Recent global decline of CO2 fertilization effects on vegetation photosynthesis

Science ◽  
2020 ◽  
Vol 370 (6522) ◽  
pp. 1295-1300 ◽  
Author(s):  
Songhan Wang ◽  
Yongguang Zhang ◽  
Weimin Ju ◽  
Jing M. Chen ◽  
Philippe Ciais ◽  
...  
Keyword(s):  
Climate Warming ◽  
Negative Feedback ◽  
Temporal Dynamics ◽  
Water Current ◽  
Nutrient Concentrations ◽  
Co2 Fertilization ◽  
Fertilization Effect ◽  
Climatic System ◽  
Fertilization Effects

The enhanced vegetation productivity driven by increased concentrations of carbon dioxide (CO2) [i.e., the CO2 fertilization effect (CFE)] sustains an important negative feedback on climate warming, but the temporal dynamics of CFE remain unclear. Using multiple long-term satellite- and ground-based datasets, we showed that global CFE has declined across most terrestrial regions of the globe from 1982 to 2015, correlating well with changing nutrient concentrations and availability of soil water. Current carbon cycle models also demonstrate a declining CFE trend, albeit one substantially weaker than that from the global observations. This declining trend in the forcing of terrestrial carbon sinks by increasing amounts of atmospheric CO2 implies a weakening negative feedback on the climatic system and increased societal dependence on future strategies to mitigate climate warming.

scholarly journals On the non-stationarity of hydrological response in anthropogenically unaffected catchments: an Australian perspective

2017 ◽  
Vol 21 (1) ◽  
pp. 281-294 ◽  
Author(s):  
Hoori Ajami ◽  
Ashish Sharma ◽  
Lawrence E. Band ◽  
Jason P. Evans ◽  
Narendra K. Tuteja ◽  
...  
Keyword(s):  
Annual Runoff ◽  
Hydrologic Response ◽  
Hydrological Response ◽  
Co2 Fertilization ◽  
Radiative Forcings ◽  
First Order ◽  
Structural Responses ◽  
Fertilization Effect ◽  
Long Term Trend

Abstract. Increases in greenhouse gas concentrations are expected to impact the terrestrial hydrologic cycle through changes in radiative forcings and plant physiological and structural responses. Here, we investigate the nature and frequency of non-stationary hydrological response as evidenced through water balance studies over 166 anthropogenically unaffected catchments in Australia. Non-stationarity of hydrologic response is investigated through analysis of long-term trend in annual runoff ratio (1984–2005). Results indicate that a significant trend (p < 0.01) in runoff ratio is evident in 20 catchments located in three main ecoregions of the continent. Runoff ratio decreased across the catchments with non-stationary hydrologic response with the exception of one catchment in northern Australia. Annual runoff ratio sensitivity to annual fractional vegetation cover was similar to or greater than sensitivity to annual precipitation in most of the catchments with non-stationary hydrologic response indicating vegetation impacts on streamflow. We use precipitation–productivity relationships as the first-order control for ecohydrologic catchment classification. A total of 12 out of 20 catchments present a positive precipitation–productivity relationship possibly enhanced by CO2 fertilization effect. In the remaining catchments, biogeochemical and edaphic factors may be impacting productivity. Results suggest vegetation dynamics should be considered in exploring causes of non-stationary hydrologic response.

scholarly journals Plant Functional Types Differ in Their Long-term Nutrient Response to eCO2 in an Extensive Grassland

Ecosystems ◽  
2021 ◽  
Author(s):  
Ruben Seibert ◽  
Louise C. Andresen ◽  
Klaus A. Jarosch ◽  
Gerald Moser ◽  
Claudia I. Kammann ◽  
...  
Keyword(s):  
Plant Biomass ◽  
Nutrient Concentrations ◽  
Plant Nutrient ◽  
Affected Plant ◽  
Nutrient Yield ◽  
Fertilization Effect ◽  
Face Experiment ◽  
Yield Responses ◽  
Plant Biomass Production

AbstractIncreasing atmospheric CO2 enhances plant biomass production and may thereby change nutrient concentrations in plant tissues. The objective of this study was to identify the effect of elevated atmospheric CO2 concentrations on nutrient concentrations of grassland biomass that have been grown for 16 years (1998–2013). The grassland biomass grown at the extensively managed Giessen FACE experiment, fumigated with ambient and elevated CO2 (aCO2; eCO2; +20%) was harvested twice annually. Concentrations of C, N, P, K, Ca, Mg, Mn, Fe, Cu and Zn were determined separately for grasses, forbs and legumes. Under eCO2, the concentration of N was reduced in grasses, Ca was reduced in grasses and forbs, P was reduced in grasses but increased in legumes, Mg concentration was reduced in grasses, forbs and legumes and K was reduced in grasses but increased in forbs. The nutrient yield (in g nutrient yield of an element per m−2) of most elements indicated negative yield responses at a zero biomass response to eCO2 for grasses. K and Zn nutrient yields responded positively to eCO2 in forbs and Mn and Fe responded positively in forbs and legumes. The results suggest that under eCO2 the nutrient concentrations were not diluted by the CO2 fertilization effect. Rather, altered plant nutrient acquisitions via changed physiological mechanisms prevail for increased C assimilation under eCO2. Furthermore, other factors such as water or nutrient availability affected plant nutrient concentrations under eCO2.

