees and their probabilities. In a baseline management regime, forest regeneration was performed by planting the same tree species that was dominant before the final cut. In other management regimes, either Scots pine, Norway spruce or silver birch was planted on medium-fertility sites. Other management actions were performed as for a baseline management. The calculated amount of wind damage was greatest in southern and central Finland under CNRM-CM5 RCP8.5, and the smallest under HadGEM2-ES RCP8.5. The most severe climate projections (HadGEM2-ES RCP8.5 and GFDL-CM3 RCP8.5) affected the wind damage risk even more than did the tree species preferences in forest regeneration. The situation was the opposite for the less severe climate projections (e.g., MPI-ESM-MR RCP4.5 and MPI-ESM-MR RCP8.5). The calculated amount of wind damage was clearly greater in the south than in the north, due to differences in forest structure. The volume of growing stock is much higher in the south for the more vulnerable Norway spruce (and birch) than in the north, which is opposite for the less vulnerable Scots pine. The increasing risk of wind damage should be taken into account in forest management because it could amplify, or even cancel out, any expected increases in forest productivity due to climate change.

Spatiotemporal dynamics of ecosystem fires and biomass burning-induced carbon emissions in China over the past two decades

Fire is a major type of disturbance that has important influences on ecosystem dynamics and carbon cycles. Yet our understanding of ecosystem fires and their carbon cycle consequences is still limited, largely due to the difficulty of large-scale fire monitoring and the complex interactions between fire, vegetation, climate, and anthropogenic factors. Here, using data from satellite-derived fire observations and ecosystem model simulations, we performed a comprehensive investigation of the spatial and temporal dynamics of China's ecosystem fire disturbances and their carbon emissions over the past two decades (1997–2016). Satellite-derived results showed that on average about 3.47 - 4.53 × 104 km2 of the land was burned annually during the past two decades, among which annual burned forest area was about 0.81 - 1.25 × 104 km2, accounting for 0.33-0.51% of the forest area in China. Biomass burning emitted about 23.02 TgC per year. Compared to satellite products, simulations from the Energy Exascale Earth System Land Model (ELM) strongly overestimated China's burned area and fire-induced carbon emissions. Annual burned area and fire-induced carbon emissions were high for boreal forest in Northeast China's Daxing'anling region and subtropical dry forest in South Yunnan, as revealed by both the satellite product and the model simulations. Our results suggest that climate and anthropogenic factors play critical roles in controlling the spatial and seasonal distribution of China's ecosystem fire disturbances. Our findings highlight the importance of multiple complementary approaches in assessing ecosystem fire disturbance and its carbon consequences. Further studies are required to improve the methods of observing and modelling China's ecosystem fire disturbances, which will provide valuable information for fire management and ecosystem sustainability in an era when both human activities and the natural environment are rapidly changing.

The microbial loop concept: A history, 1930–1974

The microbial loop as a leading concept in marine microbiology gained wide recognition in the 1980s, but it has roots extending back to the 1930s when microbiologists first began to take a more dynamic approach to investigating the roles of bacteria in ocean food webs and biogeochemical cycles. Here we present a history of the microbial loop concept with emphasis on the period starting in 1930, when marine bacteriologists in Russia and the West began to study explicitly the roles of marine bacteria in the sea. Selman Waksman at Woods Hole and Claude ZoBell at La Jolla relied on colony counts on agar plates as the basis of their work. We suggest that failure to accept direct microscopic evidence of high numbers of bacteria in seawater retarded conceptual development in the West well into the 1970s. Easterners pioneered direct count and radioisotopic techniques and created a dynamic marine microbiology integrating bacteria as important components of marine food webs by the 1960s. Yurii Sorokin and colleagues carried out extensive experimental studies of bacteria as food for marine grazers and provided data for Mikhail Vinogradov and his group to write the first numerical simulation models of ocean ecosystems incorporating microbial components. It had little impact on the Western modeling community, as other Russian work of the times. In spite of continuing technical shortcomings in the field, Lawrence Pomeroy constructed a new conceptual model, providing a synthesis pointing the way toward a modern view of marine microbial ecology that finally matured technically and conceptually in the West in the early 1980s.

