A multiyear evaluation of a Dynamic Global Vegetation Model at three AmeriFlux forest sites: Vegetation structure, phenology, soil temperature, and CO2 and H2O vapor exchange

Accounting for herbaceous communities in process-based models will advance our understanding of "grassy" ecosystems

Grassland and other herbaceous communities cover significant portions of Earth's terrestrial surface and provide many critical services, such as carbon sequestration, wildlife habitat, and food production. Forecasts of global change impacts on these services will require predictive tools, such as process-based dynamic vegetation models. Yet, model representation of herbaceous communities and ecosystems lags substantially behind that of tree communities and forests. The limited representation of herbaceous communities within models arises from two important knowledge gaps: first, our empirical understanding of the principles governing herbaceous vegetation dynamics is either incomplete or does not provide mechanistic information necessary to drive herbaceous community processes with models; second, current model structure and parameterization of grass and other herbaceous plant functional types limits the ability of models to predict outcomes of competition and growth for herbaceous vegetation. In this review, we provide direction for addressing these gaps by: (1) presenting a brief history of how vegetation dynamics have been developed and incorporated into earth system models, (2) reporting on a model simulation activity to evaluate current model capability to represent herbaceous vegetation dynamics and ecosystem function, and (3) detailing several ecological properties and phenomena that should be a focus for both empiricists and modelers to improve representation of herbaceous vegetation in models. Together, empiricists and modelers can improve representation of herbaceous ecosystem processes within models. In so doing, we will greatly enhance our ability to forecast future states of the earth system, which is of high importance given the rapid rate of environmental change on our planet.

Perennial grassland agriculture restores critical ecosystem functions in the U.S. Upper Midwest

Dominant forms of agricultural production in the U.S. Upper Midwest are undermining human health and well being. Restoring critical ecosystem functions to agriculture is key to stabilizing climate, reducing flooding, cleaning water, and enhancing biodiversity. We used simulation models to compare ecosystem functions (food-energy production, nutrient retention, and water infiltration) provided by vegetation associated with continuous corn, corn-soybean rotation, and perennial grassland producing feed for dairy livestock. Compared to continuous corn, most ecosystem functions dramatically improved in the perennial grassland system (nitrate leaching reduced ~90%, phosphorus loss reduced ~88%, drainage increased ~25%, evapotranspiration reduced ~29%), which will translate to improved ecosystem services. Our results emphasize the need to incentivize multiple ecosystem services when managing agricultural landscapes.

Species differences in the green-up date of typical vegetation in Inner Mongolia and climate-driven mechanism based on process-based phenology models

Different species within the same community may exhibit distinct phenological responses to climate change, so it is necessary to study species differences in the green-up date among abundant species within a wide area, and a suitable phenology model should be introduced to explain the associated climate-driven mechanism. Although various models have been developed, very few studies have aimed to compare their efficiency and robustness, and the relative contributions of climate driving factors have not been sufficiently examined. We analyzed phenology data for 12 species across 17 stations in Inner Mongolia and found that essential spatiotemporal and interspecies differences existed in the green-up date. Five process-based models were established for each species and their performance was comprehensively evaluated. The two-phase models (sequential model, parallel model, unified model and unified model combined with precipitation driving) generally performed better than the one-phase model (thermal time model), and the model considering precipitation performed the best, which indicates that it is necessary to introduce the chilling effect and precipitation driving effect to improve the model accuracy in arid environments. We proposed a method to estimate the contribution rates of various climate driving factors, and significant differences in the relative demand for the various climate driving factors among different species were clearly revealed. The results indicated that for natural vegetation in Inner Mongolia, the need for the chilling and temperature driving is relatively high, and the precipitation driving is very important for herbaceous vegetation, which leads to considerable spatial and interspecies differences in green-up date. We demonstrated the feasibility of quantitatively evaluating the contributions of different climate driving factors with a process-based model, and the contradiction in phenological changes among different studies may eventually be clarified.

A Multi-Perspective Assessment Method with a Dynamic Benchmark for Human Activity Impacts on Alpine Ecosystem under Climate Change

Intense human activities and rapid climate changes both have obvious impacts on alpine ecosystems. However, the magnitudes and directions of the impacts by these two drivers remain uncertain due to a lack of a reasonable assessment method to distinguish between them. The impact of natural resilience is also generally included in the dynamics of a disturbed ecosystem and is liable to be mixed into the impact of human activity. It is urgent that we quantitatively discriminate human activity impacts on the ecosystem under climate change, especially for fast-developing alpine regions. Here, we propose an assessment method to determine human activity impacts under a dynamic climate, taking the potential net primary production (NPP) of an ecosystem as a benchmark. The potential NPP (NPPP) series under the changing climate was retrieved by an improved integrated biosphere simulator based on the initial disturbed ecosystem status of the assessment period. The actual NPP (NPPA) series monitored by remote sensing was considered as the results derived from the joint impacts of climate change, natural resilience and human activity. Then, the impact of human activity was quantified as the difference between the NPPP and NPPA. The contributions of human activity and natural forces to ecosystem NPP dynamics were then calculated separately and employed to explore the dominant driver(s). This assessment method was demonstrated in a typical alpine ecosystem in Northwest China. The results indicate that this method capably revealed the positive impacts of local afforestation and land-use optimization and the negative impacts caused by grazing during the assessment period of 2001–2017. This assessment method provides a quantitative reference for assessing the performances of ecological protections or human damage to alpine ecosystems at the regional scale.

