•Interannual variability in net carbon exchange is large relative to its mean.•Many biophysical variables explain this variability, and differ by region.•Ecosystem photosynthesis modulated net carbon ...exchange more than respiration.•A few ecosystems are on the verge of switching from a carbon sink to sources.
As the lifetime of regional flux networks approach twenty years, there is a growing number of papers that have published long term records (5 years or more) of net carbon fluxes between ecosystems and the atmosphere. Unanswered questions from this body of work are: 1) how variable are carbon fluxes on a year to year basis?; 2) what are the biophysical factors that may cause interannual variability and/or temporal trends in carbon fluxes?; and 3) how does the biophysical control on this carbon flux variability differ by climate and ecological spaces? To address these questions, we surveyed published data from 59 sites that reported on five or more years of continuous measurements, yielding 544 site-years of data.
We found that the standard deviation of the interannual variability in net ecosystem carbon exchange (162gCm−2y−1) is large relative to its population mean (−200gCm−2 y−1). Broad-leaved evergreen forests and crops experienced the greatest absolute variability in interannual net carbon exchange (greater than ±300gCm−2y−1) and boreal evergreen forests and maritime wetlands were among the least variable (less than ±40 gCm−2y−1).
A disproportionate fraction of the yearly variability in net ecosystem exchange was associated with biophysical factors that modulated ecosystem photosynthesis rather than ecosystem respiration. Yet, there was appreciable and statistically significant covariance between ecosystem photosynthesis and respiration. Consequently, biophysical conditions that conspired to increase ecosystem photosynthesis to from one year to the next were associated with an increase in ecosystem respiration, and vice versa; on average, the year to year change in respiration was 40% as large as the year to year change in photosynthesis. The analysis also identified sets of ecosystems that are on the verge of switching from being carbon sinks to carbon sources. These include sites in the Arctic tundra, the evergreen forests in the Pacific northwest and some grasslands, where year to year changes in respiration are outpacing those in photosynthesis.
While a select set of climatic and ecological factors (e.g. light, rainfall, temperature, phenology) played direct and indirect roles on this variability, their impact differed conditionally, as well as by climate and ecological spaces. For example, rainfall had both positive and negative effects. Deficient rainfall caused a physiological decline in photosynthesis in temperate and semi-arid regions. Too much rain, in the humid tropics, limited photosynthesis by limiting light. In peatlands and tundra, excess precipitation limited ecosystem respiration when it raised the water table to the surface. For deciduous forests, warmer temperatures lengthened the growing season, increasing photosynthesis, but this effect also increased soil respiration.
Finally, statistical analysis was performed to evaluate the detection limit of trends; we computed the confidence intervals of trends in multi-year carbon fluxes that need to be resolved to conclude whether the differences are to be attributed to randomness or biophysical forcings. Future studies and reports on interannual variations need to consider the role of the duration of the time series on random errors when quantifying potential trends and extreme events.
Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour ...is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.
The effect of temperature and the influence of fresh substrate addition on soil organic matter decomposition are two key factors we need to understand to forecast soil carbon dynamics under climate ...change and rising CO2 levels. Here we perform a laboratory incubation experiment to address the following questions: 1) Does the temperature sensitivity differ between freshly added organic matter and bulk soil carbon? 2) Does the addition of fresh organic matter stimulate the decomposition of soil organic matter (“priming effect”)? 3) If so, does this priming effect depend on temperature? In our study, we incubated sieved soil samples without and with two labelled plant litters with different 13C signals for 199 days. The incubations were performed with two diurnal temperature treatments (5–15 °C, 15–25 °C) in a flow-through soil incubation system. Soil CO2 production was continuously monitored with an infrared gas analyser, while the 13C signal was determined from gas samples. Phospholipid fatty acids (PLFA) were used to quantify microbial biomass. We observed that the instantaneous temperature sensitivity initially did not differ between the original and the amended soil. However in the amended treatment the temperature sensitivity slightly but significantly increased during the incubation time, as did the PLFA amount from microbial biomass. Further, we found that addition of fresh plant material increased the rate of decomposition of the original soil organic matter. On a relative basis, this stimulation was similar in the warm and cold treatments (46% and 52%, respectively). Overall our study contrasts the view of a simple physico-chemically derived substrate–temperature sensitivity relationship of decomposition. Our results rather request an explicit consideration of microbial processes such as growth and priming effects.
► We studied the effect of temperature and fresh litter addition on bulk soil carbon. ► Litter addition increased decomposition of soil derived C (positive priming). ► Relative stimulation was similar at different temperature treatments. ► Along the entire incubation time temperature sensitivity rose in amended soils. ► Substrate complexity is not the most important determinant for temperature sensitivity.
