Dataset Information

Model structures amplify uncertainty in predicted soil carbon responses to climate change.

ABSTRACT: Large model uncertainty in projected future soil carbon (C) dynamics has been well documented. However, our understanding of the sources of this uncertainty is limited. Here we quantify the uncertainties arising from model parameters, structures and their interactions, and how those uncertainties propagate through different models to projections of future soil carbon stocks. Both the vertically resolved model and the microbial explicit model project much greater uncertainties to climate change than the conventional soil C model, with both positive and negative C-climate feedbacks, whereas the conventional model consistently predicts positive soil C-climate feedback. Our findings suggest that diverse model structures are necessary to increase confidence in soil C projection. However, the larger uncertainty in the complex models also suggests that we need to strike a balance between model complexity and the need to include diverse model structures in order to forecast soil C dynamics with high confidence and low uncertainty.

SUBMITTER: Shi Z

PROVIDER: S-EPMC5986763 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Model structures amplify uncertainty in predicted soil carbon responses to climate change.

Shi Zheng Z Crowell Sean S Luo Yiqi Y Moore Berrien B

Nature communications 20180604 1

Large model uncertainty in projected future soil carbon (C) dynamics has been well documented. However, our understanding of the sources of this uncertainty is limited. Here we quantify the uncertainties arising from model parameters, structures and their interactions, and how those uncertainties propagate through different models to projections of future soil carbon stocks. Both the vertically resolved model and the microbial explicit model project much greater uncertainties to climate change t ...[more]

PMID: 29867087

Similar Datasets

Project description:We report a metabarcoding study documenting the fungal taxa in 29 barren fellfield soils sampled from along a 1,650 km transect encompassing almost the entire maritime Antarctic (60-72°S) and the environmental factors structuring the richness, relative abundance, and taxonomic composition of three guilds and growth forms. The richness of the lichenised fungal guild, which accounted for 19% of the total fungal community, was positively associated with mean annual surface air temperature (MASAT), with an increase of 1.7 operational taxonomic units (OTUs) of lichenised fungi per degree Celsius rise in air temperature. Soil Mn concentration, MASAT, C:N ratio, and pH value determined the taxonomic composition of the lichenised guild, and the relative abundance of the guild was best predicted by soil Mn concentration. There was a 3% decrease in the relative abundance of the saprotrophic fungal guild in the total community for each degree Celsius rise in air temperature, and the OTU richness of the guild, which accounted for 39% of the community, was negatively associated with Mn concentration. The taxonomic composition of the saprotrophic guild varied with MASAT, pH value, and Mn, NH4 +-N, and SO4 2- concentrations. The richness of the yeast community, which comprised 3% of the total fungal community, was positively associated with soil K concentration, with its composition being determined by C:N ratio. In contrast with a similar study in the Arctic, the relative abundance and richness of lichenised fungi declined between 60°S and 69°S, with those of saprotrophic Agaricales also declining sharply in soils beyond 63°S. Basidiomycota, which accounted for 4% of reads, were much less frequent than in vegetated soils at lower latitudes, with the Ascomycota (70% of reads) being the dominant phylum. We conclude that the richness, relative abundance, and taxonomic composition of guilds and growth forms of maritime Antarctic soil fungi are influenced by air temperature and edaphic factors, with implications for the soils of the region as its climate changes during the 21st century.

Project description:Ethiopia's economy is dominated by agriculture which is mainly rain-fed and subsistence. Climate change is expected to have an adverse impact particularly on crop production. Previous studies have shown large discrepancies in the magnitude and sometimes in the direction of the impact on crop production. We assessed the impact of climate change on growth and yield of maize and wheat in Ethiopia using a multi-crop model ensemble. The multi-model ensemble (n = 48) was set up using the agroecosystem modelling framework Expert-N. The framework is modular which facilitates combining different submodels for plant growth and soil processes. The multi-model ensemble was driven by climate change projections representing the mid of the century (2021-2050) from ten contrasting climate models downscaled to finer resolution. The contributions of different sources of uncertainty in crop yield prediction were quantified. The sensitivity of crop yield to elevated CO2, increased temperature, changes in precipitations and N fertilizer were also assessed. Our results indicate that grain yields were very sensitive to changes in [CO2], temperature and N fertilizer amounts where the responses were higher for wheat than maize. The response to change in precipitation was weak, which we attribute to the high water holding capacity of the soils due to high organic carbon contents at the study sites. This may provide the sufficient buffering capacity for extended time periods with low amounts of precipitation. Under the changing climate, wheat productivity will be a major challenge with a 36 to 40% reduction in grain yield by 2050 while the impact on maize was modest. A major part of the uncertainty in the projected impact could be attributed to differences in the crop growth models. A considerable fraction of the uncertainty could also be traced back to different soil water dynamics modeling approaches in the model ensemble, which is often ignored. Uncertainties varied among the studied crop species and cultivars as well. The study highlights significant impacts of climate change on wheat yield in Ethiopia whereby differences in crop growth models causes the large part of the uncertainties.

Project description:We can effectively monitor soil condition-and develop sound policies to offset the emissions of greenhouse gases-only with accurate data from which to define baselines. Currently, estimates of soil organic C for countries or continents are either unavailable or largely uncertain because they are derived from sparse data, with large gaps over many areas of the Earth. Here, we derive spatially explicit estimates, and their uncertainty, of the distribution and stock of organic C in the soil of Australia. We assembled and harmonized data from several sources to produce the most comprehensive set of data on the current stock of organic C in soil of the continent. Using them, we have produced a fine spatial resolution baseline map of organic C at the continental scale. We describe how we made it by combining the bootstrap, a decision tree with piecewise regression on environmental variables and geostatistical modelling of residuals. Values of stock were predicted at the nodes of a 3-arc-sec (approximately 90 m) grid and mapped together with their uncertainties. We then calculated baselines of soil organic C storage over the whole of Australia, its states and territories, and regions that define bioclimatic zones, vegetation classes and land use. The average amount of organic C in Australian topsoil is estimated to be 29.7 t ha(-1) with 95% confidence limits of 22.6 and 37.9 t ha(-1) . The total stock of organic C in the 0-30 cm layer of soil for the continent is 24.97 Gt with 95% confidence limits of 19.04 and 31.83 Gt. This represents approximately 3.5% of the total stock in the upper 30 cm of soil worldwide. Australia occupies 5.2% of the global land area, so the total organic C stock of Australian soil makes an important contribution to the global carbon cycle, and it provides a significant potential for sequestration. As the most reliable approximation of the stock of organic C in Australian soil in 2010, our estimates have important applications. They could support Australia's National Carbon Accounting System, help guide the formulation of policy around carbon offset schemes, improve Australia's carbon balances, serve to direct future sampling for inventory, guide the design of monitoring networks and provide a benchmark against which to assess the impact of changes in land cover, land management and climate on the stock of C in Australia. In this way, these estimates would help us to develop strategies to adapt and mitigate the effects of climate change.

Dataset Information

Model structures amplify uncertainty in predicted soil carbon responses to climate change.

Publications

Model structures amplify uncertainty in predicted soil carbon responses to climate change.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets