Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation

Plant functional traits can predict community assembly and ecosystem functioning and are thus widely used in global models of vegetation dynamics and land–climate feedbacks. Still, we lack a global understanding of how land and climate affect plant traits. A previous global analysis of six traits observed two main axes of variation: (1) size variation at the organ and plant level and (2) leaf economics balancing leaf persistence against plant growth potential. The orthogonality of these two axes suggests they are differently influenced by environmental drivers. We find that these axes persist in a global dataset of 17 traits across more than 20,000 species. We find a dominant joint effect of climate and soil on trait variation. Additional independent climate effects are also observed across most traits, whereas independent soil effects are almost exclusively observed for economics traits. Variation in size traits correlates well with a latitudinal gradient related to water or energy limitation. In contrast, variation in economics traits is better explained by interactions of climate with soil fertility. These findings have the potential to improve our understanding of biodiversity patterns and our predictions of climate change impacts on biogeochemical cycles

Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation

Plant functional traits can predict community assembly and ecosystem functioning and are thus widely used in global models of vegetation dynamics and land–climate feedbacks. Still, we lack a global understanding of how land and climate affect plant traits. A previous global analysis of six traits observed two main axes of variation: (1) size variation at the organ and plant level and (2) leaf economics balancing leaf persistence against plant growth potential. The orthogonality of these two axes suggests they are differently influenced by environmental drivers. We find that these axes persist in a global dataset of 17 traits across more than 20,000 species. We find a dominant joint effect of climate and soil on trait variation. Additional independent climate effects are also observed across most traits, whereas independent soil effects are almost exclusively observed for economics traits. Variation in size traits correlates well with a latitudinal gradient related to water or energy limitation. In contrast, varia- tion in economics traits is better explained by interactions of climate with soil fertility. These findings have the potential to improve our understanding of biodiversity patterns and our predictions of climate change impacts on biogeochemical cycles.Environmental Biolog

Identifying Multiple Spatiotemporal Patterns: A Refined View on Terrestrial Photosynthetic Activity

Information retrieval from spatiotemporal data cubes is key to earth system sciences. Respective analyses need to consider two fundamental issues: First, natural phenomena fluctuate on dierent time scales. Second, these characteristic temporal patterns induce multiple geographical gradients. Here we propose an integrated approach of subsignal extraction and dimensionality reduction to extract geographical gradients on multiple time scales. The approach is exemplified using global remote sensing estimates of photosynthetic activity. A wide range of partly well interpretable gradients is retrieved. For instance, well known climate{induced anomalies in FAPAR over Africa and South America during the last severe ENSO event are identied. Also, the precise geographical patterns of the annual seasonal cycle and its phasing are isolated. Other features lead to new questions on the underlying environmental dynamics. Our method can provide benchmarks for comparisons of data cubes, model runs, and thus be used as a basis for sophisticated model performance evaluations.JRC.H.5-Land Resources Managemen

Extreme events in gross primary production: A characterization across continents

Climate extremes can affect the functioning of terrestrial ecosystems, for instance via a reduction of the photosynthetic capacity or alterations of respiratory processes. Yet the dominant regional and seasonal effects of hydrometeorological extremes are still not well documented and in the focus of this paper. Specifically, we quantify and characterize the role of large spatiotemporal extreme events in gross primary production (GPP) as triggers of continental anomalies. We also investigate seasonal dynamics of extreme impacts on continental GPP anomalies. We find that the 50 largest positive extremes (i.e., statistically unusual increases in carbon uptake rates) and negative extremes (i.e., statistically unusual decreases in carbon uptake rates) on each continent can explain most of the continental variation in GPP, which is in line with previous results obtained at the global scale. We show that negative extremes are larger than positive ones and demonstrate that this asymmetry is particularly strong in South America and Europe. Our analysis indicates that the overall impacts and the spatial extents of GPP extremes are power-law distributed with exponents that vary little across continents. Moreover, we show that on all continents and for all data sets the spatial extents play a more important role for the overall impact of GPP extremes compared to the durations or maximal GPP. An analysis of possible causes across continents indicates that most negative extremes in GPP can be attributed clearly to water scarcity, whereas extreme temperatures play a secondary role. However, for Europe, South America and Oceania we also identify fire as an important driver. Our findings are consistent with remote sensing products. An independent validation against a literature survey on specific extreme events supports our results to a large extent