Year-to-year changes in carbon uptake by terrestrial ecosystems have an essential role in determining atmospheric carbon dioxide concentrations
. It remains uncertain to what extent temperature and ...water availability can explain these variations at the global scale
. Here we use factorial climate model simulations
and show that variability in soil moisture drives 90 per cent of the inter-annual variability in global land carbon uptake, mainly through its impact on photosynthesis. We find that most of this ecosystem response occurs indirectly as soil moisture-atmosphere feedback amplifies temperature and humidity anomalies and enhances the direct effects of soil water stress. The strength of this feedback mechanism explains why coupled climate models indicate that soil moisture has a dominant role
, which is not readily apparent from land surface model simulations and observational analyses
. These findings highlight the need to account for feedback between soil and atmospheric dryness when estimating the response of the carbon cycle to climatic change globally
, as well as when conducting field-scale investigations of the response of the ecosystem to droughts
. Our results show that most of the global variability in modelled land carbon uptake is driven by temperature and vapour pressure deficit effects that are controlled by soil moisture.
The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real ...experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers.
Abstract
Global vegetation and associated ecosystem services critically depend on soil moisture availability which has decreased in many regions during the last three decades. While spatial patterns ...of vegetation sensitivity to global soil water have been recently investigated, long-term changes in vegetation sensitivity to soil water availability are still unclear. Here we assess global vegetation sensitivity to soil moisture during 1982-2017 by applying explainable machine learning with observation-based leaf area index (LAI) and hydro-climate anomaly data. We show that LAI sensitivity to soil moisture significantly increases in many semi-arid and arid regions. LAI sensitivity trends are associated with multiple hydro-climate and ecological variables, and strongest increasing trends occur in the most water-sensitive regions which additionally experience declining precipitation. State-of-the-art land surface models do not reproduce this increasing sensitivity as they misrepresent water-sensitive regions and sensitivity strength. Our sensitivity results imply an increasing ecosystem vulnerability to water availability which can lead to exacerbated reductions in vegetation carbon uptake under future intensified drought, consequently amplifying climate change.
Linking plant and ecosystem functional biogeography Reichstein, Markus; Bahn, Michael; Mahecha, Miguel D. ...
Proceedings of the National Academy of Sciences - PNAS,
09/2014, Letnik:
111, Številka:
38
Journal Article
Recenzirano
Odprti dostop
Significance This article defines ecosystem functional properties, which can be derived from long-term observations of gas and energy exchange between ecosystems and the atmosphere, and shows that ...variations of those cannot be easily explained by classical climatological or biogeographical approaches such as plant functional types. Instead, we argue that plant traits have the potential to explain this variation, and we call for a stronger integration of research communities dedicated to plant traits and to ecosystem–atmosphere exchange.
Classical biogeographical observations suggest that ecosystems are strongly shaped by climatic constraints in terms of their structure and function. On the other hand, vegetation function feeds back on the climate system via biosphere–atmosphere exchange of matter and energy. Ecosystem-level observations of this exchange reveal very large functional biogeographical variation of climate-relevant ecosystem functional properties related to carbon and water cycles. This variation is explained insufficiently by climate control and a classical plant functional type classification approach. For example, correlations between seasonal carbon-use efficiency and climate or environmental variables remain below 0.6, leaving almost 70% of variance unexplained. We suggest that a substantial part of this unexplained variation of ecosystem functional properties is related to variations in plant and microbial traits. Therefore, to progress with global functional biogeography, we should seek to understand the link between organismic traits and flux-derived ecosystem properties at ecosystem observation sites and the spatial variation of vegetation traits given geoecological covariates. This understanding can be fostered by synergistic use of both data-driven and theory-driven ecological as well as biophysical approaches.
Although a key driver of Earth's climate system, global land-atmosphere energy fluxes are poorly constrained. Here we use machine learning to merge energy flux measurements from FLUXNET eddy ...covariance towers with remote sensing and meteorological data to estimate global gridded net radiation, latent and sensible heat and their uncertainties. The resulting FLUXCOM database comprises 147 products in two setups: (1) 0.0833° resolution using MODIS remote sensing data (RS) and (2) 0.5° resolution using remote sensing and meteorological data (RS + METEO). Within each setup we use a full factorial design across machine learning methods, forcing datasets and energy balance closure corrections. For RS and RS + METEO setups respectively, we estimate 2001-2013 global (±1 s.d.) net radiation as 75.49 ± 1.39 W m
and 77.52 ± 2.43 W m
, sensible heat as 32.39 ± 4.17 W m
and 35.58 ± 4.75 W m
, and latent heat flux as 39.14 ± 6.60 W m
and 39.49 ± 4.51 W m
(as evapotranspiration, 75.6 ± 9.8 × 10
km
yr
and 76 ± 6.8 × 10
km
yr
). FLUXCOM products are suitable to quantify global land-atmosphere interactions and benchmark land surface model simulations.
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