Search USGSSearch

Geology topics

J. Strauss

Publications and source records attributed to J. Strauss.

3 recordsLinked to original sources

A simplified, data-constrained approach to estimate the permafrost carbon–climate feedback

We present an approach to estimate the feedback from large-scale thawing of permafrost soils using a simplified, data-constrained model that combines three elements: soil carbon (C) maps and profiles to identify the distribution and type of C in permafrost soils; incubation experiments to quantify the rates of C lost after thaw; and models of soil thermal dynamics in response to climate warming. We call the approach the Permafrost Carbon Network Incubation–Panarctic Thermal scaling approach (PInc-PanTher). The approach assumes that C stocks do not decompose at all when frozen, but once thawed follow set decomposition trajectories as a function of soil temperature. The trajectories are determined according to a three-pool decomposition model fitted to incubation data using parameters specific to soil horizon types. We calculate litterfall C inputs required to maintain steady-state C balance for the current climate, and hold those inputs constant. Soil temperatures are taken from the soil thermal modules of ecosystem model simulations forced by a common set of future climate change anomalies under two warming scenarios over the period 2010 to 2100. Under a medium warming scenario (RCP4.5), the approach projects permafrost soil C losses of 12.2–33.4 Pg C; under a high warming scenario (RCP8.5), the approach projects C losses of 27.9–112.6 Pg C. Projected C losses are roughly linearly proportional to global temperature changes across the two scenarios. These results indicate a global sensitivity of frozen soil C to climate change ( γ sensitivity) of −14 to −19 Pg C °C −1 on a 100 year time scale. For CH 4 emissions, our approach assumes a fixed saturated area and that increases in CH 4 emissions are related to increased heterotrophic respiration in anoxic soil, yielding CH 4 emission increases of 7% and 35% for the RCP4.5 and RCP8.5 scenarios, respectively, which add an additional greenhouse gas forcing of approximately 10–18%. The simplified approach presented here neglects many important processes that may amplify or mitigate C release from permafrost soils, but serves as a data-constrained estimate on the forced, large-scale permafrost C response to warming.

Philosophical Transactions of the Royal Society A:

A new data set for estimating organic carbon storage to 3 m depth in soils of the northern circumpolar permafrost region

High-latitude terrestrial ecosystems are key components in the global carbon cycle. The Northern Circumpolar Soil Carbon Database (NCSCD) was developed to quantify stocks of soil organic carbon (SOC) in the northern circumpolar permafrost region (a total area of 18.7 × 10 6 km 2 ). The NCSCD is a geographical information system (GIS) data set that has been constructed using harmonized regional soil classification maps together with pedon data from the northern permafrost region. Previously, the NCSCD has been used to calculate SOC storage to the reference depths 0–30 cm and 0–100 cm (based on 1778 pedons). It has been shown that soils of the northern circumpolar permafrost region also contain significant quantities of SOC in the 100–300 cm depth range, but there has been no circumpolar compilation of pedon data to quantify this deeper SOC pool and there are no spatially distributed estimates of SOC storage below 100 cm depth in this region. Here we describe the synthesis of an updated pedon data set for SOC storage (kg C m -2 ) in deep soils of the northern circumpolar permafrost regions, with separate data sets for the 100–200 cm (524 pedons) and 200–300 cm (356 pedons) depth ranges. These pedons have been grouped into the North American and Eurasian sectors and the mean SOC storage for different soil taxa (subdivided into Gelisols including the sub-orders Histels, Turbels, Orthels, permafrost-free Histosols, and permafrost-free mineral soil orders) has been added to the updated NCSCDv2. The updated version of the data set is freely available online in different file formats and spatial resolutions that enable spatially explicit applications in GIS mapping and terrestrial ecosystem models. While this newly compiled data set adds to our knowledge of SOC in the 100–300 cm depth range, it also reveals that large uncertainties remain. Identified data gaps include spatial coverage of deep (> 100 cm) pedons in many regions as well as the spatial extent of areas with thin soils overlying bedrock and the quantity and distribution of massive ground ice. An open access data-portal for the pedon data set and the GIS-data sets is available online at http://bolin.su.se/data/ncscd/ .

Earth System Science Data

The effect of resource quantity and resource stoichiometry on microbial carbon-use-efficiency

The carbon-use-efficiency (CUE) of microorganisms is an important parameter in determining ecosystem-level carbon (C) cycling; however, little is known about how variance in resources affects microbial CUE. To elucidate how resource quantity and resource stoichiometry affect microbial CUE, we cultured four microorganisms - two fungi (Aspergillus nidulans and Trichoderma harzianum) and two bacteria (Pectobacterium carotovorum and Verrucomicrobium spinosum) - under 12 unique C, nitrogen (N) and phosphorus (P) ratios. Whereas the CUE of A. nidulans was strongly affected by C, bacterial CUE was more strongly affected by mineral nutrients (N and P). Specifically, CUE in P. carotovorum was positively correlated with P, while CUE of V. spinosum primarily depended on N. This resulted in a positive relationship between fungal CUE and resource C : nutrient stoichiometry and a negative relationship between bacterial CUE and resource C : nutrient stoichiometry. The difference in the direction of the relationship between CUE and C : nutrient for fungi vs. bacteria was consistent with differences in biomass stoichiometry and suggested that fungi have a higher C demand than bacteria. These results suggest that the links between biomass stoichiometry, resource demand and CUE may provide a mechanism for commonly observed temporal and spatial patterns in microbial community structure and function in natural habitats.

FEMS Microbiology Ecology