scholarly journals The quasi-equilibrium framework re-visited: analyzing long-term CO<sub>2</sub> enrichment responses in plant-soil models

2018 ◽  
Author(s):  
Mingkai Jiang ◽  
Sönke Zaehle ◽  
Martin G. De Kauwe ◽  
Anthony P. Walker ◽  
Silvia Caldararu ◽  
...  
Keyword(s):  
Soil Nutrient ◽  
Analytical Framework ◽  
Plant Responses ◽  
Quasi Equilibrium ◽  
Co2 Fertilization ◽  
Nitrogen Pool ◽  
Plant Soil ◽  
Fertilization Effect ◽  
Co2 Fertilization Effect

Abstract. Elevated carbon dioxide (CO2) can increase plant growth, but the magnitude of this CO2 fertilization effect is modified by soil nutrient availability. Predicting how nutrient availability affects plant responses to elevated CO2 is a key consideration for ecosystem models, and many modelling groups have moved to, or are moving towards, incorporating nutrient limitation in their models. The choice of assumptions to represent nutrient cycling processes has a major impact on model predictions, but it can be difficult to attribute outcomes to specific assumptions in complex ecosystem simulation models. Here we revisit the quasi-equilibrium (QE) analytical framework introduced by Comins &amp; McMurtrie (1993) and explore the consequences of specific model assumptions for ecosystem net primary productivity. We review the literature applying this framework to plant-soil models, and then examine the effect of several new assumptions on predicted plant responses to elevated CO2. Examination of alternative assumptions for plant nitrogen uptake showed that a linear function of the mineral nitrogen pool or a saturating function of root biomass yield similar CO2 responses over time. In contrast, a saturating function of the mineral nitrogen pool yields no soil nutrient feedback at the very long-term, near-equilibrium timescale, meaning that a full CO2 fertilization effect on production is realized. We show that incorporating a priming effect on slow soil organic matter decomposition attenuates the nutrient feedback effect on production, leading to a strong medium-term CO2 response. Finally, we demonstrate that using a “potential NPP” approach to represent nutrient limitation of growth yields a relatively small CO2 fertilization effect across all timescales. Our results highlight that the QE analytical framework is effective for evaluating both the consequence and the mechanism through which different model assumptions affect predictions. To help constrain predictions of the future terrestrial carbon sink, we recommend use of this framework to analyze likely outcomes of new model assumptions before introducing them to complex model structures.

Climate Warming and Long-Term Trends in Saskatchewan Hay Yield

2016 ◽  
Vol 2 (12) ◽  
pp. 765-768 ◽  
Author(s):  
Paul G. Jefferson
Keyword(s):  
Climate Warming ◽  
Long Term Trends

Economic conditions and perceptions of immigrants as an economic threat in Europe: Temporal dynamics and mediating processes

2021 ◽  
pp. 002071522199352
Author(s):  
Boris Heizmann ◽  
Nora Huth
Keyword(s):  
Temporal Dynamics ◽  
European Social Survey ◽  
Primary Objective ◽  
Conflict Theory ◽  
Economic Conditions ◽  
Political Climate ◽  
Short Term ◽  
Economic Threat ◽  
Party Positions

This article addresses the extent to which economic downturns influence the perception of immigrants as an economic threat and through which channels this occurs. Our primary objective is an investigation of the specific mechanisms that connect economic conditions to the perception of immigrants as a threat. We therefore also contribute to theoretical discussions based on group threat and realistic group conflict theory by exposing the dominant source of competition relevant to these relationships. Furthermore, we investigate whether people react more sensitive to short-term economic dynamics within countries than to the long-term economic circumstances. Our database comprises all waves of the European Social Survey from 2002 to 2017. The macro-economic indicators we use include GDP per capita, unemployment, and national debt levels, covering the most salient economic dimensions. We furthermore control for the country’s migration situation and aggregate party positions toward cultural diversity. Our results show that the dynamic short-term developments of the economy and migration within countries are of greater relevance for perceived immigrant threat than the long-term situation. In contrast, the long-term political climate appears to be more important than short-term changes in the aggregate party positions. Further mediation analyses show that objective economic conditions influence anti-immigrant attitudes primarily through individual perceptions of the country’s economic performance and that unemployment rates are of primary importance.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

