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"Thornton, Peter"
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global analysis of soil microbial biomass carbon, nitrogen and phosphorus in terrestrial ecosystems
AIM: To estimate the concentrations, stoichiometry and storage of soil microbial biomass carbon (C), nitrogen (N) and phosphorus (P) at biome and global scales. LOCATION: Global. METHOD: We collected 3422 data points to summarize the concentrations and stoichiometry of C, N and P in soils, soil microbial biomass at global and biome levels, and to estimate the global storage of soil microbial biomass C and N. RESULTS: The results show that concentrations of C, N and P in soils and soil microbial biomass vary substantially across biomes; the fractions of soil elements C, N and P in soil microbial biomass are 1.2, 2.6 and 8.0%, respectively. The best estimates of C:N:P stoichiometry for soil elements and soil microbial biomass are 287:17:1 and 42:6:1, respectively, at global scale, and they vary in a wide range among biomes. The vertical distribution of soil microbial biomass follows the distribution of roots up to 1 m depth. MAIN CONCLUSIONS: The global storage of soil microbial biomass C and N were estimated to be 16.7 Pg C and 2.6 Pg N in the 0–30 cm soil profiles, and 23.2 Pg C and 3.7 Pg N in the 0–100 cm soil profiles. We did not estimate P in soil microbial biomass due to insufficient data and insignificant correlation between soil total P and climate variables used for spatial extrapolation. The spatial patterns of soil microbial biomass C and N were consistent with those of soil organic C and total N, i.e. high density in northern high latitude, and low density in low latitudes and the Southern Hemisphere.
Journal Article
Global pattern and controls of soil microbial metabolic quotient
The microbial metabolic quotient (MMQ), microbial respiration per unit of biomass, is a fundamental factor controlling heterotrophic respiration, the largest carbon flux in soils. The magnitude and controls of MMQ at regional scale remain uncertain. We compiled a comprehensive data set of MMQ to investigate the global patterns and controls of MMQ in top 30 cm soils. Published MMQ values, generally measured in laboratory microcosms, were adjusted on ambient soil temperature using long-term (30 yr) average site soil temperature and a Q₁₀ = 2. The area-weighted global average of MMQ_Soil is estimated as 1.8 (1.5–2.2) (95% confidence interval) μmol C·h⁻¹·mmol⁻¹ microbial biomass carbon (MBC) with substantial variations across biomes and between cropland and natural ecosystems. Variation was most closely associated with biological factors, followed by edaphic and meteorological parameters. MMQ_Soil was greatest in sandy clay and sandy clay loam and showed a pH maximum of 6.7 ± 0.1 (mean ± se). At large scale, MMQ_Soil varied with latitude and mean annual temperature (MAT), and was negatively correlated with microbial N:P ratio, supporting growth rate theory. These trends led to large differences in MMQ_Soil between natural ecosystems and cropland. When MMQ was adjusted to 11°C (MMQ_Ref), the global MAT in the top 30 cm of soils, the area-weighted global averages of MMQ_Ref was 1.5 (1.3–1.8) μmol C-mmol MBC⁻¹·h⁻¹. The values, trends, and controls of MMQ_Soil add to our understanding of soil microbial influences on soil carbon cycling and could be used to represent microbial activity in global carbon models.
Journal Article
Incorporating phosphorus cycling into global modeling efforts: a worthwhile, tractable endeavor
Myriad field, laboratory, and modeling studies show that nutrient availability plays a fundamental role in regulating CO2 exchange between the Earth's biosphere and atmosphere, and in determining how carbon pools and fluxes respond to climatic change. Accordingly, global models that incorporate coupled climate–carbon cycle feedbacks made a significant advance with the introduction of a prognostic nitrogen cycle. Here we propose that incorporating phosphorus cycling represents an important next step in coupled climate–carbon cycling model development, particularly for lowland tropical forests where phosphorus availability is often presumed to limit primary production. We highlight challenges to including phosphorus in modeling efforts and provide suggestions for how to move forward.