Ecosystem models of Lake Victoria (East Africa): Can Ecopath with Ecosim and Atlantis predict similar policy outcomes?

Ecosystem simulation models are valuable quantitative decision tools for supporting ecosystem-based fisheries management. However, the application of ecosystem models in fisheries management is still undermined by the lack of simple procedures to test the effect of model uncertainty on policy outcomes. The use of multiple ecosystem models is viewed as “insurance” against the effects of uncertainty emanating from modelling complex systems, which calls for investigations to ascertain whether models with different structure and assumptions can give consistent policy evaluations. We compared two structurally-distinct ecosystem models, Ecopath with Ecosim (EwE) and Atlantis, for Lake Victoria by varying fishing mortality of the key functional groups: Nile perch (the top predator) and haplochromines (key prey species). We compared model behaviour at the ecosystem level and at the level of functional groups, by evaluating changes in biomass of targeted groups and the consequent effects of changes in target groups on non-target groups. Results showed qualitative similarities (direction of change) for the major harvested groups; however, the cascading effects on non-target species varied across models, depending on the species interaction feedbacks. We conclude that: EwE and Atlantis, despite the huge differences in ecological processes between the models, can give consistent qualitative advice, which is needed for strategic management decisions; consistency in the representation of trophic interactions may help to minimize variations in simulated fishery responses due to model structure. This study helps to highlight scenarios that are robust to model choice, and for which simpler models (such as EwE) could also provide reliable advice.

Effects of intensified silviculture on timber production and its economic profitability in boreal Norway spruce and Scots pine stands under changing climatic conditions

Abstract The aim of this study was to examine how intensified silviculture affects timber production (sawlogs and pulpwood) and its economic profitability (net present value [NPV], with 2 per cent interest rate) based on forest ecosystem model simulations. The study was conducted on Norway spruce and Scots pine stands located on medium-fertile upland forest sites under middle boreal conditions in Finland, under current climate and minor climate change (the RCP2.6 forcing scenario). In intensified silviculture, improved regeneration materials were used, with 10–20 per cent higher growth than the unimproved materials, and/or nitrogen (N) fertilization of 150 kg ha−1, once or twice during a rotation of 50–70 years. Compared to the baseline management regime, the use of improved seedlings, alone or together with N fertilization, increased timber production by up to 26–28 per cent and the NPV by up to 32–60 per cent over rotation lengths of 60–70 years, regardless of tree species (although more in spruce) or climate applied. The use of improved seedlings affected timber yield and NPV more than N fertilization. Minor climate change also increased these outcomes in Scots pine, but not in Norway spruce.

The quasi-equilibrium framework revisited: analyzing long-term CO<sub>2</sub> enrichment responses in plant–soil models

Abstract. Elevated carbon dioxide (CO2) can increase plant growth, but the magnitude of this CO2 fertilization effect is modified by soil nutrient availability. Predicting how nutrient availability affects plant responses to elevated CO2 is a key consideration for ecosystem models, and many modeling groups have moved to, or are moving towards, incorporating nutrient limitation in their models. The choice of assumptions to represent nutrient cycling processes has a major impact on model predictions, but it can be difficult to attribute outcomes to specific assumptions in complex ecosystem simulation models. Here we revisit the quasi-equilibrium analytical framework introduced by Comins and McMurtrie (1993) and explore the consequences of specific model assumptions for ecosystem net primary productivity (NPP). We review the literature applying this framework to plant–soil models and then analyze the effect of several new assumptions on predicted plant responses to elevated CO2. Examination of alternative assumptions for plant nitrogen uptake showed that a linear function of the mineral nitrogen pool or a linear function of the mineral nitrogen pool with an additional saturating function of root biomass yield similar CO2 responses at longer timescales (>5 years), suggesting that the added complexity may not be needed when these are the timescales of interest. In contrast, a saturating function of the mineral nitrogen pool with linear dependency on root biomass yields no soil nutrient feedback on the very-long-term (>500 years), near-equilibrium timescale, meaning that one should expect the model to predict a full CO2 fertilization effect on production. Secondly, we show that incorporating a priming effect on slow soil organic matter decomposition attenuates the nutrient feedback effect on production, leading to a strong medium-term (5–50 years) CO2 response. Models incorporating this priming effect should thus predict a strong and persistent CO2 fertilization effect over time. Thirdly, we demonstrate that using a “potential NPP” approach to represent nutrient limitation of growth yields a relatively small CO2 fertilization effect across all timescales. Overall, our results highlight the fact that the quasi-equilibrium analytical framework is effective for evaluating both the consequences and mechanisms through which different model assumptions affect predictions. To help constrain predictions of the future terrestrial carbon sink, we recommend the use of this framework to analyze likely outcomes of new model assumptions before introducing them to complex model structures.

