Influences of observation errors in eddy flux data on inverse model parameter estimation

Eddy covariance data are increasingly used to estimate parameters of ecosystem models. For proper maximum likelihood parameter estimates the error structure in the observed data has to be fully characterized. In this study we propose a method to characterize the random error of the eddy covariance flux data, and analyse error distribution, standard deviation, cross- and autocorrelation of CO<sub>2</sub> and H<sub>2</sub>O flux errors at four different European eddy covariance flux sites. Moreover, we examine how the treatment of those errors and additional systematic errors influence statistical estimates of parameters and their associated uncertainties with three models of increasing complexity – a hyperbolic light response curve, a light response curve coupled to water fluxes and the SVAT scheme BETHY. In agreement with previous studies we find that the error standard deviation scales with the flux magnitude. The previously found strongly leptokurtic error distribution is revealed to be largely due to a superposition of almost Gaussian distributions with standard deviations varying by flux magnitude. The crosscorrelations of CO<sub>2</sub> and H<sub>2</sub>O fluxes were in all cases negligible (<i>R</i><sup>2</sup> below 0.2), while the autocorrelation is usually below 0.6 at a lag of 0.5 h and decays rapidly at larger time lags. This implies that in these cases the weighted least squares criterion yields maximum likelihood estimates. To study the influence of the observation errors on model parameter estimates we used synthetic datasets, based on observations of two different sites. We first fitted the respective models to observations and then added the random error estimates described above and the systematic error, respectively, to the model output. This strategy enables us to compare the estimated parameters with true parameters. We illustrate that the correct implementation of the random error standard deviation scaling with flux magnitude significantly reduces the parameter uncertainty and often yields parameter retrievals that are closer to the true value, than by using ordinary least squares. The systematic error leads to systematically biased parameter estimates, but its impact varies by parameter. The parameter uncertainty slightly increases, but the true parameter is not within the uncertainty range of the estimate. This means that the uncertainty is underestimated with current approaches that neglect selective systematic errors in flux data. Hence, we conclude that potential systematic errors in flux data need to be addressed more thoroughly in data assimilation approaches since otherwise uncertainties will be vastly underestimated

Future challenges of representing land-processes in studies on land-atmosphere interactions

Over recent years, it has become increasingly apparent that climate change and air pollution need to be considered jointly for improved attribution and projections of human-caused changes in the Earth system. Exchange processes at the land surface come into play in this context, because many compounds that either act as greenhouse gases, as pollutant precursors, or both, have not only anthropogenic but also terrestrial sources and sinks. And since the fluxes of multiple gases and particulate matter between the terrestrial biota and the atmosphere are directly or indirectly coupled to vegetation and soil carbon, nutrient and water balances, quantification of their geographic patterns or changes over time requires due consideration of the underlying biological processes. In this review we highlight a number of critical aspects and recent progress in this respect, identifying in particular a number of areas where studies have shown that accounting for ecological process understanding can alter global model projections of land-atmosphere interactions substantially. Specifically, this concerns the improved quantification of uncertainties and dynamic system responses, including acclimation, and the incorporation of exchange processes that so far have been missing from global models even though they are proposed to be of relevance for our understanding of terrestrial biota-climate feedbacks. Progress has also been made regarding studies on the impacts of land use/land cover change on climate change, but the absence of a mechanistically based representation of human responseprocesses in ecosystem models that are coupled to climate models limits our ability to analyse how climate change or air pollution in turn might affect human land use. A more integrated perspective is necessary and should become an active area of research that bridges the socio-economic and biophysical communities

Physically, physiologically and conceptually hidden: improving the description and communication of seed persistence

Seed persistence is a trait that is difficult to observe or measure and consequently, has remained conceptually obscure for 40 years since Grubb’s influential description of the regeneration niche. Seed persistence is the ability of seeds to persist in a viable state post-dispersal and is relevant to current research in plant community dynamics and conservation. However, categorisations of seed persistence as transient, short-term or long-term persistent do not acknowledge the variation in persistence times as a result of deterministic processes and are difficult to apply in a predictive capacity. Consequently, a more robust understanding of seed persistence is needed in niche descriptions that are temporally explicit and in predicting the distributional changes of species in the current and future climate. We surmise an alternative to the categorizations of seed persistence on the basis of seed bank type and argue that it is best expressed as a continuous variable. We review the methods available for estimating seed persistence in situ and provide a number of testable hypotheses to contribute to the development of this important research topic. We maintain that seed persistence has not been incorporated adequately into niche theory and highlight that it can make several contributions including properly defining metapopulation niche, population growth definition. This holistic approach by integrating seed persistence into niche theory would allow us to better predict the survival of plants in a changing environment