Assessment of Seasonal Variability of Extreme Temperature in Mainland China under Climate Change

Some studies have suggested that variations in the seasonal cycle of temperature and season onset could affect the efficiency in the use of radiation by plants, which would then affect yield. However, the study of the temporal variation in extreme climatic variables is not sufficient in China. Using seasonal trend analysis (STA), this article evaluates the distribution of extreme temperature seasonality trends in mainland China, describes the trends in the seasonal cycle, and detects changes in extreme temperature characterized by the number of hot days (HD) and frost days (FD), the frequency of warm days (TX90p), cold days (TX10p), warm nights (TN90p), and cold nights (TN10p). The results show a statistically significant positive trend in the annual average amplitudes of extreme temperatures. The amplitude and phase of the annual cycle experience less variation than that of the annual average amplitude for extreme temperatures. The phase of the annual cycle in maximum temperature mainly shows a significant negative trend, accounting for approximately 30% of the total area of China, which is distributed across the regions except for northeast and southwest. The amplitude of the annual cycle indicates that the minimum temperature underwent slightly greater variation than the maximum temperature, and its distribution has a spatial characteristic that is almost bounded by the 400 mm isohyet, increasing in the northwest and decreasing in the southeast. In terms of the extreme air temperature indices, HD, TX90p, and TN90p show an increasing trend, FD, TX10p, and TN10p show a decreasing trend. They are statistically significant (p < 0.05). This number of days also suggests that temperature has increased over mainland China in the past 42 years.

Fine-Scale Analysis of the Energy-Land-Water Nexus: Nitrate Leaching Implications of Biomass Cofiring in the Midwestern United States

As scientists seek to better understand the linkages between energy, water, and land systems, they confront a critical question of scale for their analysis. Many studies exploring this nexus restrict themselves to a small area in order to capture fine-scale processes, whereas other studies focus on interactions between energy, water, and land over broader domains but apply coarse resolution methods. Detailed studies of a narrow domain can be misleading if the policy intervention considered is broad-based and has impacts on energy, land, and agricultural markets. Regional studies with aggregate low-resolution representations may miss critical feedbacks driven by the dynamic interactions between subsystems. This study applies a novel, gridded energy-land-water modeling system to analyze the local environmental impacts of biomass cofiring of coal power plants across the upper MISO region. We use this framework to examine the impacts of a hypothetical biomass cofiring technology mandate of coal-fired power plants using corn residues. We find that this scenario has a significant impact on land allocation, fertilizer applications, and nitrogen leaching. The effects also impact regions not involved in cofiring through agricultural markets. Further, some MISO coal-fired plants would cease generation because the competition for biomass increases the cost of this feedstock and because the higher operating costs of cofiring renders them uncompetitive with other generation sources. These factors are not captured by analyses undertaken at the level of an individual power plant. We also show that a region-wide analysis of this cofiring mandate would have registered only a modest increase in nitrate leaching (just +5% across the upper MISO region). Such aggregate analyses would have obscured the extremely large increases in leaching at particular locations, as much as +60%. Many of these locations are already pollution hotspots. Fine-scale analysis, nested within a broader framework, is necessary to capture these critical environmental interactions within the energy, land, and water nexus.

Comparing the effects of climate and land use on surface water quality using future watershed scenarios

Eutrophication of freshwaters occurs in watersheds with excessive pollution of phosphorus (P). Factors that affect P cycling and transport, including climate and land use, are changing rapidly and can have legacy effects, making future freshwater quality uncertain. Focusing on the Yahara Watershed (YW) of southern Wisconsin, USA, an intensive agricultural landscape, we explored the relative influence of land use and climate on three indicators of water quality over a span of 57 years (2014-2070). The indicators included watershed-averaged P yield from the land surface, direct drainage P loads to a lake, and average summertime lake P concentration. Using biophysical model simulations of future watershed scenarios, we found that climate exerted a stronger influence than land use on all three indicators, yet land use had an important role in influencing long term outcomes for each. Variations in P yield due to land use exceeded those due to climate in 36 of 57 years, whereas variations in load and lake total P concentration due to climate exceeded those due to land use in 54 of 57 years, and 52 of 57 years, respectively. The effect of land use was thus strongest for P yield off the landscape and attenuated in the stream and lake aquatic systems where the influence of weather variability was greater. Overall these findings underscore the dominant role of climate in driving inter-annual nutrient fluxes within the hydrologic network and suggest a challenge for land use to influence water quality within streams and lakes over timescales less than a decade. Over longer timescales, reducing applications of P throughout the watershed was an effective management strategy under all four climates investigated, even during decades with wetter conditions and more frequent extreme precipitation events.

