The influence of seasonal phenology on canopy photosynthesis in tropical evergreen forests remains poorly understood, and its representation in global ecosystem models is highly simplified, typically ...with no seasonal variation of canopy leaf properties taken into account. Including seasonal variation in leaf age and photosynthetic capacity could improve the correspondence of global vegetation model outputs with the wet–dry season CO2 patterns measured at flux tower sites in these forests. We introduced a leaf litterfall dynamics scheme in the global terrestrial ecosystem model ORCHIDEE based on seasonal variations in net primary production (NPP), resulting in higher leaf turnover in periods of high productivity. The modifications in the leaf litterfall scheme induce seasonal variation in leaf age distribution and photosynthetic capacity. We evaluated the results of the modification against seasonal patterns of three long-term in-situ leaf litterfall datasets of evergreen tropical forests in Panama, French Guiana and Brazil. In addition, we evaluated the impact of the model improvements on simulated latent heat (LE) and gross primary productivity (GPP) fluxes for the flux tower sites Guyaflux (French Guiana) and Tapajós (km 67, Brazil). The results show that the introduced seasonal leaf litterfall corresponds well with field inventory leaf litter data and times with its seasonality. Although the simulated litterfall improved substantially by the model modifications, the impact on the modelled fluxes remained limited. The seasonal pattern of GPP improved clearly for the Guyaflux site, but no significant improvement was obtained for the Tapajós site. The seasonal pattern of the modelled latent heat fluxes was hardly changed and remained consistent with the observed fluxes. We conclude that we introduced a realistic and generic litterfall dynamics scheme, but that other processes need to be improved in the model to achieve better simulations of GPP seasonal patterns for tropical evergreen forests.
Assigning proper prior uncertainties for inverse modelling of CO2 is of high importance, both to regularise the otherwise ill-constrained inverse problem and to quantitatively characterise the ...magnitude and structure of the error between prior and "true" flux. We use surface fluxes derived from three biosphere models – VPRM, ORCHIDEE, and 5PM – and compare them against daily averaged fluxes from 53 eddy covariance sites across Europe for the year 2007 and against repeated aircraft flux measurements encompassing spatial transects. In addition we create synthetic observations using modelled fluxes instead of the observed ones to explore the potential to infer prior uncertainties from model–model residuals. To ensure the realism of the synthetic data analysis, a random measurement noise was added to the modelled tower fluxes which were used as reference. The temporal autocorrelation time for tower model–data residuals was found to be around 30 days for both VPRM and ORCHIDEE but significantly different for the 5PM model with 70 days. This difference is caused by a few sites with large biases between the data and the 5PM model. The spatial correlation of the model–data residuals for all models was found to be very short, up to few tens of kilometres but with uncertainties up to 100 % of this estimation. Propagating this error structure to annual continental scale yields an uncertainty of 0.06 Gt C and strongly underestimates uncertainties typically used from atmospheric inversion systems, revealing another potential source of errors. Long spatial e-folding correlation lengths up to several hundreds of kilometres were determined when synthetic data were used. Results from repeated aircraft transects in south-western France are consistent with those obtained from the tower sites in terms of spatial autocorrelation (35 km on average) while temporal autocorrelation is markedly lower (13 days). Our findings suggest that the different prior models have a common temporal error structure. Separating the analysis of the statistics for the model data residuals by seasons did not result in any significant differences of the spatial e-folding correlation lengths.
We present the first estimate of the global distribution of CO2 surface fluxes from 14 stations of the Total Carbon Column Observing Network (TCCON). The evaluation of this inversion is based on 1) ...comparison with the fluxes from a classical inversion of surface air-sample-measurements, and 2) comparison of CO2 mixing ratios calculated from the inverted fluxes with independent aircraft measurements made during the two years analyzed here, 2009 and 2010. The former test shows similar seasonal cycles in the northern hemisphere and consistent regional carbon budgets between inversions from the two datasets, even though the TCCON inversion appears to be less precise than the classical inversion. The latter test confirms that the TCCON inversion has improved the quality (i.e., reduced the uncertainty) of the surface fluxes compared to the assumed or prior fluxes. The consistency between the surface-air-sample-based and the TCCON-based inversions despite remaining flaws in transport models opens the possibility of increased accuracy and robustness of flux inversions based on the combination of both data sources and confirms the usefulness of space-borne monitoring of the CO2 column.