A novel bias correction methodology for climate impact simulations

Understanding, quantifying and attributing the impacts of extreme weather and climate events in the terrestrial biosphere is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit biases in their output that hinder any straightforward assessment of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies, most of which have been criticized for physical inconsistency and the nonpreservation of the multivariate correlation structure. In this study, we introduce a novel, resampling-based bias correction scheme that fully preserves the physical consistency and multivariate correlation structure of the model output. This procedure strongly improves the representation of climatic extremes and variability in a large regional climate model ensemble (HadRM3P, climateprediction.net/weatherathome), which is illustrated for summer extremes in temperature and rainfall over Central Europe. Moreover, we simulate biosphere–atmosphere fluxes of carbon and water using a terrestrial ecosystem model (LPJmL) driven by the bias-corrected climate forcing. The resampling-based bias correction yields strongly improved statistical distributions of carbon and water fluxes, including the extremes. Our results thus highlight the importance of carefully considering statistical moments beyond the mean for climate impact simulations. In conclusion, the present study introduces an approach to alleviate climate model biases in a physically consistent way and demonstrates that this yields strongly improved simulations of climate extremes and associated impacts in the terrestrial biosphere. A wider uptake of our methodology by the climate and impact modelling community therefore seems desirable for accurately quantifying changes in past, current and future extremes

Statistical properties of random CO2 flux measurement uncertainty inferred from model residuals

Information about the uncertainties associated with eddy covariance measurements of surface-atmosphere CO2 exchange is needed for data assimilation and inverse analyses to estimate model parameters, validation of ecosystem models against flux data, as well as multi-site synthesis activities (e.g., regional to continental integration) and policy decision-making. While model residuals (mismatch between fitted model predictions and measured fluxes) can potentially be analyzed to infer data uncertainties, the resulting uncertainty estimates may be sensitive to the particular model chosen. Here we use 10 site-years of data from the CarboEurope program, and compare the statistical properties of the inferred random flux measurement error calculated first using residuals from five different models, and secondly using paired observations made under similar environmental conditions. Spectral analysis of the model predictions indicated greater persistence (i.e., autocorrelation or memory) compared to the measured values. Model residuals exhibited weaker temporal correlation, but were not uncorrelated white noise. Random flux measurement uncertainty, expressed as a standard deviation, was found to vary predictably in relation to the expected magnitude of the flux, in a manner that was nearly identical (for negative, but not positive, fluxes) to that reported previously for forested sites. Uncertainty estimates were generally comparable whether the uncertainty was inferred from model residuals or paired observations, although the latter approach resulted in somewhat smaller estimates. Higher order moments (e.g., skewness and kurtosis) suggested that for fluxes close to zero, the measurement error is commonly skewed and leptokurtic. Skewness could not be evaluated using the paired observation approach, because differencing of paired measurements resulted in a symmetric distribution of the inferred error. Patterns were robust and not especially sensitive to the model used, although more flexible models, which did not impose a particular functional form on relationships between environmental drivers and modeled fluxes, appeared to give the best results. We conclude that evaluation of flux measurement errors from model residuals is a viable alternative to the standard paired observation approach

Global Convergence in the Temperature Sensitivity of Respiration at Ecosystem Level

The respiratory release of CO₂ from the land surface is a major flux in the global carbon cycle, antipodal to photosynthetic CO₂ uptake. Understanding the sensitivity of respiratory processes to temperature is central for quantifying the climate-carbon cycle feedback. Here, we approximate the sensitivity of terrestrial ecosystem respiration to air temperature (Q₁₀) across 60 FLUXNET sites using a methodology that circumvents confounding effects. Contrary to previous findings, our results suggest that Q₁₀ is independent of mean annual temperature, does not differ among biomes, and is confined to values around 1.4 ({+/-}0.1). The strong relation between photosynthesis and respiration, instead, is highly variable among sites. Overall, the results partly explain a less pronounced climate-carbon cycle feedback than suggested by current carbon cycle climate models