Interpretation of vegetation phenology changes using daytime and night-time temperatures across the Yellow River Basin, China

Vegetation phenology is highly sensitive to climate change and has a crucial effect on the carbon balance. Prior studies have mainly investigated the effects of mean temperature and precipitation on phenology. The asymmetric and opposing response of phenology to daytime and night-time temperature remains largely unknown. Using the satellite phenology derived from GIMMS NDVI_3g datasets dating back to the 1980s, we show that significantly advanced start of the season (SOS), delayed end of the season (EOS) and prolonged length of growing season (LEN) (P < 0.05) has been taking place in the Yellow River Basin in China. The extension of LEN was more attributed to the advance of SOS than a delayed EOS. The daytime T_max and night-time T_min had opposite effects on the timing of SOS, MOS, and EOS in 63.1%,40.0%, and 53.5% of the pixels of the study area, respectively. If higher T_max leads to an earlier or later transition date, an increase in T_min systematically leads to an opposite effect. These opposite effects were obvious in SOS of 70.5%, 66.2%, and 70.6% of shrubland, grassland, and crop fields, respectively. For EOS, the opposing effects accounting for 58.2%,60.2%, and 54.5% of forest land, shrubland, and grassland, respectively. These results reveal different impacts of climate change on the daytime and night-time carbon cycle in terrestrial ecosystems, and such impacts vary with the land surface type. Knowledge of these opposing responses of phenology to daytime and night-time warming may help to understand the feedback of terrestrial ecosystem structure and function to climate change, thus to improve the existing terrestrial ecosystem carbon cycle model, which is of great significance for climate change and ecology research.

Underestimates of Grassland Gross Primary Production in MODIS Standard Products

As the biggest carbon flux of terrestrial ecosystems from photosynthesis, gross primary productivity (GPP) is an important indicator in understanding the carbon cycle and biogeochemical process of terrestrial ecosystems. Despite advances in remote sensing-based GPP modeling, spatial and temporal variations of GPP are still uncertain especially under extreme climate conditions such as droughts. As the only official products of global spatially explicit GPP, MOD17A2H (GPPMOD) has been widely used to assess the variations of carbon uptake of terrestrial ecosystems. However, systematic assessment of its performance has rarely been conducted especially for the grassland ecosystems where inter-annual variability is high. Based on a collection of GPP datasets (GPPEC) from a global network of eddy covariance towers (FluxNet), we compared GPPMOD and GPPEC at all FluxNet grassland sites with more than five years of observations. We evaluated the performance and robustness of GPPMOD in different grassland biomes (tropical, temperate, and alpine) by using a bootstrapping method for calculating 95% confident intervals (CI) for the linear regression slope, coefficients of determination (R2), and root mean square errors (RMSE). We found that GPPMOD generally underestimated GPP by about 34% across all biomes despite a significant relationship (R2 = 0.66 (CI, 0.63–0.69), RMSE = 2.46 (2.33–2.58) g Cm−2 day−1) for the three grassland biomes. GPPMOD had varied performances with R2 values of 0.72 (0.68–0.75) (temperate), 0.64 (0.59–0.68) (alpine), and 0.40 (0.27–0.52) (tropical). Thus, GPPMOD performed better in low GPP situations (e.g., temperate grassland type), which further indicated that GPPMOD underestimated GPP. The underestimation of GPP could be partly attributed to the biased maximum light use efficiency (εmax) values of different grassland biomes. The uncertainty of the fraction of absorbed photosynthetically active radiation (FPAR) and the water scalar based on the vapor pressure deficit (VPD) could have other reasons for the underestimation. Therefore, more accurate estimates of GPP for different grassland biomes should consider improvements in εmax, FPAR, and the VPD scalar. Our results suggest that the community should be cautious when using MODIS GPP products to examine spatial and temporal variations of carbon fluxes.

Drivers and mechanisms of tree mortality in moist tropical forests

Tree mortality rates appear to be increasing in moist tropical forests (MTFs) with significant carbon cycle consequences. Here, we review the state of knowledge regarding MTF tree mortality, create a conceptual framework with testable hypotheses regarding the drivers, mechanisms and interactions that may underlie increasing MTF mortality rates, and identify the next steps for improved understanding and reduced prediction. Increasing mortality rates are associated with rising temperature and vapor pressure deficit, liana abundance, drought, wind events, fire and, possibly, CO₂ fertilization-induced increases in stand thinning or acceleration of trees reaching larger, more vulnerable heights. The majority of these mortality drivers may kill trees in part through carbon starvation and hydraulic failure. The relative importance of each driver is unknown. High species diversity may buffer MTFs against large-scale mortality events, but recent and expected trends in mortality drivers give reason for concern regarding increasing mortality within MTFs. Models of tropical tree mortality are advancing the representation of hydraulics, carbon and demography, but require more empirical knowledge regarding the most common drivers and their subsequent mechanisms. We outline critical datasets and model developments required to test hypotheses regarding the underlying causes of increasing MTF mortality rates, and improve prediction of future mortality under climate change.