We analyze how biases of meteorological drivers impact the calculation of ecosystem CO2, water and energy fluxes by models. To do so, we drive the same ecosystem model by meteorology from gridded ...products and by meteorology from local observation at eddy-covariance flux sites. The study is focused on six flux tower sites in France spanning across a climate gradient of 7–14 °C annual mean surface air temperature and 600–1040 mm mean annual rainfall, with forest, grassland and cropland ecosystems. We evaluate the results of the ORCHIDEE process-based model driven by meteorology from four different analysis data sets against the same model driven by site-observed meteorology. The evaluation is decomposed into characteristic time scales. The main result is that there are significant differences in meteorology between analysis data sets and local observation. The phase of seasonal cycle of air temperature, humidity and shortwave downward radiation is reproduced correctly by all meteorological models (average R2 = 0.90). At sites located in altitude, the misfit of meteorological drivers from analysis data sets and tower meteorology is the largest. We show that day-to-day variations in weather are not completely well reproduced by meteorological models, with R2 between analysis data sets and measured local meteorology going from 0.35 to 0.70. The bias of meteorological driver impacts the flux simulation by ORCHIDEE, and thus would have an effect on regional and global budgets. The forcing error, defined by the simulated flux difference resulting from prescribing modeled instead of observed local meteorology drivers to ORCHIDEE, is quantified for the six studied sites at different time scales. The magnitude of this forcing error is compared to that of the model error defined as the modeled-minus-observed flux, thus containing uncertain parameterizations, parameter values, and initialization. The forcing error is on average smaller than but still comparable to model error, with the ratio of forcing error to model error being the largest on daily time scale (86%) and annual time scales (80%). The forcing error incurred from using a gridded meteorological data set to drive vegetation models is therefore an important component of the uncertainty budget of regional CO2, water and energy fluxes simulations, and should be taken into consideration in up-scaling studies.
Space-borne retrievals of solar-induced chlorophyll fluorescence (SIF) over land surfaces have recently become a resource for studying and quantifying the broad scale dynamics of gross carbon uptake ...(gross primary productivity—GPP) across ecosystems. To prepare for the assimilation of SIF data in terrestrial biosphere models, we examine how differences between SIF products (due to differences in acquisition characteristics and processing chain) may affect the optimization of model parameters and the resultant GPP estimate. We compare recent daily mean SIF products (one from the Orbiting Carbon Observatory-2 OCO-2 and two from the Global Ozone Monitoring Experiment–2 GOME-2, GlobFluo GF and NASA-v28 N28, missions), averaged at 0.5° × 0.5° spatial resolution and 16-day temporal resolution, at the biome level. Phase differences between these products are relatively small. A first-order correction of the difference in spectral sampling between the two instruments shows that OCO-2 and N28 are consistent in terms of magnitude and amplitude, while GF is twice as large as the others. Using a bias-blind toy data assimilation framework, we analyze how biases between SIF products, and between model and products, can be partially alleviated by optimizing the slope and intercept parameters of a linear GPP-SIF operator. As observation biases can transfer to biases in other optimized process-based parameters and to modeled carbon fluxes— thereby resulting in unidentified inaccurate parameter values—we argue that potential SIF biases should be treated cautiously in real-world experiments in order to achieve realistic and reliable future simulations.