Imaging spectrometry-derived estimates of regional ecosystem composition for the Sierra Nevada, California

The composition of the plant canopy is a key attribute of terrestrial ecosystems, influencing the fluxes of carbon, water, and energy between the land surface and the atmosphere. Terrestrial ecosystem and biosphere models, which are used to predict how ecosystems are expected to respond to changes in climate, atmospheric CO2, and land-use change, require accurate representations of plant canopy composition at large spatial scales. The ability to accurately specify plant canopy composition is important because it determines the physiological and ecological properties of plants (such as leaf photosynthetic capacity, patterns of plant carbon allocation and tissue turnover, and the resulting dynamics of plant demography) that govern the biophysical and biogeochemical functioning of ecosystems. Traditionally, plant canopy composition has been represented in a coarse-grained manner within terrestrial biosphere models, with ecosystems being comprised of a single plant functional type (PFT). However, models are increasingly seeking to represent fine-scale spatial variation in plant functional diversity. In this study, we show how imaging spectrometry measurements can provide spatially-comprehensive estimates of within-biome heterogeneity in PFT composition across a functionally diverse and topographically heterogeneous ~710 km2 area in the Southern Sierra Mountains of California. AVIRIS (Airborne Visible Infrared Imaging Spectrometer) data at 18 m resolution from the recent HyspIRI Preparatory Mission (Hyperspectral InfraRed Imager) were used to estimate the sub-pixel fractions of seven PFTs represented in the ED2 terrestrial biosphere model: Shrub, Oak, Western Hardwood, Western Pine, Cedar/Fir, and High-elevation Pine, plus a Grass/NPV (Non-Photosynthetic Vegetation) fraction using Multiple Endmember Spectral Mixture Analysis (MESMA). ED2 is an individual-based terrestrial biosphere model capable of representing fine-scale sub-pixel ecosystem heterogeneity. Our results show that this methodology captures important elevation-related shifts in canopy composition that occur within the study area that are not resolved by existing multi-spectral land-cover products. These estimates modestly improved when the putative PFT endmembers considered in the mixture analysis were constrained using available geospatial data about the presence and absence of the PFTs in particular areas: the average RMSEs (root-mean-square errors) with the geospatially-constrained versus conventional method were 11.3% and 11.9% respectively, with larger reductions in the bias (i.e. mean error) in the abundances of Oak, Cedar/Fir, and Western Hardwood PFTs (ranging from 2.0% to 7.8%). At the hectare scale around four flux towers in the Southern Sierra Mountains, the overall composition improved from an RMSE of 18.2% (5.0–24.2% for individual PFTs) to an RMSE of 9.5% (3.3–13.2% for individual PFTs). Downgrading AVIRIS to 30 m resolution resulted in a reduction in accuracy of the constrained method to an RMSE of 12.7% (0–23.7%) with <1% change in the bias for all tree and shrub PFTs. Our results demonstrate that imaging spectrometry measurements from planned satellite missions such as HyspIRI, EnMAP (Environmental Mapping and Analysis Program), and HISUI (Hyper-spectral Imager SUIte) can provide important and much-needed information about fine-scale heterogeneity in the composition of plant canopies for constraining and improving terrestrial ecosystem and biosphere model simulations of regional- and global-scale vegetation dynamics and function.