Journal Article
Gridded daily weather data for North America with comprehensive uncertainty quantification
Access to daily high-resolution gridded surface weather data based on direct observations and over long time periods is essential for many studies and applications including vegetation, wildlife, soil health, hydrological modelling, and as driver data in Earth system models. We present Daymet V4, a 40-year daily meteorological dataset on a 1 km grid for North America, Hawaii, and Puerto Rico, providing temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length. The dataset includes an objective quantification of uncertainty based on strict cross-validation analysis for temperature and precipitation results. The dataset represents several improvements from a previous version, and this data descriptor provides complete documentation for updated methods. Improvements include: reductions in the timing bias of input reporting weather station measurements; improvement to the three-dimensional regression model techniques in the core algorithm; and a novel approach to handling high elevation temperature measurement biases. We show cross-validation analyses with the underlying weather station data to demonstrate the technical validity of new dataset generation methods, and to quantify improved accuracy.
Measurement(s)
temperature of air • precipitation • shortwave radiation • vapor pressure of air • snowpack
Technology Type(s)
digital curation
Factor Type(s)
elevation • geographic location
Sample Characteristic - Environment
land surface
Sample Characteristic - Location
North America
Machine-accessible metadata file describing the reported data:
https://doi.org/10.6084/m9.figshare.14538075
Journal Article
The sensitivity of the forest carbon budget shifts across processes along with stand development and climate change
The future trajectory of atmospheric CO₂ concentration depends on the development of the terrestrial carbon sink, which in turn is influenced by forest dynamics under changing environmental conditions. An in-depth understanding of model sensitivities and uncertainties in non-steady-state conditions is necessary for reliable and robust projections of forest development and under scenarios of global warming and CO₂ enrichment. Here, we systematically assessed if a biogeochemical process-based model (3D-CMCC-CNR), which embeds similarities with many other vegetation models, applied in simulating net primary productivity (NPP) and standing woody biomass (SWB), maintained a consistent sensitivity to its 55 input parameters through time, during forest ageing and structuring as well as under climate change scenarios. Overall, the model applied at three contrasting European forests showed low sensitivity to the majority of its parameters. Interestingly, model sensitivity to parameters varied through the course of >100 yr of simulations. In particular, the model showed a large responsiveness to the allometric parameters used for initialize forest carbon and nitrogen pools early in forest simulation (i.e., for NPP up to ∼37%, 256 g C·m−2·yr−1 and for SWB up to ∼90%, 65 Mg C/ha, when compared to standard simulation), with this sensitivity decreasing sharply during forest development. At medium to longer time scales, and under climate change scenarios, the model became increasingly more sensitive to additional and/or different parameters controlling biomass accumulation and autotrophic respiration (i.e., for NPP up to ∼30%, 167 g C·m−2·yr−1 and for SWB up to ∼24%, 64 Mg C/ha, when compared to standard simulation). Interestingly, model outputs were shown to be more sensitive to parameters and processes controlling stand development rather than to climate change (i.e., warming and changes in atmospheric CO₂ concentration) itself although model sensitivities were generally higher under climate change scenarios. Our results suggest the need for sensitivity and uncertainty analyses that cover multiple temporal scales along forest developmental stages to better assess the potential of future forests to act as a global terrestrial carbon sink.
Journal Article
Representing the function and sensitivity of coastal interfaces in Earth system models
Between the land and ocean, diverse coastal ecosystems transform, store, and transport material. Across these interfaces, the dynamic exchange of energy and matter is driven by hydrological and hydrodynamic processes such as river and groundwater discharge, tides, waves, and storms. These dynamics regulate ecosystem functions and Earth’s climate, yet global models lack representation of coastal processes and related feedbacks, impeding their predictions of coastal and global responses to change. Here, we assess existing coastal monitoring networks and regional models, existing challenges in these efforts, and recommend a path towards development of global models that more robustly reflect the coastal interface.
