ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Publication Date: 2024-02-21
    Description: Emergent constraints on carbon cycle feedbacks in response to warming and increasing atmospheric CO〈sub〉2 〈/sub〉 concentration have previously been identified in Earth system models participating in the Coupled Model Intercomparison Project (CMIP) Phase 5. Here, we examine whether two of these emergent constraints also hold for CMIP6. The spread of the sensitivity of tropical land carbon uptake to tropical warming in an idealized simulation with a 1% per year increase of atmospheric CO〈sub〉2 〈/sub〉 shows only a slight decrease in CMIP6 (−52 ± 35 GtC/K) compared to CMIP5 (−49 ± 40 GtC/K). For both model generations, the observed interannual variability in the growth rate of atmospheric CO〈sub〉2 〈/sub〉 yields a consistent emergent constraint on the sensitivity of tropical land carbon uptake with a constrained range of −37 ± 14 GtC/K for the combined ensemble (i.e., a reduction of ∼30% in the best estimate and 60% in the uncertainty range relative to the multimodel mean of the combined ensemble). A further emergent constraint is based on a relationship between CO〈sub〉2 〈/sub〉 fertilization and the historical increase in the CO〈sub〉2 〈/sub〉 seasonal cycle amplitude in high latitudes. However, this emergent constraint is not evident in CMIP6. This is in part because the historical increase in the amplitude of the CO〈sub〉2 〈/sub〉 seasonal cycle is more accurately simulated in CMIP6, such that the models are all now close to the observational constraint.
    Description: Plain Language Summary: The statistical model of so‐called emergent constraints help to better understand the sensitivity of Earth system processes in a changing climate. Here, we analyze the robustness of two previously found emergent constraints on carbon cycle feedbacks, using models from the Coupled Model Intercomparison Project (CMIP) of Phases 5 and 6. First the decrease of carbon storage in the tropics due to increasing near‐surface air temperatures, which is found to be robust on the choise of model ensemble. Giving a constraint estimate of −52 ± 35 GtC/K for CMIP6 models, being within the range of uncertainty for the previously estimated result for CMIP5. Second, the increase of carbon storage in high latitudes due to CO〈sub〉2 〈/sub〉 fertilization effect, which is found to be not evident among CMIP6 models. This is in part because the historical increase in the amplitude of the CO〈sub〉2 〈/sub〉 seasonal cycle is more accurately simulated in CMIP6, such that the models are all now close to the observational constraint.
    Description: Key Points: An emergent constraint on the sensitivity of tropical land carbon to global warming, originally derived from Coupled Model Intercomparison Project Phase 5 (CMIP5), also holds for CMIP6. The combined CMIP5 + CMIP6 ensemble gives an emergent constraint on the sensitivity of tropical land carbon to global warming of −37 ± 14 GtC/K. An emergent constraint on the fertilization feedback due to rising CO〈sub〉2 〈/sub〉 levels, previously derived, is not evident in CMIP6.
    Description: Horizon 2020 Framework Programme http://dx.doi.org/10.13039/100010661
    Description: ERC
    Description: https://doi.org/10.5281/zenodo.6900341
    Description: https://doi.org/10.5281/zenodo.3387139
    Description: https://github.com/ESMValGroup
    Description: https://docs.esmvaltool.org/
    Keywords: ddc:551 ; carbon cycle ; emergent constraint ; CMIP5 ; CMIP6 ; fertilization effect ; temperature warming
    Language: English
    Type: doc-type:article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 2
    Publication Date: 2019-07-13
    Description: Scenarios that limit global warming to below 2 degrees Centigrade by 2100 assume significant land-use change to support large-scale carbon dioxide (CO2) removal from the atmosphere by afforestation/reforestation, avoided deforestation, and Biomass Energy with Carbon Capture and Storage (BECCS). The more ambitious mitigation scenarios require even greater land area for mitigation and/or earlier adoption of CO2 removal strategies. Here we show that additional land-use change to meet a 1.5 degrees Centigrade climate change target could result in net losses of carbon from the land. The effectiveness of BECCS strongly depends on several assumptions related to the choice of biomass, the fate of initial above ground biomass, and the fossil-fuel emissions offset in the energy system. Depending on these factors, carbon removed from the atmosphere through BECCS could easily be offset by losses due to land-use change. If BECCS involves replacing high-carbon content ecosystems with crops, then forest-based mitigation could be more efficient for atmospheric CO2 removal than BECCS.