Trait-based representation of hydrological functional properties of plants in weather and ecosystem models

Land surface models and dynamic global vegetation models typically represent vegetation through coarse plant functional type groupings based on leaf form, phenology, and bioclimatic limits. Although these groupings were both feasible and functional for early model generations, in light of the pace at which our knowledge of functional ecology, ecosystem demographics, and vegetation-climate feedbacks has advanced and the ever growing demand for enhanced model performance, these groupings have become antiquated and are identified as a key source of model uncertainty. The newest wave of model development is centered on shifting the vegetation paradigm away from plant functional types (PFTs) and towards flexible trait-based representations. These models seek to improve errors in ecosystem fluxes that result from information loss due to over-aggregation of dissimilar species into the same functional class. We advocate the importance of the inclusion of plant hydraulic trait representation within the new paradigm through a framework of the whole-plant hydraulic strategy. Plant hydraulic strategy is known to play a critical role in the regulation of stomatal conductance and thus transpiration and latent heat flux. It is typical that coexisting plants employ opposing hydraulic strategies, and therefore have disparate patterns of water acquisition and use. Hydraulic traits are deterministic of drought resilience, response to disturbance, and other demographic processes. The addition of plant hydraulic properties in models may not only improve the simulation of carbon and water fluxes but also vegetation population distributions.

Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison

To predict forest response to long-term climate change with high confidence requires that dynamic global vegetation models (DGVMs) be successfully tested against ecosystem response to short-term variations in environmental drivers, including regular seasonal patterns. Here, we used an integrated dataset from four forests in the Brasil flux network, spanning a range of dry-season intensities and lengths, to determine how well four state-of-the-art models (IBIS, ED2, JULES, and CLM3.5) simulated the seasonality of carbon exchanges in Amazonian tropical forests. We found that most DGVMs poorly represented the annual cycle of gross primary productivity (GPP), of photosynthetic capacity (Pc), and of other fluxes and pools. Models simulated consistent dry-season declines in GPP in the equatorial Amazon (Manaus K34, Santarem K67, and Caxiuanã CAX); a contrast to observed GPP increases. Model simulated dry-season GPP reductions were driven by an external environmental factor, 'soil water stress' and consequently by a constant or decreasing photosynthetic infrastructure (Pc), while observed dry-season GPP resulted from a combination of internal biological (leaf-flush and abscission and increased Pc) and environmental (incoming radiation) causes. Moreover, we found models generally overestimated observed seasonal net ecosystem exchange (NEE) and respiration (R_e ) at equatorial locations. In contrast, a southern Amazon forest (Jarú RJA) exhibited dry-season declines in GPP and R_e consistent with most DGVMs simulations. While water limitation was represented in models and the primary driver of seasonal photosynthesis in southern Amazonia, changes in internal biophysical processes, light-harvesting adaptations (e.g., variations in leaf area index (LAI) and increasing leaf-level assimilation rate related to leaf demography), and allocation lags between leaf and wood, dominated equatorial Amazon carbon flux dynamics and were deficient or absent from current model formulations. Correctly simulating flux seasonality at tropical forests requires a greater understanding and the incorporation of internal biophysical mechanisms in future model developments.

Three times greater weight of daytime than of night-time temperature on leaf unfolding phenology in temperate trees

The phenology of spring leaf unfolding plays a key role in the structure and functioning of ecosystems. The classical concept of heat requirement (growing degree days) for leaf unfolding was developed hundreds of years ago, but this model does not include the recently reported greater importance of daytime than night-time temperature. A manipulative experiment on daytime vs night-time warming with saplings of three species of temperate deciduous trees was conducted and a Bayesian method was applied to explore the different effects of daytime and night-time temperatures on spring phenology. We found that both daytime and night-time warming significantly advanced leaf unfolding, but the sensitivities to increased daytime and night-time temperatures differed significantly. Trees were most sensitive to daytime warming (7.4 ± 0.9, 4.8 ± 0.3 and 4.8 ± 0.2 d advancement per degree Celsius warming (d °C^-1 ) for birch, oak and beech, respectively) and least sensitive to night-time warming (5.5 ± 0.9, 3.3 ± 0.3 and 2.1 ± 0.9 d °C^-1 ). Interestingly, a Bayesian analysis found that the impact of daytime temperature on leaf unfolding was approximately three times higher than that of night-time temperatures. Night-time global temperature is increasing faster than daytime temperature, so model projections of future spring phenology should incorporate the effects of these different temperatures.

