Atmospheric CO2 drives most of the greenhouse effect increase. One major uncertainty on the future rate of increase of CO2 in the atmosphere is the impact of the anticipated climate change on the ...vegetation. Dynamic Global Vegetation Models (DGVM) are used to address this question. ORCHIDEE is such a DGVM that has proven useful for climate change studies. However, there is no objective and methodological way to accurately assess each new available version on the global scale. In this paper, we submit a methodological evaluation of ORCHIDEE by correlating satellite-derived Vegetation Index time series against those of the modeled Fraction of absorbed Photosynthetically Active Radiation (FPAR). A perfect correlation between the two is not expected, however an improvement of the model should lead to an increase of the overall performance. We detail two case studies in which model improvements are demonstrated, using our methodology. In the first one, a new phenology version in ORCHIDEE is shown to bring a significant impact on the simulated annual cycles, in particular for C3 Grasses and C3 Crops. In the second case study, we compare the simulations when using two different weather fields to drive ORCHIDEE. The ERA-Interim forcing leads to a better description of the FPAR interannual anomalies than the simulation forced by a mixed CRU-NCEP dataset. This work shows that long time series of satellite observations, despite their uncertainties, can identify weaknesses in global vegetation models, a necessary first step to improving them.
This paper is a modelling study of crop management impacts on carbon and water fluxes at a range of European sites. The model is a crop growth model (STICS) coupled with a process-based land surface ...model (ORCHIDEE). The data are online eddy-covariance observations of CO2 and H2 O fluxes at five European maize cultivation sites. The results show that the ORCHIDEE-STICS model explains up to 75 % of the observed daily net CO2 ecosystem exchange (NEE) variance, and up to 79 % of the latent heat flux (LE) variance at five sites. The model is better able to reproduce gross primary production (GPP) variations than terrestrial ecosystem respiration (TER) variations. We conclude that structural deficiencies in the model parameterizations of leaf area index (LAI) and TER are the main sources of error in simulating CO2 and H2 O fluxes. A number of sensitivity tests, with variable crop variety, nitrogen fertilization, irrigation, and planting date, indicate that any of these management factors is able to change NEE by more than 15 %, but that the response of NEE to management parameters is highly site-dependent. Changes in management parameters are found to impact not only the daily values of NEE and LE, but also the cumulative yearly values. In addition, LE is shown to be less sensitive to management parameters than NEE. Multi-site model evaluations, coupled with sensitivity analysis to management parameters, thus provide important information about model errors, which helps to improve the simulation of CO2 and H2 O fluxes across European croplands.
Europe has experienced a wide scale warming over the past decades and climate simulations predict further warming and changes in precipitation patterns during the 21st century. The winter of ...2006–2007 has been exceptionally mild with averaged temperatures that may become the norm during the second half of the 21st century. Here we report on satellite observations of the vegetation greening that occurred at the subcontinent scale almost 10 days earlier than the average over the past three decades. Even at the relatively coarse resolution of the satellite data, which mixes several vegetation types, there is a strong negative temporal correlation between the February–April mean temperature and the start of growth date. The western Europe mean vegetation onset sensitivity is −3.9 days per degree of temperature, and is mainly driven by crops and grasslands, with a biome‐specific sensitivity of −4.7 days/°C. For forested biomes, onset anomalies are better correlated to the March–May mean temperature, with a sensitivity of −3.6 days/°C. Based upon the satellite data, there is no consistent indication that a lack of cold days in the winter 2006–2007 had any effect in delaying the vegetation onset.