Drivers of future seasonal cycle changes in oceanic &amp;lt;i&amp;gt;p&amp;lt;/i&amp;gt;CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;

Abstract. Recent observation-based results show that the seasonal amplitude of surface ocean partial pressure of CO2 (pCO2) has been increasing on average at a rate of 2–3 µatm per decade (Landschützer et al., 2018). Future increases in pCO2 seasonality are expected, as marine CO2 concentration ([CO2]) will increase in response to increasing anthropogenic carbon emissions (McNeil and Sasse, 2016). Here we use seven different global coupled atmosphere–ocean–carbon cycle–ecosystem model simulations conducted as part of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to study future projections of the pCO2 annual cycle amplitude and to elucidate the causes of its amplification. We find that for the RCP8.5 emission scenario the seasonal amplitude (climatological maximum minus minimum) of upper ocean pCO2 will increase by a factor of 1.5 to 3 over the next 60–80 years. To understand the drivers and mechanisms that control the pCO2 seasonal amplification we develop a complete analytical Taylor expansion of pCO2 seasonality in terms of its four drivers: dissolved inorganic carbon (DIC), total alkalinity (TA), temperature (T), and salinity (S). Using this linear approximation we show that the DIC and T terms are the dominant contributors to the total change in pCO2 seasonality. To first order, their future intensification can be traced back to a doubling of the annual mean pCO2, which enhances DIC and alters the ocean carbonate chemistry. Regional differences in the projected seasonal cycle amplitude are generated by spatially varying sensitivity terms. The subtropical and equatorial regions (40∘ S–40∘ N) will experience a ≈30–80 µatm increase in seasonal cycle amplitude almost exclusively due to a larger background CO2 concentration that amplifies the T seasonal effect on solubility. This mechanism is further reinforced by an overall increase in the seasonal cycle of T as a result of stronger ocean stratification and a projected shoaling of mean mixed layer depths. The Southern Ocean will experience a seasonal cycle amplification of ≈90–120 µatm in response to the mean pCO2-driven change in the mean DIC contribution and to a lesser extent to the T contribution. However, a decrease in the DIC seasonal cycle amplitude somewhat counteracts this regional amplification mechanism.

Vegetation Greening Despite Weakening Coupling Between Vegetation Growth and Temperature Over the Boreal Region

AbstractThe previously reported strong coupling between interannual variations in vegetation growth and mean growing season temperature in the extratropical (&gt;25°N) Northern Hemisphere (NH) has displayed trends of progressive weakening in recent decades. However, the spatiotemporal patterns and drivers of the weakening coupling and its impacts on vegetation growth across the NH remain poorly understood. Here we address these issues using the vegetation index retrieved from remote sensing, global ecosystem model simulations, and gridded climate data. Our results reveal marked fading of the positive coupling between the mean growing season normalized difference vegetation index (NDVIGS) and mean growing season temperature. Areas with significant positive coupling have shrunk from 31.2% to 20.0% over the periods of 1982–1997 and 1997–2012 across the extratropical NH. Vegetation productivity simulations from global ecosystem models did not capture the above mentioned patterns. There shows a continuous vegetation greening, with a greening rate of 0.0007–0.001 NDVI/year, despite the weakening of the positive coupling in the extratropical NH during the past few decades. Our results highlight that a comprehensive understanding of the underlying processes governing vegetation growth‐climate coupling is urgently needed for accurate predictions of the future trajectories of the response of boreal terrestrial ecosystems to a warmer and more extreme climate regime.

A comprehensive evaluation of pedotransfer functions for predicting soil water content in environmental modeling and ecosystem management