Projected carbon stocks in the conterminous USA with land use and variable fire regimes

The dynamic global vegetation model (DGVM) MC2 was run over the conterminous USA at 30 arc sec (~800 m) to simulate the impacts of nine climate futures generated by 3GCMs (CSIRO, MIROC and CGCM3) using 3 emission scenarios (A2, A1B and B1) in the context of the LandCarbon national carbon sequestration assessment. It first simulated potential vegetation dynamics from coast to coast assuming no human impacts and naturally occurring wildfires. A moderate effect of increased atmospheric CO2 on water use efficiency and growth enhanced carbon sequestration but did not greatly influence woody encroachment. The wildfires maintained prairie-forest ecotones in the Great Plains. With simulated fire suppression, the number and impacts of wildfires was reduced as only catastrophic fires were allowed to escape. This greatly increased the expansion of forests and woodlands across the western USA and some of the ecotones disappeared. However, when fires did occur, their impacts (both extent and biomass consumed) were very large. We also evaluated the relative influence of human land use including forest and crop harvest by running the DGVM with land use (and fire suppression) and simple land management rules. From 2041 through 2060, carbon stocks (live biomass, soil and dead biomass) of US terrestrial ecosystems varied between 155 and 162 Pg C across the three emission scenarios when potential natural vegetation was simulated. With land use, periodic harvest of croplands and timberlands as well as the prevention of woody expansion across the West reduced carbon stocks to a range of 122-126 Pg C, while effective fire suppression reduced fire emissions by about 50%. Despite the simplicity of our approach, the differences between the size of the carbon stocks confirm other reports of the importance of land use on the carbon cycle over climate change.

Increased heat requirement for leaf flushing in temperate woody species over 1980-2012: effects of chilling, precipitation and insolation

Recent studies have revealed large unexplained variation in heat requirement-based phenology models, resulting in large uncertainty when predicting ecosystem carbon and water balance responses to climate variability. Improving our understanding of the heat requirement for spring phenology is thus urgently needed. In this study, we estimated the species-specific heat requirement for leaf flushing of 13 temperate woody species using long-term phenological observations from Europe and North America. The species were defined as early and late flushing species according to the mean date of leaf flushing across all sites. Partial correlation analyses were applied to determine the temporal correlations between heat requirement and chilling accumulation, precipitation and insolation sum during dormancy. We found that the heat requirement for leaf flushing increased by almost 50% over the study period 1980-2012, with an average of 30 heat units per decade. This temporal increase in heat requirement was observed in all species, but was much larger for late than for early flushing species. Consistent with previous studies, we found that the heat requirement negatively correlates with chilling accumulation. Interestingly, after removing the variation induced by chilling accumulation, a predominantly positive partial correlation exists between heat requirement and precipitation sum, and a predominantly negative correlation between heat requirement and insolation sum. This suggests that besides the well-known effect of chilling, the heat requirement for leaf flushing is also influenced by precipitation and insolation sum during dormancy. However, we hypothesize that the observed precipitation and insolation effects might be artefacts attributable to the inappropriate use of air temperature in the heat requirement quantification. Rather than air temperature, meristem temperature is probably the prominent driver of the leaf flushing process, but these data are not available. Further experimental research is thus needed to verify whether insolation and precipitation sums directly affect the heat requirement for leaf flushing.

Leaf onset in the northern hemisphere triggered by daytime temperature

Recent warming significantly advanced leaf onset in the northern hemisphere. This signal cannot be accurately reproduced by current models parameterized by daily mean temperature (T_mean). Here using in situ observations of leaf unfolding dates (LUDs) in Europe and the United States, we show that the interannual anomalies of LUD during 1982–2011 are triggered by daytime (T_max) more than by nighttime temperature (T_min). Furthermore, an increase of 1 °C in T_max would advance LUD by 4.7 days in Europe and 4.3 days in the United States, more than the conventional temperature sensitivity estimated from T_mean. The triggering role of T_max, rather than the T_min or T_mean variable, is also supported by analysis of the large-scale patterns of satellite-derived vegetation green-up in spring in the northern hemisphere (>30°N). Our results suggest a new conceptual framework of leaf onset using daytime temperature to improve the performance of phenology modules in current Earth system models.

Recent warming has significantly advanced leaf onset in the northern hemisphere. Here, the authors show asymmetric effects of daytime and nighttime temperature change on the timing of leaf onset.

Model-data assimilation of multiple phenological observations to constrain and predict leaf area index

Our limited ability to accurately simulate leaf phenology is a leading source of uncertainty in models of ecosystem carbon cycling. We evaluate if continuously updating canopy state variables with observations is beneficial for predicting phenological events. We employed ensemble adjustment Kalman filter (EAKF) to update predictions of leaf area index (LAI) and leaf extension using tower-based photosynthetically active radiation (PAR) and moderate resolution imaging spectrometer (MODIS) data for 2002-2005 at Willow Creek, Wisconsin, USA, a mature, even-aged, northern hardwood, deciduous forest. The ecosystem demography model version 2 (ED2) was used as the prediction model, forced by offline climate data. EAKF successfully incorporated information from both the observations and model predictions weighted by their respective uncertainties. The resulting. estimate reproduced the observed leaf phenological cycle in the spring and the fall better than a parametric model prediction. These results indicate that during spring the observations contribute most in determining the correct bud-burst date, after which the model performs well, but accurately modeling fall leaf senesce requires continuous model updating from observations. While the predicted net ecosystem exchange (NEE) of CO2 precedes tower observations and unassimilated model predictions in the spring, overall the prediction follows observed NEE better than the model alone. Our results show state data assimilation successfully simulates the evolution of plant leaf phenology and improves model predictions of forest NEE.