Present-day Sahelian vegetation in a highly anthropized semi-arid region is assessed from local to regional scales, through the joint analysis of MODIS LAI (1km2 and 8-day resolutions), daily ...rainfall, morphopedological and land cover datasets covering the period 2000–2008. The study area is located in northwest Senegal and consists of the “Niayes” and the northwestern “Peanut Basin” eco-regions, characterized by market gardening and rain-fed cultivated crops, respectively. The objectives are i) to analyse at pixel scale LAI time series and their relation to vegetation and soil types, ii) the estimation of phenological metrics (start of season SOS, end of season EOS, growing season length GSL) and their inter-annual variability, iii) to recognize the vegetation responses to rainfall trends (mean annual precipitation, MAP; frequency of rainy events, K; combination of MAP and K, called F).
Pixel-scale analyses show that LAI time series 1) describe the actual phenology (agreeing with ground-truth AGHRYMET data), and thus can be used as a proxy for Sahelian vegetation dynamics, 2) are strongly dependent on soil types. Median maps of SOS and EOS suggest an increase of the GSL from Saint-Louis to Dakar, in agreement with both the North-South rainfall gradient and the intensification of agricultural practices around Dakar. Significant correlations (R: 0.64) between annual variation coefficient of LAI and MAP for both herbaceous crops and natural vegetation are highlighted; this correlation is reinforced (R: 0.7) using the rainfall distribution factors K and F. Rainfall thresholds allowing the SOS can be defined for each type of vegetation. These thresholds are estimated at 0–5mm, 20mm and 40mm for natural herbs, herbaceous crops and shrublands, respectively.
If previous works revealed the close link between the MAP and the SOS, our results highlight that LAI dynamics are also controlled by rainfall distribution during the Monsoon season. In this study, climatic indicators are proposed for estimating vegetation dynamics and monitoring SOS. Coupling Earth Observation data, such as MODIS LAI, with rainfall data, vegetation and soil information is found to be a reliable method for vegetation monitoring and for assessing the impact of human pressure on vegetation degradation.
► We study Sahelian vegetation dynamics in north-west Senegal (period 2000–2008). ► Phenological metric and dynamics of vegetation are calculated from MODIS time series. ► We analyze correlations between LAI, rainfall and land cover data. ► We find rainfall thresholds allowing the phenological onset (different vegetations). ► We defined links between annual vegetation dynamics and precipitation trends.
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.
The ORCHIDEE land surface model has recently been updated to improve the representation of high‐latitude environments. The model now includes improved soil thermodynamics and the representation of ...permafrost physical processes (soil thawing and freezing), as well as a new snow model to improve the representation of the seasonal evolution of the snow pack and the resulting insulation effects. The model was evaluated against data from the experimental sites of the WSibIso‐Megagrant project (www.wsibiso.ru). ORCHIDEE was applied in stand‐alone mode, on two experimental sites located in the Yamal Peninsula in the northwestern part of Siberia. These sites are representative of circumpolar‐Arctic tundra environments and differ by their respective fractions of shrub/tree cover and soil type. After performing a global sensitivity analysis to identify those parameters that have most influence on the simulation of energy and water transfers, the model was calibrated at local scale and evaluated against in situ measurements (vertical profiles of soil temperature and moisture, as well as active layer thickness) acquired during summer 2012. The results show how sensitivity analysis can identify the dominant processes and thereby reduce the parameter space for the calibration process. We also discuss the model performance at simulating the soil temperature and water content (i.e., energy and water transfers in the soil‐vegetation‐atmosphere continuum) and the contribution of the vertical discretization of the hydrothermal properties. This work clearly shows, at least at the two sites used for validation, that the new ORCHIDEE vertical discretization can represent the water and heat transfers through complex cryogenic Arctic soils—soils which present multiple horizons sometimes with peat inclusions. The improved model allows us to prescribe the vertical heterogeneity of the soil hydrothermal properties.
Key Points
The ORCHIDEE land surface model is calibrated and evaluated at local scale for two sites typical of Arctic environments (forest and shrub‐tundra)
A global sensitivity analysis identified the parameters having most influence on water and energy flux simulation
Model performance and improvements provided by the vertical discretization of the soil hydrothermal properties are highlighted
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.
PDF