Aiming at producing improved estimates of carbon source/sink spatial and interannual patterns across Europe (35% croplands), this work combines the terrestrial biosphere model Organizing Carbon and ...Hydrology in Dynamic Ecosystems (ORCHIDEE), for vegetation productivity, water balance, and soil carbon dynamics, and the generic crop model Simulateur Multidisciplinaire pour les Cultures Standard (STICS), for phenology, irrigation, nitrogen balance, and harvest. The ORCHIDEE‐STICS model, relying on three plant functional types for the representation of temperate agriculture, is evaluated over the last few decades at various spatial and temporal resolutions. The simulated leaf area index seasonal cycle is largely improved relative to the original ORCHIDEE simulating grasslands, and compares favorably with remote‐sensing observations (correlation doubles over Europe). Crop yield is derived from annual net primary productivity and compared with wheat and grain maize harvest data for five European countries. Discrepancies between 30 year mean simulated and reported yields are large in Mediterranean countries. Interannual variability amplitude expressed relative to the mean is reduced toward the observed variability (≈10%) when using ORCHIDEE‐STICS. Overall, this study highlights the importance of accounting for the specific phenologies of crops sown both in winter and in spring and for irrigation applied to spring crops in regional/global models of the terrestrial carbon cycle. Limitations suggest to account for temporal and spatial variability in agricultural practices for further simulation improvement.
The difference is found at the marginsThe terrestrial biosphere absorbs about a quarter of all anthropogenic carbon dioxide emissions, but the amount that they take up varies from year to year. Why? ...Combining models and observations, Ahlstrom et al. found that marginal ecosystems-semiarid savannas and low-latitude shrublands-are responsible for most of the variability. Biological productivity in these semiarid regions is water-limited and strongly associated with variations in precipitation, unlike wetter tropical areas. Understanding carbon uptake by these marginal lands may help to improve predictions of variations in the global carbon cycle.Science, this issue p. 895 The growth rate of atmospheric carbon dioxide (CO2) concentrations since industrialization is characterized by large interannual variability, mostly resulting from variability in CO2 uptake by terrestrial ecosystems (typically termed carbon sink). However, the contributions of regional ecosystems to that variability are not well known. Using an ensemble of ecosystem and land-surface models and an empirical observation-based product of global gross primary production, we show that the mean sink, trend, and interannual variability in CO2 uptake by terrestrial ecosystems are dominated by distinct biogeographic regions. Whereas the mean sink is dominated by highly productive lands (mainly tropical forests), the trend and interannual variability of the sink are dominated by semi-arid ecosystems whose carbon balance is strongly associated with circulation-driven variations in both precipitation and temperature.
Several studies have used grape harvest date (GHD) as a proxy for temperature variations of the last centuries in Europe. However, the use of grape harvest dates to reconstruct climate is not ...straightforward, with four possible causes of major flaws. In this study we identify and evaluate the accuracy of GHD as a proxy to past temperature anomalies, uncertainties in the model used to relate temperature to GHD, identity of the grape varieties cultivated in the past, type of wine produced in the past and cultural practices used in the past. Our analyses are based on several phenological and crop models, and on the most complete data set on grape vine phenology and harvest quality. We show that the two methodologies currently used — linear regression models and process-based phenological models — can be accurate, but process-based phenological models ascertain robustness to be applied confidently in different vineyards and different periods. However, we show that several factors can induce a bias in temperature reconstructions using process-based models. We demonstrate the importance of historical information on the studied areas such as the varieties cultivated, the style of wine produced, the quality sought, the agricultural practices, in order to build the most robust model.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, OILJ, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Estimates of climate conditions before the 19th century are based on proxy data reconstructions or sparse meteorological measurements. The reconstruction of the atmospheric circulation that prevailed ...during the European Little Ice Age (∼1500–1850) has fostered many efforts. This study illustrates a methodology, combining historical proxies and modern datasets to obtain detailed information on the atmospheric circulation that prevailed over the North Atlantic region during the Little Ice Age. We used reconstructions of temperature gradients over France based on grape harvest dates to infer the atmospheric circulation. We found that blocking situations were more likely in summer, inducing a continental atmospheric flow. This study advocates that the reconstructions of the past atmospheric circulation should take this regime into account.