A new model to simulate climate-change impacts on forest succession for local land management

We developed a new climate-sensitive vegetation state-and-transition simulation model (CV-STSM) to simulate future vegetation at a fine spatial grain commensurate with the scales of human land-use decisions, and under the joint influences of changing climate, site productivity, and disturbance. CV-STSM integrates outputs from four different modeling systems. Successional changes in tree species composition and stand structure were represented as transition probabilities and organized into a state-and-transition simulation model. States were characterized based on assessments of both current vegetation and of projected future vegetation from a dynamic global vegetation model (DGVM). State definitions included sufficient detail to support the integration of CV-STSM with an agent-based model of land-use decisions and a mechanistic model of fire behavior and spread. Transition probabilities were parameterized using output from a stand biometric model run across a wide range of site productivities. Biogeographic and biogeochemical projections from the DGVM were used to adjust the transition probabilities to account for the impacts of climate change on site productivity and potential vegetation type. We conducted experimental simulations in the Willamette Valley, Oregon, USA. Our simulation landscape incorporated detailed new assessments of critically imperiled Oregon white oak (Quercus garryana) savanna and prairie habitats among the suite of existing and future vegetation types. The experimental design fully crossed four future climate scenarios with three disturbance scenarios. CV-STSM showed strong interactions between climate and disturbance scenarios. All disturbance scenarios increased the abundance of oak savanna habitat, but an interaction between the most intense disturbance and climate-change scenarios also increased the abundance of subtropical tree species. Even so, subtropical tree species were far less abundant at the end of simulations in CV-STSM than in the dynamic global vegetation model simulations. Our results indicate that dynamic global vegetation models may overestimate future rates of vegetation change, especially in the absence of stand-replacing disturbances. Modeling tools such as CV-STSM that simulate rates and direction of vegetation change affected by interactions and feedbacks between climate and land-use change can help policy makers, land managers, and society as a whole develop effective plans to adapt to rapidly changing climate.

Unexpected role of winter precipitation in determining heat requirement for spring vegetation green-up at northern middle and high latitudes

Heat requirement, expressed in growing degree days (GDD), is a widely used method to assess and predict the effect of temperature on plant development. Until recently, the analysis of spatial patterns of GDD requirement for spring vegetation green-up onset was limited to local and regional scales, mainly because of the sparse and aggregated spatial availability of ground phenology data. Taking advantage of the large temporal and spatial scales of remote sensing-based green-up onset data, we studied the spatial patterns of GDD requirement for vegetation green-up at northern middle and high latitudes. We further explored the correlations between GDD requirement for vegetation green-up and previous winter season chilling temperatures and precipitation, using spatial partial correlations. We showed that GDD requirement for vegetation green-up onset declines towards the north at a mean rate of 18.8 °C-days per degree latitude between 35°N and 70°N, and vary significantly among different vegetation types. Our results confirmed that the GDD requirement for vegetation green-up is negatively correlated with previous winter chilling, which was defined as the number of chilling days from the day when the land surface froze in the previous autumn to the day of green-up onset. This negative correlation is a well-known phenomenon from local studies. Interestingly, irrespective of the vegetation type, we also found a positive correlation between the GDD requirement and previous winter season precipitation, which was defined as the sum of the precipitation of the month when green-up onset occur and the precipitation that occurred during the previous 2 months. Our study suggests that GDD requirement, chilling and precipitation may have complex interactions in their effects on spring vegetation green-up phenology. These findings have important implications for improving phenology models and could therefore advance our understanding of the interplay between spring phenology and carbon fluxes.

Inclusion of soil carbon lateral movement alters terrestrial carbon budget in China

The lateral movement of soil carbon has a profound effect on the carbon budget of terrestrial ecosystems; however, it has never been quantified in China, which is one of the strongest soil erosion areas in the world. In this study, we estimated that the overall soil erosion in China varies from 11.27 to 18.17 Pg yr(-1) from 1982 to 2011, accounting for 7-21% of total soil erosion globally. Soil erosion induces a substantial lateral redistribution of soil organic carbon ranging from 0.64 to 1.04 Pg C yr(-1). The erosion-induced carbon flux ranges from a 0.19 Pg C yr(-1) carbon source to a 0.24 Pg C yr(-1) carbon sink in the terrestrial ecosystem, which is potentially comparable in magnitude to previously estimated total carbon budget of China (0.19 to 0.26 Pg yr(-1)). Our results showed that the lateral movement of soil carbon strongly alters the carbon budget in China, and highlighted the urgent need to integrate the processes of soil erosion into the regional or global carbon cycle estimates.