Long‐term mean changes in actual evapotranspiration over China under climate warming and the attribution analysis within the Budyko framework

2021 ◽  
Author(s):  
Tao Su ◽  
Taichen Feng ◽  
Bicheng Huang ◽  
Zixuan Han ◽  
Zhonghua Qian ◽  
...  
Keyword(s):  
Climate Warming ◽  
Actual Evapotranspiration ◽  
Attribution Analysis

scholarly journals Acute symptoms of mild to moderate COVID-19 are highly heterogeneous across individuals and over time

2021 ◽  
Author(s):  
Thomas L Rodebaugh ◽  
Madelyn R Frumkin ◽  
Angela M Reiersen ◽  
Eric J Lenze ◽  
Michael S Avidan ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Survey Methods ◽  
Clinical Care ◽  
Clinical Deterioration ◽  
Symptom Profiles ◽  
Fluctuating Course ◽  
The Common ◽  
Symptom Changes ◽  
Over Time

Abstract Background The symptoms of COVID-19 appear to be heterogenous, and the typical course of these symptoms is unknown. Our objectives were to characterize the common trajectories of COVID-19 symptoms and assess how symptom course predicts other symptom changes as well as clinical deterioration. Methods 162 participants with acute COVID-19 responded to surveys up to 31 times for up to 17 days. Several statistical methods were used to characterize the temporal dynamics of these symptoms. Because nine participants showed clinical deterioration, we explored whether these participants showed any differences in symptom profiles. Results Trajectories varied greatly between individuals, with many having persistently severe symptoms or developing new symptoms several days after being diagnosed. A typical trajectory was for a symptom to improve at a decremental rate, with most symptoms still persisting to some degree at the end of the reporting period. The pattern of symptoms over time suggested a fluctuating course for many patients. Participants who showed clinical deterioration were more likely to present with higher reports of severity of cough and diarrhea. Conclusion The course of symptoms during the initial weeks of COVID-19 is highly heterogeneous and is neither predictable nor easily characterized using typical survey methods. This has implications for clinical care and early-treatment clinical trials. Additional research is needed to determine whether the decelerating improvement pattern seen in our data is related to the phenomenon of patients reporting long-term symptoms, and whether higher symptoms of diarrhea in early illness presages deterioration.

scholarly journals MEG signatures of long-term effects of agreement and disagreement with the majority

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. Gorin ◽  
V. Klucharev ◽  
A. Ossadtchi ◽  
I. Zubarev ◽  
V. Moiseeva ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Social Influence ◽  
Temporal Dynamics ◽  
Peer Group ◽  
Long Term Effects ◽  
Learning Mechanisms ◽  
Source Imaging ◽  
The Face ◽  
First Session

AbstractPeople often change their beliefs by succumbing to an opinion of others. Such changes are often referred to as effects of social influence. While some previous studies have focused on the reinforcement learning mechanisms of social influence or on its internalization, others have reported evidence of changes in sensory processing evoked by social influence of peer groups. In this study, we used magnetoencephalographic (MEG) source imaging to further investigate the long-term effects of agreement and disagreement with the peer group. The study was composed of two sessions. During the first session, participants rated the trustworthiness of faces and subsequently learned group rating of each face. In the first session, a neural marker of an immediate mismatch between individual and group opinions was found in the posterior cingulate cortex, an area involved in conflict-monitoring and reinforcement learning. To identify the neural correlates of the long-lasting effect of the group opinion, we analysed MEG activity while participants rated faces during the second session. We found MEG traces of past disagreement or agreement with the peers at the parietal cortices 230 ms after the face onset. The neural activity of the superior parietal lobule, intraparietal sulcus, and precuneus was significantly stronger when the participant’s rating had previously differed from the ratings of the peers. The early MEG correlates of disagreement with the majority were followed by activity in the orbitofrontal cortex 320 ms after the face onset. Altogether, the results reveal the temporal dynamics of the neural mechanism of long-term effects of disagreement with the peer group: early signatures of modified face processing were followed by later markers of long-term social influence on the valuation process at the ventromedial prefrontal cortex.

Land conversion regulates the effects of long-term climate warming on soil micro-food web communities

2021 ◽  
Vol 314 ◽  
pp. 107426
Author(s):  
Pingting Guan ◽  
Mohammad Mahamood ◽  
Yurong Yang ◽  
Donghui Wu
Keyword(s):  
Food Web ◽  
Climate Warming ◽  
Land Conversion ◽  
Web Communities
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