Many pedotransfer functions (PTFs) have been developed for predicting the soil water content at different matric potentials. The use of these functions has been encouraged because of the time and work typically required for measuring it, while the PTFs require commonly measured soil properties such as sand, silt, clay, organic matter content, or bulk density for predicting water retention. In addition, several environmental and ecosystem management simulation models such as DRAINMOD, HYDRUS, EPIC, SPAW, and WEPP use PTFs for computing soil hydraulic properties. Because of the increasing use of the PTFs and their effect in many soil water simulation and transport models, this study revised and tested 13 different PTFs for predicting soil water content at −33 and −1500 kPa, values usually known as field capacity and wilting point. Three of these PTFs were derived from tropical soils while the rest were developed with soil samples collected across the United States. These PTFs were evaluated in Chilean soils as an independent dataset and their improvement after calibration was assessed with this new data. The results demonstrate that the PTFs performance depends on the soils used for their development as the estimates showed a significant improvement after calibration. When predicting water content, Rawls et al. (2004) was the best function before calibration (RMSE = 0.08 for −33 and −1500 kPa), while Gupta and Larson (1979) was the best after calibration (RMSE of 0.06 and 0.05, and r2 values of 0.69 and 0.66 at −33 and −1500 kPa, respectively). Nonlinear PTFs performed better than linear PTFs when predicting water content at field capacity. Finally, bulk density proved to be the key variable and can be used as footprint for soils changes through time. Organic matter content was also a significant input but improved the estimates for some specific matric potentials and PTFs.

Effects of using certain tree species in forest regeneration on volume growth, timber yield, and carbon stock of boreal forests in Finland under different CMIP5 projections

We studied how the use of certain tree species in forest regeneration affected the volume growth, timber yield, and carbon stock of boreal forests in Finland under the current climate (1981–2010) and recent-generation global climate model (GCM) predictions (i.e., multi-model means and individual GCMs of CMIP5), using the representative concentration pathways RCP4.5 and RCP8.5 over the period 2010–2099. Forest ecosystem model simulations were conducted on upland national forest inventory plots throughout Finland. In a baseline management regime, forest regeneration was performed by planting the same tree species that was dominant before the final cut. In alternative management regimes, either Scots pine, Norway spruce, or silver birch were planted on medium-fertility sites. Other management actions over rotation were done as in a baseline management. Compared to baseline management, an increased planting of birch resulted in relative sense highest increase in the volume growth, timber yield, and carbon stock in forests in the south, especially under severe climate projections (e.g., multi-model mean RCP8.5, and GCMs such as HadGEM2-ES RCP8.5 and GFDL-CM3 RCP8.5). This situation was opposite for Norway spruce. In the north, the volume growth, timber yield, and carbon stock of forests increased the most under severe climate projections (e.g., multi-model mean RCP8.5 and CNRM-CM5 RCP8.5), regardless of tree species preference. The magnitude of the climate change impacts depended largely on the geographical region and the severity of the climate projection. Increasing the cultivation of birch and Scots pine, as opposed to Norway spruce, could be recommended for the south. In the north, all three species could be cultivated, regardless of the severity of climate change.

Phenological variation decreased carbon uptake in European forests during 1999–2013

A number of studies have suggested that the duration of a growing season has significantly lengthened during the past decades, but the connections between phenology variability and the terrestrial carbon cycle are far from clear. In this study, we used a process-based ecosystem simulation model, BIOME-BGC, to investigate spatio-temporal variation in phenology and its impacts on carbon fluxes in European forests during 1999–2013. We found that the start of vegetation growing season advanced on average by 0.22 ± 0.55 d yr−1 and the length of growing season extended on average by 0.42 ± 0.86 d yr−1 for the period 1999–2013. Model simulations indicated that European forests acted as a weak carbon (C) sink with a mean value of 0.27 Tg C yr−1 (1 Tg = 1012 g) during 1999–2013. Phenological variation lowered the net ecosystem exchange (NEE) by 3.99 Tg C for the same period, and this could be explained by the opposing effect of enhanced heterotrophic respiration directly induced by the extension of growing season. NEE effects were negatively correlated with heterotrophic respiration (R2 = 0.43), and one Tg increase in the heterotrophic respiration decreased NEE by 2.28 Tg C. The implications for the practical management is that a climate change will result in a significant change of selection pressure, and that phenology is a major aspect of tree functioning that will need adjusting for a future climate.