Global validation of a process-based model on vegetation gross primary production using eddy covariance observations

Gross Primary Production (GPP) is the largest flux in the global carbon cycle. However, large uncertainties in current global estimations persist. In this study, we examined the performance of a process-based model (Integrated BIosphere Simulator, IBIS) at 62 eddy covariance sites around the world. Our results indicated that the IBIS model explained 60% of the observed variation in daily GPP at all validation sites. Comparison with a satellite-based vegetation model (Eddy Covariance-Light Use Efficiency, EC-LUE) revealed that the IBIS simulations yielded comparable GPP results as the EC-LUE model. Global mean GPP estimated by the IBIS model was 107.50±1.37 Pg C year(-1) (mean value ± standard deviation) across the vegetated area for the period 2000-2006, consistent with the results of the EC-LUE model (109.39±1.48 Pg C year(-1)). To evaluate the uncertainty introduced by the parameter Vcmax, which represents the maximum photosynthetic capacity, we inversed Vcmax using Markov Chain-Monte Carlo (MCMC) procedures. Using the inversed Vcmax values, the simulated global GPP increased by 16.5 Pg C year(-1), indicating that IBIS model is sensitive to Vcmax, and large uncertainty exists in model parameterization.

A tree-ring perspective on the terrestrial carbon cycle

Tree-ring records can provide valuable information to advance our understanding of contemporary terrestrial carbon cycling and to reconstruct key metrics in the decades preceding monitoring data. The growing use of tree rings in carbon-cycle research is being facilitated by increasing recognition of reciprocal benefits among research communities. Yet, basic questions persist regarding what tree rings represent at the ecosystem level, how to optimally integrate them with other data streams, and what related challenges need to be overcome. It is also apparent that considerable unexplored potential exists for tree rings to refine assessments of terrestrial carbon cycling across a range of temporal and spatial domains. Here, we summarize recent advances and highlight promising paths of investigation with respect to (1) growth phenology, (2) forest productivity trends and variability, (3) CO2 fertilization and water-use efficiency, (4) forest disturbances, and (5) comparisons between observational and computational forest productivity estimates. We encourage the integration of tree-ring data: with eddy-covariance measurements to investigate carbon allocation patterns and water-use efficiency; with remotely sensed observations to distinguish the timing of cambial growth and leaf phenology; and with forest inventories to develop continuous, annually-resolved and long-term carbon budgets. In addition, we note the potential of tree-ring records and derivatives thereof to help evaluate the performance of earth system models regarding the simulated magnitude and dynamics of forest carbon uptake, and inform these models about growth responses to (non-)climatic drivers. Such efforts are expected to improve our understanding of forest carbon cycling and place current developments into a long-term perspective.

A high-resolution approach to estimating ecosystem respiration at continental scales using operational satellite data

A better understanding of the local variability in land-atmosphere carbon fluxes is crucial to improving the accuracy of global carbon budgets. Operational satellite data backed by ground measurements at Fluxnet sites proved valuable in monitoring local variability of gross primary production at highly resolved spatio-temporal resolutions. Yet, we lack similar operational estimates of ecosystem respiration (Re) to calculate net carbon fluxes. If successful, carbon fluxes from such a remote sensing approach would form an independent and sought after measure to complement widely used dynamic global vegetation models (DGVMs). Here, we establish an operational semi-empirical Re model, based only on data from the Moderate Resolution Imaging Spectroradiometer (MODIS) with a resolution of 1 km and 8 days. Fluxnet measurements between 2000 and 2009 from 100 sites across North America and Europe are used for parameterization and validation. Our analysis shows that Re is closely tied to temperature and plant productivity. By separating temporal and intersite variation, we find that MODIS land surface temperature (LST) and enhanced vegetation index (EVI) are sufficient to explain observed Re across most major biomes with a negligible bias [R² = 0.62, RMSE = 1.32 (g C m(-2) d(-1)), MBE = 0.05 (g C m(-2) d(-1))]. A comparison of such satellite-derived Re with those simulated by the DGVM LPJmL reveals similar spatial patterns. However, LPJmL shows higher temperature sensitivities and consistently simulates higher Re values, in high-latitude and subtropical regions. These differences remain difficult to explain and they are likely associated either with LPJmL parameterization or with systematic errors in the Fluxnet sampling technique. While uncertainties remain with Re estimates, the model formulated in this study provides an operational, cross-validated and unbiased approach to scale Fluxnet Re to the continental scale and advances knowledge of spatio-temporal Re variability.

Spring leaf phenology and the diurnal temperature range in a temperate maple forest

Spring leaf phenology in temperate climates is intricately related to numerous aspects of the lower atmosphere [e.g., surface energy balance, carbon flux, humidity, the diurnal temperature range (DTR)]. To further develop and improve the accuracy of ecosystem and climate models, additional investigations of the specific nature of the relationships between spring leaf phenology and various ecosystem and climate processes are required in different environments. This study used visual observations of maple leaf phenology, below-canopy light intensities, and micrometeorological data collected during the spring seasons of 2008, 2009, and 2010 to examine the potential influence of leaf phenology on a seasonal transition in the trend of the DTR. The timing of a reversal in the DTR trend occurred near the time when the leaves were unfolding and expanding. The results suggest that the spring decline in the DTR can be attributed primarily to the effect of canopy closure on daily maximum temperature. These findings improve our understanding of the relationship between leaf phenology and the diurnal temperature range in temperate maple forests during the spring. They also demonstrate the necessity of incorporating accurate phenological data into ecosystem and climate models and warrant a careful examination of the extent to which canopy phenology is currently incorporated into existing models.

