Natural and anthropogenic climate change: incorporating historical land cover change, vegetation dynamics and the global carbon cycle

2004 ◽  
Vol 22 (5) ◽  
pp. 461-479 ◽  
Author(s):  
H. D. Matthews ◽  
A. J. Weaver ◽  
K. J. Meissner ◽  
N. P. Gillett ◽  
M. Eby
Keyword(s):  
Climate Change ◽  
Land Cover ◽  
Carbon Cycle ◽  
Land Cover Change ◽  
Vegetation Dynamics ◽  
Global Carbon Cycle ◽  
Anthropogenic Climate Change ◽  
Global Carbon

Time-scale dependence of climate-carbon cycle feedbacks for weak perturbations in CMIP5 models

2021 ◽  
Author(s):  
Guilherme Torres Mendonça ◽  
Julia Pongratz ◽  
Christian Reick
Keyword(s):  
Climate Change ◽  
Carbon Cycle ◽  
Time Scales ◽  
Radiative Forcing ◽  
Global Carbon Cycle ◽  
Atmospheric Co2 ◽  
Ice Age ◽  
Cmip5 Models ◽  
Global Carbon ◽  
Different Time Scales

<p>The increase in atmospheric CO2 driven by anthropogenic emissions is the main radiative forcing causing climate change. But this increase is not only a result from emissions, but also from changes in the global carbon cycle. These changes arise from feedbacks between climate and the carbon cycle that drive CO2 into or out of the atmosphere in addition to the emissions, thereby either accelerating or buffering climate change. Therefore, understanding the contribution of these feedbacks to the global response of the carbon cycle is crucial in advancing climate research. Currently, this contribution is quantified by the α-β-γ framework (Friedlingstein et al., 2003). But this quantification is only valid for a particular perturbation scenario and time period. In contrast, a recently proposed generalization (Rubino et al., 2016) of this framework for weak perturbations quantifies this contribution for all scenarios and at different time scales. </p><p>Thereby, this generalization provides a systematic framework to investigate the response of the global carbon cycle in terms of the climate-carbon cycle feedbacks. In the present work we employ this framework to study these feedbacks and the airborne fraction in different CMIP5 models. We demonstrate (1) that this generalization of the α-β-γ framework consistently describes the linear dynamics of the carbon cycle in the MPI-ESM; and (2) how by this framework the climate-carbon cycle feedbacks and airborne fraction are quantified at different time scales in CMIP5 models. Our analysis shows that, independently of the perturbation scenario, (1) the net climate-carbon cycle feedback is negative at all time scales; (2) the airborne fraction generally decreases for increasing time scales; and (3) the land biogeochemical feedback dominates the model spread in the airborne fraction at all time scales. This last result therefore emphasizes the need to improve our understanding of this particular feedback.</p><p><strong>References:</strong></p><p>P. Friedlingstein, J.-L. Dufresne, P. Cox, and P. Rayner. How positive is the feedback between climate change and the carbon cycle? Tellus B, 55(2):692–700, 2003.</p><p>M. Rubino, D. Etheridge, C. Trudinger, C. Allison, P. Rayner, I. Enting, R. Mulvaney, L. Steele, R. Langenfelds, W. Sturges, et al. Low atmospheric CO2 levels during the Little Ice Age due to cooling-induced terrestrial uptake. Nature Geoscience, 9(9):691–694, 2016.</p>

scholarly journals Effects of Climate Change on Land Cover Change and Vegetation Dynamics in Xinjiang, China

2020 ◽  
Vol 17 (13) ◽  
pp. 4865 ◽  
Author(s):  
Haochen Yu ◽  
Zhengfu Bian ◽  
Shouguo Mu ◽  
Junfang Yuan ◽  
Fu Chen
Keyword(s):  
Climate Change ◽  
Land Cover ◽  
Land Cover Change ◽  
Extreme Precipitation ◽  
Vegetation Dynamics ◽  
Pearson Correlation ◽  
Extreme Temperature ◽  
Climate Extremes ◽  
Linear Regression Method ◽  
Vegetation Growth

Since the Silk-road Economic belt initiatives were proposed, Xinjiang has provided a vital strategic link between China and Central Asia and even Eurasia. However, owing to the weak and vulnerable ecosystem in this arid region, even a slight climate change would probably disrupt vegetation dynamics and land cover change. Thus, there is an urgent need to determine the Normalized Difference Vegetation Index (NDVI) and Land-use/Land-cover (LULC) responses to climate change. Here, the extreme-point symmetric mode decomposition (ESMD) method and linear regression method (LRM) were applied to recognize the variation trends of the NDVI, temperature, and precipitation between the growing season and other seasons. Combining the transfer matrix of LULC, the Pearson correlation analysis was utilized to reveal the response of NDVI to climate change and climate extremes. The results showed that: (1) Extreme temperature showed greater variation than extreme precipitation. Both the ESMD and the LRM exhibited an increased volatility trend for the NDVI, with the significant improvement regions mainly located in the margin of basins. (2) Since climate change had a warming trend, the permanent snow has been reduced by 20,436 km2. The NDVI has a higher correlation to precipitation than temperature. Furthermore, the humid trend could provide more suitable conditions for vegetation growth, but the warm trend might prevent vegetation growth. Spatially, the response of the NDVI in North Xinjiang (NXC) was more sensitive to precipitation than that in South Xinjiang (SXC). Seasonally, the NDVI has a greater correlation to precipitation in spring and summer, but the opposite occurs in autumn. (3) The response of the NDVI to extreme precipitation was stronger than the response to extreme temperature. The reduction in diurnal temperature variation was beneficial to vegetation growth. Therefore, continuous concentrated precipitation and higher night-time-temperatures could enhance vegetation growth in Xinjiang. This study could enrich the understanding of the response of land cover change and vegetation dynamics to climate extremes and provide scientific support for eco-environment sustainable management in the arid regions.