Assessing impacts of plant stoichiometric traits on terrestrial ecosystem carbon accumulation using the E3SM land model

Abstract Carbon (C) enters into the terrestrial ecosystems via photosynthesis and cycles through the system together with other essential nutrients (i.e., nitrogen [N] and phosphorus [P]). Such a strong coupling of C, N, and P leads to the theoretical prediction that limited nutrient availability will limit photosynthesis rate, plant growth, and future terrestrial C dynamics. However, the lack of reliable information about plant tissue stoichiometric constraints remains a challenge for quantifying nutrient limitations on projected global C cycling. In this study, we harmonized observed plant tissue C:N:P stoichiometry from more than 6,000 plant species with the commonly used plant functional type framework in global land models. Using observed C:N:P stoichiometry and the flexibility of these ratios as emergent plant traits, we show that observationally constrained fixed plant stoichiometry does not improve model estimates of present‐day C dynamics compared with unconstrained stoichiometry. However, adopting stoichiometric flexibility significantly improves model predictions of C fluxes and stocks. The 21st century simulations with RCP8.5 CO2 concentrations show that stoichiometric flexibility, rather than baseline stoichiometric ratios, is the dominant controller of plant productivity and ecosystem C accumulation in modeled responses to CO2 fertilization. The enhanced nutrient limitations and plant P use efficiency mainly explain this result. This study is consistent with the previous consensus that nutrient availability will limit xfuture land carbon sequestration but challenges the idea that imbalances between C and nutrient supplies and fixed stoichiometry limit future land C sinks. We show here that it is necessary to represent nutrient stoichiometric flexibility in models to accurately project future terrestrial ecosystem carbon sequestration

Plant-driven variation in decomposition rates improves projections of global litter stock distribution.

Plant litter stocks are critical, regionally for their role in fueling fire regimes and controlling soil fertility, and globally through their feedback to atmospheric CO₂ and climate. Here we employ two global databases linking plant functional types to decomposition rates of wood and leaf litter (Cornwell et al., 2008; Weedon et al., 2009) to improve future projections of climate and carbon cycle using an intermediate complexity Earth System model. Implementing separate wood and leaf litter decomposabilities and their temperature sensitivities for a range of plant functional types yielded a more realistic distribution of litter stocks in all present biomes with the exception of boreal forests and projects a strong increase in global litter stocks by 35 Gt C and a concomitant small decrease in atmospheric CO₂ by 3 ppm by the end of this century. Despite a relatively strong increase in litter stocks, the modified parameterization results in less elevated wildfire emissions because of a litter redistribution towards more humid regions

Simultaneous assimilation of satellite and eddy covariance data for improving terrestrial water and carbon simulations at a semi-arid woodland site in Botswana

Terrestrial productivity in semi-arid woodlands is strongly susceptible to changes in precipitation, and semi-arid woodlands constitute an important element of the global water and carbon cycles. Here, we use the Carbon Cycle Data Assimilation System (CCDAS) to investigate the key parameters controlling ecological and hydrological activities for a semi-arid savanna woodland site in Maun, Botswana. Twenty-four eco-hydrological process parameters of a terrestrial ecosystem model are optimized against two data streams separately and simultaneously: daily averaged latent heat flux (LHF) derived from eddy covariance measurements, and decadal fraction of absorbed photosynthetically active radiation (FAPAR) derived from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Assimilation of both data streams LHF and FAPAR for the years 2000 and 2001 leads to improved agreement between measured and simulated quantities not only for LHF and FAPAR, but also for photosynthetic CO2 uptake. The mean uncertainty reduction (relative to the prior) over all parameters is 14.9% for the simultaneous assimilation of LHF and FAPAR, 8.5% for assimilating LHF only, and 6.1% for assimilating FAPAR only. The set of parameters with the highest uncertainty reduction is similar between assimilating only FAPAR or only LHF. The highest uncertainty reduction for all three cases is found for a parameter quantifying maximum plant-available soil moisture. This indicates that not only LHF but also satellite-derived FAPAR data can be used to constrain and indirectly observe hydrological quantities.JRC.H.7-Climate Risk Managemen