    Keywords: Earth Resources and Remote Sensing; Meteorology and Climatology
    Type: GSFC-E-DAA-TN63037 , Nature Communications (e-ISSN 2041-1723); 9; 1; 2938
    Format: text
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 3
    Publication Date: 2019-07-13
    Description: Earth system models are complex and represent a large number of processes, resulting in a persistent spread across climate projections for a given future scenario. Owing to different model performances against observations and the lack of independence among models, there is now evidence that giving equal weight to each available model projection is suboptimal. This Perspective discusses newly developed tools that facilitate a more rapid and comprehensive evaluation of model simulations with observations, process-based emergent constraints that are a promising way to focus evaluation on the observations most relevant to climate projections, and advanced methods for model weighting. These approaches are needed to distil the most credible information on regional climate changes, impacts, and risks for stakeholders and policy-makers.
    Keywords: Meteorology and Climatology
    Type: GSFC-E-DAA-TN65080 , Nature Climate Change (ISSN 1758-678X) (e-ISSN 1758-6798); 9; 102-110
    Format: text
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 4
    Publication Date: 2019-07-13
    Description: Recent studies have shown that current dynamic vegetation models have serious weaknesses in reproducing the observed vegetation dynamics and contribute to bias in climate simulations. This study intends to identify the major factors that underlie the connections between vegetation dynamics and climate variability and investigates vegetation spatial distribution and temporal variability at seasonal to decadal scales over North America (NA) to assess a 2-D biophysical model/dynamic vegetation model's (Simplified Simple Biosphere Model version 4, coupled with the Top-down Representation of Interactive Foliage and Flora Including Dynamics Model (SSiB4/TRIFFID)) ability to simulate these characteristics for the past 60 years (1948 through 2008). Satellite data are employed as constraints for the study and to compare the relationships between vegetation and climate from the observational and the simulation data sets. Trends in NA vegetation over this period are examined. The optimum temperature for photosynthesis, leaf drop threshold temperatures, and competition coefficients in the Lotka-Volterra equation, which describes the population dynamics of species competing for some common resource, have been identified as having major impacts on vegetation spatial distribution and obtaining proper initial vegetation conditions in SSiB4/TRIFFID. The finding that vegetation competition coefficients significantly affect vegetation distribution suggests the importance of including biotic effects in dynamical vegetation modeling. The improved SSiB4/TRIFFID can reproduce the main features of the NA distributions of dominant vegetation types, the vegetation fraction, and leaf area index (LAI), including its seasonal, interannual, and decadal variabilities. The simulated NA LAI also shows a general increasing trend after the 1970s in responding to warming. Both simulation and satellite observations reveal that LAI increased substantially in the southeastern U.S. starting from the 1980s. The effects of the severe drought during 1987-1992 and the last decade in the southwestern U.S. on vegetation are also evident from decreases in the simulated and satellite-derived LAIs. Both simulated and satellite-derived LAIs have the strongest correlations with air temperature at northern middle to high latitudes in spring reflecting the effect of these climatic variables on photosynthesis and phenological processes. Meanwhile, in southwestern dry lands, negative correlations appear due to the heat and moisture stress there during the summer. Furthermore, there are also positive correlations between soil wetness and LAI, which increases from spring to summer. The present study shows both the current improvements and remaining weaknesses in dynamical vegetation models. It also highlights large continental-scale variations that have occurred in NA vegetation over the past six decades and their potential relations to climate. With more observational data availability, more studies with differentmodels and focusing on different regions will be possible and are necessary to achieve comprehensive understanding of the vegetation dynamics and climate interactions.
    Keywords: Geosciences (General); Meteorology and Climatology
    Type: GSFC-E-DAA-TN23303 , Journal of Geophysical Research: Atmospheres; 120; 4; 1300-1321
    Format: application/pdf
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 5
    facet.materialart.
    Unknown
    IPCC
    In:  In: Climate Change 2021: The Physical Science Basis: Contribution of Working Group I to the Sixth : Assessment Report of the Intergovernmental Panel on Climate Change : Chapter 5. , ed. by Masson-Delmotte, V., Zhai, P., Pirani, A., Conners, S. L., Pean, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekci, O., Yu, R. and Zhou, B. IPCC, Genf, Switzerland, pp. 1-221.
    Publication Date: 2022-01-06
    Type: Book chapter , NonPeerReviewed
    Format: text
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...