Model ORCHIDEE Research Articles

Ecosystem model optimization using in situ flux observations: benefit of Monte Carlo versus variational schemes and analyses of the year-to-year model performances

Abstract. Terrestrial ecosystem models can provide major insights into the responses of Earth's ecosystems to environmental changes and rising levels of atmospheric CO2. To achieve this goal, biosphere models need mechanistic formulations of the processes that drive the ecosystem functioning from diurnal to decadal timescales. However, the subsequent complexity of model equations is associated with unknown or poorly calibrated parameters that limit the accuracy of long-term simulations of carbon or water fluxes and their interannual variations. In this study, we develop a data assimilation framework to constrain the parameters of a mechanistic land surface model (ORCHIDEE) with eddy-covariance observations of CO2 and latent heat fluxes made during the years 2001–2004 at the temperate beech forest site of Hesse, in eastern France. As a first technical issue, we show that for a complex process-based model such as ORCHIDEE with many (28) parameters to be retrieved, a Monte Carlo approach (genetic algorithm, GA) provides more reliable optimal parameter values than a gradient-based minimization algorithm (variational scheme). The GA allows the global minimum to be found more efficiently, whilst the variational scheme often provides values relative to local minima. The ORCHIDEE model is then optimized for each year, and for the whole 2001–2004 period. We first find that a reduced (<10) set of parameters can be tightly constrained by the eddy-covariance observations, with a typical error reduction of 90%. We then show that including contrasted weather regimes (dry in 2003 and wet in 2002) is necessary to optimize a few specific parameters (like the temperature dependence of the photosynthetic activity). Furthermore, we find that parameters inverted from 4 years of flux measurements are successful at enhancing the model fit to the data on several timescales (from monthly to interannual), resulting in a typical modeling efficiency of 92% over the 2001–2004 period (Nash–Sutcliffe coefficient). This suggests that ORCHIDEE is able robustly to predict, after optimization, the fluxes of CO2 and the latent heat of a specific temperate beech forest (Hesse site). Finally, it is shown that using only 1 year of data does not produce robust enough optimized parameter sets in order to simulate properly the year-to-year flux variability. This emphasizes the need to assimilate data over several years, including contrasted weather regimes, to improve the simulated flux interannual variability.

Use of various remote sensing land cover products for plant functional type mapping over Siberia

Abstract. High-latitude ecosystems play an important role in the global carbon cycle and in regulating the climate system and are presently undergoing rapid environmental change. Accurate land cover data sets are required to both document these changes as well as to provide land-surface information for benchmarking and initializing Earth system models. Earth system models also require specific land cover classification systems based on plant functional types (PFTs), rather than species or ecosystems, and so post-processing of existing land cover data is often required. This study compares over Siberia, multiple land cover data sets against one another and with auxiliary data to identify key uncertainties that contribute to variability in PFT classifications that would introduce errors in Earth system modeling. Land cover classification systems from GLC 2000, GlobCover 2005 and 2009, and MODIS collections 5 and 5.1 are first aggregated to a common legend, and then compared to high-resolution land cover classification systems, vegetation continuous fields (MODIS VCFs) and satellite-derived tree heights (to discriminate against sparse, shrub, and forest vegetation). The GlobCover data set, with a lower threshold for tree cover and taller tree heights and a better spatial resolution, tends to have better distributions of tree cover compared to high-resolution data. It has therefore been chosen to build new PFT maps for the ORCHIDEE land surface model at 1 km scale. Compared to the original PFT data set, the new PFT maps based on GlobCover 2005 and an updated cross-walking approach mainly differ in the characterization of forests and degree of tree cover. The partition of grasslands and bare soils now appears more realistic compared with ground truth data. This new vegetation map provides a framework for further development of new PFTs in the ORCHIDEE model like shrubs, lichens and mosses, to represent the water and carbon cycles in northern latitudes better. Updated land cover data sets are critical for improving and maintaining the relevance of Earth system models for assessing climate and human impacts on biogeochemistry and biophysics. The new PFT map at 5 km scale is available for download from the PANGAEA website at 10.1594/PANGAEA.810709.

Modelling LAI, surface water and carbon fluxes at high-resolution over France: comparison of ISBA-A-gs and ORCHIDEE

Abstract. The Leaf Area Index (LAI) is a measure of the amount of photosynthetic leaves and governs the canopy conductance to water vapor and carbon dioxide. Four different estimates of LAI were compared over France: two LAI products derived from satellite remote sensing, and two LAI simulations derived from land surface modelling. The simulated LAI was produced by the ISBA-A-gs model and by the ORCHIDEE model (developed by CNRM-GAME and by IPSL, respectively), for the 1994–2007 period. The two models were driven by the same atmospheric variables and used the same land cover map (SAFRAN and ECOCLIMAP-II, respectively). The MODIS and CYCLOPES satellite LAI products were used. Both products were available from 2000 to 2007 and this relatively long period allowed to investigate the interannual and the seasonal variability of monthly LAI values. In particular the impact of the 2003 and 2005 droughts were analyzed. The two models presented contrasting results, with a difference of one month between the average leaf onset dates simulated by the two models, and a maximum interannual variability of LAI simulated at springtime by ORCHIDEE and at summertime by ISBA-A-gs. The comparison with the satellite LAI products showed that, in general, the seasonality was better represented by ORCHIDEE, while ISBA-A-gs tended to better represent the interannual variability, especially for grasslands. While the two models presented comparable values of net carbon fluxes, ORCHIDEE simulated much higher photosynthesis rates than ISBA-A-gs (+70%), while providing lower transpiration estimates (−8%).

Reconsidering the change in global biosphere productivity between the Last Glacial Maximum and present day from the triple oxygen isotopic composition of air trapped in ice cores

We present a global model to infer past biosphere productivity using the record of triple isotopic composition of atmospheric oxygen. Our model incorporates recent determinations of the mass‐dependent relationships between δ17O and δ18O associated with leaf transpiration and various O2 uptake processes. It also considers the spatial and seasonal variations of vegetation distribution, climatic conditions, and isotopic composition of meteoric water. On the basis of this model, we provide global estimates for the Last Glacial Maximum (LGM) and the present of (1) the triple isotopic composition of leaf water, (2) isotopic fractionation factors for terrestrial dark respiration in soils and in leaves as well as total terrestrial respiration, (3) relationships between δ17O and δ18O associated with terrestrial biological steady state, and (4) 17O anomalies issued from both the terrestrial and oceanic biospheres. Using these data and the vegetation distribution simulated by the ORCHIDEE model, we estimated that the rate of global biological productivity during the LGM was 60–75% of the present rate. Our value for the LGM is at the lower end of previous estimates and suggests that the rise in biosphere productivity since the last glacial is larger than previously thought.

On the assignment of prior errors in Bayesian inversions of CO2 surface fluxes

For the estimation of surface CO2 fluxes from atmospheric concentration measurements, most often Bayesian approaches have been adopted. As with all Bayesian techniques the definition of prior probability distributions is a critical step in the analysis. However, practical considerations usually guide the definition of prior information rather than objective criterions. In this paper, in situ CO2 flux pointwise measurements made by the eddy‐covariance technique are used to estimate the errors of prior fluxes provided by the prognostic carbon‐water‐energy model ORCHIDEE. The results contradict the usual convenient assumption of a multivariate Gaussian distribution. The errors of ORCHIDEE have a heavier‐tail distribution with a linear temporal dependency after the second lag day and no particular spatial structure. Such error distribution significantly complicates the inversion of CO2 surface fluxes.