scholarly journals Vulnerability of Permafrost Carbon to Climate Change: Implications for the Global Carbon Cycle

BioScience ◽  
2008 ◽  
Vol 58 (8) ◽  
pp. 701-714 ◽  
Author(s):  
Edward A. G. Schuur ◽  
James Bockheim ◽  
Josep G. Canadell ◽  
Eugenie Euskirchen ◽  
Christopher B. Field ◽  
...  
Keyword(s):  
Climate Change ◽  
Carbon Cycle ◽  
Global Carbon Cycle ◽  
Global Carbon

Synergistic impacts of climate and land cover change, and water management on terrestrial hydrology over headwaters with an integrated attribution framework

2020 ◽  
Author(s):  
Peng Ji ◽  
Xing Yuan
Keyword(s):  
Climate Change ◽  
Water Management ◽  
Land Cover ◽  
Land Cover Change ◽  
River Basin ◽  
Yellow River ◽  
Yellow River Basin ◽  
Anthropogenic Climate Change ◽  
Surface Model ◽  
Streamflow Extremes

<p>Located over eastern Tibetan Plateau, the upper Yellow River basin (UYRB) provides about half of the total annual discharge of the entire Yellow River basin in northern China, and influences more than one hundred million people over downstream regions. In the Anthropocene, human activities such as greenhouse gases emission, human-induced land cover change and water management are changing the terrestrial hydrological process and streamflow extremes over UYRB. However, quantifying their separate influence is a great challenge due to limited observations and difficulty in modeling hydro-thermal processes over alpine regions.</p><p>Here we find significant fingerprints of anthropogenic climate change and land cover change in decreasing total water storage and increasing extremely low streamflow over UYRB headwater. While anthropogenic climate change, reservoir operation and land cover change significantly decreasing the probability of extreme flooding event over the UYRB by 31%, 45% and 10% respectively. The newly-developed Conjunctive Surface-Subsurface Process version 2 (CSSPv2) land surface model was first implemented at a high resolution (3km) over the UYRB. Comprehensive evaluations show the model well captures the variation and variability of hydrological variables. Simulations with and without land cover change were then compared to assess the impact of land cover change, while reservoir influence was calculated by comparing the modeled naturalized streamflow with observed streamflow. CSSPv2 was also driven by CMIP5 outputs with natural or anthropogenic forcings to assess influence of anthropogenic climate change. An integrated hydro-climate attribution framework was finally used to unify the contributions of different factors. Our results highlight the local-scale human influences (including land cover change and water management) on the streamflow extremes, which are still not well incorporated in current global climate models for detection and attribution studies.</p>

scholarly journals First pan-Arctic assessment of dissolved organic carbon in lakes of the permafrost region

2021 ◽  
Vol 18 (12) ◽  
pp. 3917-3936
Author(s):  
Lydia Stolpmann ◽  
Caroline Coch ◽  
Anne Morgenstern ◽  
Julia Boike ◽  
Michael Fritz ◽  
...  
Keyword(s):  
Climate Change ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Cycle ◽  
Dissolved Organic Carbon ◽  
Significant Relationship ◽  
Global Carbon Cycle ◽  
Doc Concentration ◽  
Global Carbon ◽  
Permafrost Regions

Abstract. Lakes in permafrost regions are dynamic landscape components and play an important role for climate change feedbacks. Lake processes such as mineralization and flocculation of dissolved organic carbon (DOC), one of the main carbon fractions in lakes, contribute to the greenhouse effect and are part of the global carbon cycle. These processes are in the focus of climate research, but studies so far are limited to specific study regions. In our synthesis, we analyzed 2167 water samples from 1833 lakes across the Arctic in permafrost regions of Alaska, Canada, Greenland, and Siberia to provide first pan-Arctic insights for linkages between DOC concentrations and the environment. Using published data and unpublished datasets from the author team, we report regional DOC differences linked to latitude, permafrost zones, ecoregions, geology, near-surface soil organic carbon contents, and ground ice classification of each lake region. The lake DOC concentrations in our dataset range from 0 to 1130 mg L−1 (10.8 mg L−1 median DOC concentration). Regarding the permafrost regions of our synthesis, we found median lake DOC concentrations of 12.4 mg L−1 (Siberia), 12.3 mg L−1 (Alaska), 10.3 mg L−1 (Greenland), and 4.5 mg L−1 (Canada). Our synthesis shows a significant relationship between lake DOC concentration and lake ecoregion. We found higher lake DOC concentrations at boreal permafrost sites compared to tundra sites. We found significantly higher DOC concentrations in lakes in regions with ice-rich syngenetic permafrost deposits (yedoma) compared to non-yedoma lakes and a weak but significant relationship between soil organic carbon content and lake DOC concentration as well as between ground ice content and lake DOC. Our pan-Arctic dataset shows that the DOC concentration of a lake depends on its environmental properties, especially on permafrost extent and ecoregion, as well as vegetation, which is the most important driver of lake DOC in this study. This new dataset will be fundamental to quantify a pan-Arctic lake DOC pool for estimations of the impact of lake DOC on the global carbon cycle and climate change.

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