A number of studies have suggested that the growing season duration has significantly lengthened during the past decades, but the connections between phenology variability and the terrestrial carbon ...(C) cycle are far from clear. In this study, we used the “ORganizing Carbon and Hydrology In Dynamic Ecosystems” (ORCHIDEE) process based ecosystem model together with observed climate data to investigate spatiotemporal changes in phenology and their impacts on carbon fluxes in the Northern Hemisphere (>25°N) during 1980–2002. We found that the growing season length (GSL) has increased by 0.30 days yr−1 (R2 = 0.27, P = 0.010), owing to the combination of an earlier onset in spring (0.16 days yr−1) and a later termination in autumn (0.14 days yr−1). Trends in the GSL are however highly variable across the regions. In Eurasia, there is a significant trend toward earlier vegetation green‐up with an overall advancement rate of 0.28 days yr−1 (R2 = 0.32, P = 0.005), while in North America there is a significantly delayed vegetation senescence by 0.28 days yr−1 (R2 = 0.26, P = 0.013) during the study period. Our results also suggested that the GSL strongly correlates with annual gross primary productivity (GPP) and net primary productivity (NPP), indicating that longer growing seasons may eventually enhance vegetation growth. A 1‐day extension in GSL leads to an increase in annual GPP of 5.8 gC m−2 yr−1 (or 0.6% per day), and an increase in NPP of 2.8 gC m−2 yr−1 per day. However, owing to enhanced soil carbon decomposition accompanying the GPP increase, a change in GSL correlates only poorly with a change in annual net ecosystem productivity (NEP).
▶ Pseudo remote sensing estimates of biomass and height are assimilated in ORCHIDEE-FM. ▶ A positive NEP can only be simulated when the growth stage of the forests is assimilated. ▶ Assimilating in ...situ biomass or height brings down the RMSE of simulated NEP by up to 35%. ▶ Assimilating remotely estimated biomass brings down the RMSE of simulated NEP by up to 25%. ▶ The most improved simulated flux is respiration.
Global vegetation models (GVMs) simulate CO2, water and energy fluxes at large scales, typically no smaller than 10×10km. GVM simulations are thus expected to simulate the average functioning, but not the local variability. The two main limiting factors in refining this scale are (1) the scale at which the pedo-climatic inputs – temperature, precipitation, soil water reserve, etc. – are available to drive models and (2) the lack of geospatial information on the vegetation type and the age of forest stands. This study assesses how remotely sensed biomass or stand height could help the new generation of GVMs, which explicitly represent forest age structure and management, to better simulate this local variability. For the ORCHIDEE-FM model, we find that a simple assimilation of biomass or height brings down the root mean square error (RMSE) of some simulated carbon fluxes by 30–50%. Current error levels of remote sensing estimates do not impact this improvement for large gross fluxes (e.g. terrestrial ecosystem respiration), but they reduce the improvement of simulated net ecosystem productivity, adding 13.5–21% of RMSE to assimilations using the in situ estimates. The data assimilation under study is more effective to improve the simulation of respiration than the simulation of photosynthesis. The assimilation of height or biomass in ORCHIDEE-FM enables the correct retrieval of variables that are more difficult to measure over large areas, such as stand age. A combined assimilation of biomass and net ecosystem productivity could possibly enable the new generation of GVMs to retrieve other variables that are seldom measured, such as soil carbon content.
A mechanistic model of grassland dynamics is used to simulate fluxes of carbon dioxide (CO2) and nitrous oxide (N2O) in European grasslands. The simulations refer to five sites belonging to a ...monitoring network setup within the framework of the European Union project GREENGRASS. Simulated gross primary productivity ranges from 0.4 to 1.9 kg C m-2 year-1 depending on environmental conditions and management. Ecosystem respiration is calculated in the order of 0.7-1.5 kg C m-2 year-1, resulting in a net CO2 uptake of about 0.3 kg C m-2 year-1, in reasonable agreement with observations. Linear relationships between ecosystem respiration and gross primary productivity, as well as between net primary productivity and annual precipitation are indicated by the simulations. Annual emissions of N2O are predicted in the range of 1-5 kg N ha-1 year-1, a factor of 2-10 higher than observed. This is caused by an overestimation of the background fluxes. On the other hand, the model fails to faithfully reproduce timing, duration and magnitude of peak emissions triggered by the application of mineral and organic fertilizers and by rain events.
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