Miscanthus establishment and overwintering in the Midwest USA: a regional modeling study of crop residue management on critical minimum soil temperatures

Miscanthus is an intriguing cellulosic bioenergy feedstock because its aboveground productivity is high for low amounts of agrochemical inputs, but soil temperatures below −3.5°C could threaten successful cultivation in temperate regions. We used a combination of observed soil temperatures and the Agro-IBIS model to investigate how strategic residue management could reduce the risk of rhizome threatening soil temperatures. This objective was addressed using a historical (1978–2007) reconstruction of extreme minimum 10 cm soil temperatures experienced across the Midwest US and model sensitivity studies that quantified the impact of crop residue on soil temperatures. At observation sites and for simulations that had bare soil, two critical soil temperature thresholds (50% rhizome winterkill at −3.5°C and −6.0°C for different Miscanthus genotypes) were reached at rhizome planting depth (10 cm) over large geographic areas. The coldest average annual extreme 10 cm soil temperatures were between −8°C to −11°C across North Dakota, South Dakota, and Minnesota. Large portions of the region experienced 10 cm soil temperatures below −3.5°C in 75% or greater for all years, and portions of North and South Dakota, Minnesota, and Wisconsin experienced soil temperatures below −6.0°C in 50–60% of all years. For simulated management options that established varied thicknesses (1–5 cm) of miscanthus straw following harvest, extreme minimum soil temperatures increased by 2.5°C to 6°C compared to bare soil, with the greatest warming associated with thicker residue layers. While the likelihood of 10 cm soil temperatures reaching −3.5°C was greatly reduced with 2–5 cm of surface residue, portions of the Dakotas, Nebraska, Minnesota, and Wisconsin still experienced temperatures colder than −3.5°C in 50–80% of all years. Nonetheless, strategic residue management could help increase the likelihood of overwintering of miscanthus rhizomes in the first few years after establishment, although low productivity and biomass availability during these early stages could hamper such efforts.

Influence of spring and autumn phenological transitions on forest ecosystem productivity

We use eddy covariance measurements of net ecosystem productivity (NEP) from 21 FLUXNET sites (153 site-years of data) to investigate relationships between phenology and productivity (in terms of both NEP and gross ecosystem photosynthesis, GEP) in temperate and boreal forests. Results are used to evaluate the plausibility of four different conceptual models. Phenological indicators were derived from the eddy covariance time series, and from remote sensing and models. We examine spatial patterns (across sites) and temporal patterns (across years); an important conclusion is that it is likely that neither of these accurately represents how productivity will respond to future phenological shifts resulting from ongoing climate change. In spring and autumn, increased GEP resulting from an 'extra' day tends to be offset by concurrent, but smaller, increases in ecosystem respiration, and thus the effect on NEP is still positive. Spring productivity anomalies appear to have carry-over effects that translate to productivity anomalies in the following autumn, but it is not clear that these result directly from phenological anomalies. Finally, the productivity of evergreen needleleaf forests is less sensitive to phenology than is productivity of deciduous broadleaf forests. This has implications for how climate change may drive shifts in competition within mixed-species stands.

Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests

Spring phenology is thought to exert a major influence on the carbon (C) balance of temperate and boreal ecosystems. We investigated this hypothesis using four spring onset phenological indicators in conjunction with surface-atmosphere CO(2) exchange data from the conifer-dominated Howland Forest and deciduous-dominated Harvard Forest AmeriFlux sites. All phenological measures, including CO(2) source-sink transition dates, could be well predicted on the basis of a simple two-parameter spring warming model, indicating good potential for improving the representation of phenological transitions and their dynamic responsiveness to climate variability in land surface models. The date at which canopy-scale photosynthetic capacity reached a threshold value of 12 micromol m(-2) s(-1) was better correlated with spring and annual flux integrals than were either deciduous or coniferous bud burst dates. For all phenological indicators, earlier spring onset consistently, but not always significantly, resulted in higher gross primary productivity (GPP) and ecosystem respiration (RE) for both seasonal (spring months, April-June) and annual flux integrals. The increase in RE was less than that in GPP; depending on the phenological indicator used, a one-day advance in spring onset increased springtime net ecosystem productivity (NEP) by 2-4 g C m(-2) day(-1). In general, we could not detect significant differences between the two forest types in response to earlier spring, although the response to earlier spring was generally more pronounced for Harvard Forest than for Howland Forest, suggesting that future climate warming may favor deciduous species over coniferous species, at least in this region. The effect of earlier spring tended to be about twice as large when annual rather than springtime flux integrals were considered. This result is suggestive of both immediate and lagged effects of earlier spring onset on ecosystem C cycling, perhaps as a result of accelerated N cycling rates and cascading effects on N uptake, foliar N concentrations and photosynthetic capacity.