Effects of CMIP5 Projections on Volume Growth, Carbon Stock and Timber Yield in Managed Scots Pine, Norway Spruce and Silver Birch Stands under Southern and Northern Boreal Conditions

We investigated how recent-generation (CMIP5) global climate model projections affect the volume growth, carbon stock, timber yield and its profitability in managed Scots pine, Norway spruce and Silver birch stands on medium fertile upland sites under southern and northern boreal conditions in Finland. Forest ecosystem model simulations were conducted for the current climate and changing climate, under two representative concentration pathways (RCP4.5 and RCP8.5), using 10 individual global climate model (GCM) projections. In addition to the baseline thinning, we maintained either 20% higher or lower stocking in thinning over a 90-year period. In the south, the severe climate projections, such as HadGEM2-ES RCP8.5 and GFDL-CM3 RCP8.5, as opposed to MPI-ESM-MR RCP4.5, considerably decreased the volume growth, carbon stock and timber yield, as well as its profitability, in Norway spruce stands, but also partially in Scots pine stands, compared to the current climate. Silver birch gained the most from the climate change in the south and Scots pine in the north. The impacts of the thinning regime varied, depending on tree species, site and climate applied. Depending on the severity of the climate change, even opposing adaptive management measures may be needed in different boreal regions.

Weakening Relationship Between Vegetation Growth Over the Tibetan Plateau and Large‐Scale Climate Variability

AbstractVegetation growth on the Tibetan Plateau is strongly affected by large‐scale climate variability, particularly the Pacific Decadal Oscillation (PDO) and the North Atlantic Oscillation (NAO). However, potential temporal changes in both the direction and strength of relationships between regional vegetation growth and large‐scale climate variability remain poorly understood. Here we quantify temporal changes in these relationships during 1982–2012, using satellite‐derived normalized difference vegetation index, global ecosystem model simulations of net primary productivity, regional tree ring chronologies, and PDO and NAO indexes. We found consistent weakening relationships between mean growing‐season (April–October) normalized difference vegetation index and both PDO and NAO. A similar pattern was also found in the temporal relationship between net primary productivity and PDO. Such weakening relationships were partly attributable to weakening regional summer atmospheric circulation and its causal effects on changes in hydrothermal conditions over the Tibetan Plateau. These findings highlight a varying coupling of regional vegetation growth to large‐scale climate variability at the interannual/decadal scale during past decades, and this feature should be considered in future prediction of terrestrial vegetation growth in response to shifting climate regime.

Velocity of change in vegetation productivity over northern high latitudes

Warming is projected to increase the productivity of northern ecosystems. However, knowledge on whether the northward displacement of vegetation productivity isolines matches that of temperature isolines is still limited. Here we compared changes in the spatial patterns of vegetation productivity and temperature using the velocity of change concept, which expresses these two variables in the same unit of displacement per time. We show that across northern regions (>50°N), the average velocity of change in growing-season normalized difference vegetation index (NDVIGS, an indicator of vegetation productivity; 2.8 ± 1.1 km yr-1) is lower than that of growing-season mean temperature (T GS; 5.4 ± 1.0 km yr-1). In fact, the NDVIGS velocity was less than half of the T GS velocity in more than half of the study area, indicating that the northward movement of productivity isolines is much slower than that of temperature isolines across the majority of northern regions (about 80% of the area showed faster changes in temperature than productivity isolines). We tentatively attribute this mismatch between the velocities of productivity and temperature to the effects of limited resource availability and vegetation acclimation mechanisms. Analyses of ecosystem model simulations further suggested that limited nitrogen availability is a crucial obstacle for vegetation to track the warming trend.

Rapid 21st century climate change projected to shift composition and growth of Canada’s Acadian Forest Region