Coastal systems are hotspots of ecological, geochemical and economic activity, yet their dynamics are not accurately represented in global models. In this Review, Ward and colleagues assess the current state of coastal science and recommend approaches for including the coastal interface in predictive models.
Journal Article
Human-induced greening of the northern extratropical land surface
Observed northern extratropical land greening is consistent with anthropogenic forcings, where greenhouse gases play a dominant role, but not with simulations that include only natural forcings and internal climate variability.
Significant land greening in the northern extratropical latitudes (NEL) has been documented through satellite observations during the past three decades
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. This enhanced vegetation growth has broad implications for surface energy, water and carbon budgets, and ecosystem services across multiple scales
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. Discernible human impacts on the Earth’s climate system have been revealed by using statistical frameworks of detection–attribution
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. These impacts, however, were not previously identified on the NEL greening signal, owing to the lack of long-term observational records, possible bias of satellite data, different algorithms used to calculate vegetation greenness, and the lack of suitable simulations from coupled Earth system models (ESMs). Here we have overcome these challenges to attribute recent changes in NEL vegetation activity. We used two 30-year-long remote-sensing-based leaf area index (LAI) data sets
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, simulations from 19 coupled ESMs with interactive vegetation, and a formal detection and attribution algorithm
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. Our findings reveal that the observed greening record is consistent with an assumption of anthropogenic forcings, where greenhouse gases play a dominant role, but is not consistent with simulations that include only natural forcings and internal climate variability. These results provide the first clear evidence of a discernible human fingerprint on physiological vegetation changes other than phenology and range shifts
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Journal Article
Urban warming advances spring phenology but reduces the response of phenology to temperature in the conterminous United States
Urbanization has caused environmental changes, such as urban heat islands (UHIs), that affect terrestrial ecosystems. However, how and to what extent urbanization affects plant phenology remains relatively unexplored. Here, we investigated the changes in the satellite-derived start of season (SOS) and the covariation between SOS and temperature (RT
) in 85 large cities across the conterminous United States for the period 2001–2014. We found that 1) the SOS came significantly earlier (6.1 ± 6.3 d) in 74 cities and RT
was significantly weaker (0.03 ± 0.07) in 43 cities when compared with their surrounding rural areas (P < 0.05); 2) the decreased magnitude in RT
mainly occurred in cities in relatively cold regions with an annual mean temperature <17.3 °C (e.g., Minnesota, Michigan, and Pennsylvania); and 3) the magnitude of urban−rural difference in both SOS and RT
was primarily correlated with the intensity of UHI. Simulations of two phenology models further suggested that more and faster heat accumulation contributed to the earlier SOS, while a decrease in required chilling led to a decline in RT
magnitude in urban areas. These findings provide observational evidence of a reduced covariation between temperature and SOS in major US cities, implying the response of spring phenology to warming conditions in nonurban environments may decline in the warming future.
Journal Article
Quantification of human contribution to soil moisture-based terrestrial aridity
Current knowledge of the spatiotemporal patterns of changes in soil moisture-based terrestrial aridity has considerable uncertainty. Using Standardized Soil Moisture Index (SSI) calculated from multi-source merged data sets, we find widespread drying in the global midlatitudes, and wetting in the northern subtropics and in spring between 45°N–65°N, during 1971–2016. Formal detection and attribution analysis shows that human forcings, especially greenhouse gases, contribute significantly to the changes in 0–10 cm SSI during August–November, and 0–100 cm during September–April. We further develop and apply an emergent constraint method on the future SSI’s signal-to-noise (S/N) ratios and trends under the Shared Socioeconomic Pathway 5-8.5. The results show continued significant presence of human forcings and more rapid drying in 0–10 cm than 0–100 cm. Our findings highlight the predominant human contributions to spatiotemporally heterogenous terrestrial aridification, providing a basis for drought and flood risk management.
Historical latitudinal and seasonal trends in global soil moisture aridity are attributable to greenhouse gas emissions.
Journal Article