Abstract The impact of climate change on forests is expected to vary globally and regionally. Canada’s Acadian Forest Region lies in the transition between the North American boreal and temperate forest biomes and may be particularly sensitive to changes in climate because many of its component species are currently at their southern or northern climatic range limits. Although some species may be lost, others may exhibit major productivity boosts—affecting the goods and services we derive from them. In this study, we use a well-established forest ecosystem simulation model, PICUS, to provide the first exploration of the impact of climate change on the composition and growth of the Acadian Forest Region for the period 2011 to 2100 under two radiative forcing scenarios, RCP 2.6 and RCP 8.5. In the short term (2011–2040), little to no changes in forest composition or growth were projected under either forcing scenario compared with current forest conditions (simulated for 1981–2010 baseline climate); however, by mid-century, PICUS projected increasing departures from the baseline simulations in both composition and growth, with the greatest changes occurring under RCP 8.5 during the late 21st century (2071–2100). Our study indicates that under rapid 21st century warming, Canada’s Acadian Forest Region will begin to lose its boreal character (i.e., “deborealize”) as key tree species fail to regenerate and survive. Furthermore, increased growth and establishment by warm-adapted, temperate tree species may be unable to keep pace with the rapid loss of boreal species. This potential “lag effect” may lead to a temporary decrease in forest growth and wood supply during the late 21st century.

Contrasting and interacting changes in simulated spring and summer carbon cycle extremes in European ecosystems

Climate extremes have the potential to cause extreme responses of terrestrial ecosystem functioning. However, it is neither straightforward to quantify and predict extreme ecosystem responses, nor to attribute these responses to specific climate drivers. Here, we construct a factorial experiment based on a large ensemble of process-oriented ecosystem model simulations driven by a regional climate model (12 500 model years in 1985–2010) in six European regions. Our aims are to (1) attribute changes in the intensity and frequency of simulated ecosystem productivity extremes (EPEs) to recent changes in climate extremes, CO2 concentration, and land use, and to (2) assess the effect of timing and seasonal interaction on the intensity of EPEs. Evaluating the ensemble simulations reveals that (1) recent trends in EPEs are seasonally contrasting: spring EPEs show consistent trends towards increased carbon uptake, while trends in summer EPEs are predominantly negative in net ecosystem productivity (i.e. higher net carbon release under drought and heat in summer) and close-to-neutral in gross productivity. While changes in climate and its extremes (mainly warming) and changes in CO2 increase spring productivity, changes in climate extremes decrease summer productivity neutralizing positive effects of CO2. Furthermore, we find that (2) drought or heat wave induced carbon losses in summer (i.e. negative EPEs) can be partly compensated by a higher uptake in the preceding spring in temperate regions. Conversely, however, carry-over effects from spring to summer that arise from depleted soil moisture exacerbate the carbon losses caused by climate extremes in summer, and are thus undoing spring compensatory effects. While the spring-compensation effect is increasing over time, the carry-over effect shows no trend between 1985–2010. The ensemble ecosystem model simulations provide a process-based interpretation and generalization for spring-summer interacting carbon cycle effects caused by climate extremes (i.e. compensatory and carry-over effects). In summary, the ensemble ecosystem modelling approach presented in this paper offers a novel route to scrutinize ecosystem responses to changing climate extremes in a probabilistic framework, and to pinpoint the underlying eco-physiological mechanisms.

Higher temperature variability reduces temperature sensitivity of vegetation growth in Northern Hemisphere

AbstractInterannual air temperature variability has changed over some regions in Northern Hemisphere (NH), accompanying with climate warming. However, whether and to what extent it regulates the interannual sensitivity of vegetation growth to temperature variability (i.e., interannual temperature sensitivity)—one central issue in understanding and predicting the responses of vegetation growth to changing climate—still remains poorly quantified and understood. Here we quantify the relationships between the interannual temperature sensitivity of mean growing‐season (April–October) normalized difference vegetation index (NDVI) and ecosystem model simulations of gross primary productivity (GPP), and variability in mean growing‐season temperature for forest, shrub, and grass over NH. We find that higher interannual variability in mean growing‐season temperature leads to consistent decrease in interannual temperature sensitivity of mean growing‐season NDVI among all vegetation types but not in model simulations of GPP. Drier condition associates with ~130 ± 150% further decrease in interannual temperature sensitivity of mean growing‐season NDVI by temperature variability in forest and shrub. These results illustrate that varying temperature variability can significantly regulate the interannual temperature sensitivity of vegetation growth over NH, interacted with drought variability and nonlinear responses of photosynthesis to temperature. Our findings call for an improved characterization of the nonlinear effects of temperature variability on vegetation growth within global ecosystem models.