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  • Numerical analysis/modeling  (2)
  • Terrestrial ecosystem model  (2)
  • 1
    Publication Date: 2022-05-25
    Description: Author Posting. © American Meteorological Society, 2009. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 22 (2009): 5175–5204, doi:10.1175/2009JCLI2863.1.
    Description: The Massachusetts Institute of Technology (MIT) Integrated Global System Model is used to make probabilistic projections of climate change from 1861 to 2100. Since the model’s first projections were published in 2003, substantial improvements have been made to the model, and improved estimates of the probability distributions of uncertain input parameters have become available. The new projections are considerably warmer than the 2003 projections; for example, the median surface warming in 2091–2100 is 5.1°C compared to 2.4°C in the earlier study. Many changes contribute to the stronger warming; among the more important ones are taking into account the cooling in the second half of the twentieth century due to volcanic eruptions for input parameter estimation and a more sophisticated method for projecting gross domestic product (GDP) growth, which eliminated many low-emission scenarios. However, if recently published data, suggesting stronger twentieth-century ocean warming, are used to determine the input climate parameters, the median projected warming at the end of the twenty-first century is only 4.1°C. Nevertheless, all ensembles of the simulations discussed here produce a much smaller probability of warming less than 2.4°C than implied by the lower bound of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) projected likely range for the A1FI scenario, which has forcing very similar to the median projection in this study. The probability distribution for the surface warming produced by this analysis is more symmetric than the distribution assumed by the IPCC because of a different feedback between the climate and the carbon cycle, resulting from the inclusion in this model of the carbon–nitrogen interaction in the terrestrial ecosystem.
    Description: This work was supported in part by the Office of Science (BER), U.S. Department of Energy Grants DE-FG02-94ER61937 and DE-FG02-93ER61677, and by the industrial and foundations sponsors of The MIT Joint Program on the Science and Policy of Global Change (http://globalchange.mit.edu/sponsors/ current.html).
    Keywords: Probability forecasts/models ; Climate prediction ; Anthropogenic effects ; Numerical analysis/modeling ; Feedback
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
    Publication Date: 2022-05-25
    Description: Author Posting. © American Meteorological Society, 2010. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 23 (2010): 2230–2231, doi:10.1175/2009JCLI3566.1.
    Description: Corrigendum: Sokolov, A., and Coauthors, 2009: Probabilistic forecast for twenty-first-century climate based on uncertainties in emissions (without policy) and climate parameters. J. Climate, 22, 5175–5204.
    Keywords: Probability forecasts/models ; Climate prediction ; Anthropogenic effects ; Numerical analysis/modeling ; Feedback
    Repository Name: Woods Hole Open Access Server
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  • 3
    Publication Date: 2022-05-26
    Description: Author Posting. © American Geophysical Union, 2015. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Atmospheres 120 (2015): 2647–2660, doi:10.1002/2014JD022531.
    Description: The ecosystems in Northern Eurasia (NE) play an important role in the global water cycle and the climate system. While evapotranspiration (ET) is a critical variable to understand this role, ET over this region remains largely unstudied. Using an improved version of the Terrestrial Ecosystem Model with five widely used forcing data sets, we examine the impact that uncertainties in climate forcing data have on the magnitude, variability, and dominant climatic drivers of ET for the period 1979–2008. Estimates of regional average ET vary in the range of 241.4–335.7 mm yr−1 depending on the choice of forcing data. This range corresponds to as much as 32% of the mean ET. Meanwhile, the spatial patterns of long-term average ET across NE are generally consistent for all forcing data sets. Our ET estimates in NE are largely affected by uncertainties in precipitation (P), air temperature (T), incoming shortwave radiation (R), and vapor pressure deficit (VPD). During the growing season, the correlations between ET and each forcing variable indicate that T is the dominant factor in the north and P in the south. Unsurprisingly, the uncertainties in climate forcing data propagate as well to estimates of the volume of water available for runoff (here defined as P-ET). While the Climate Research Unit data set is overall the best choice of forcing data in NE according to our assessment, the quality of these forcing data sets remains a major challenge to accurately quantify the regional water balance in NE.
    Description: This research is supported by the NASA Land Use and Land Cover Change program (NASA- NNX09AI26G, NN-H-04-Z-YS-005-N, and NNX09AM55G); the Department of Energy (DE-FG02-08ER64599); the National Science Foundation (NSF-1028291, NSF-0919331, and AGS 0847472); and the NSF Carbon and Water in the Earth Program (NSF-0630319). D.G.M. acknowledges financial support from The Netherlands Organisation for Scientific Research (NWO) Veni grant 863.14.004
    Description: 2015-10-03
    Keywords: Evapotranspiration ; Northern Eurasia ; Terrestrial ecosystem model ; Climate reanalysis ; Forcing uncertainty
    Repository Name: Woods Hole Open Access Server
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  • 4
    Publication Date: 2022-05-26
    Description: Author Posting. © The Authors, 2005. This is the author's version of the work. It is posted here by permission of Blackwell for personal use, not for redistribution. The definitive version was published in Global Change Biology 12 (2006): 731-750, doi:10.1111/j.1365-2486.2006.01113.x.
    Description: In terrestrial high-latitude regions, observations indicate recent changes in snow cover, permafrost, and soil freeze-thaw transitions due to climate change. These modifications may result in temporal shifts in the growing season and the associated rates of terrestrial productivity. Changes in productivity will influence the ability of these ecosystems to sequester atmospheric CO2. We use the Terrestrial Ecosystem Model (TEM), which simulates the soil thermal regime, in addition to terrestrial carbon, nitrogen and water dynamics, to explore these issues over the years 1960-2100 in extratropical regions (30°-90°N). Our model simulations show decreases in snow cover and permafrost stability from 1960 to 2100. Decreases in snow cover agree well with NOAA satellite observations collected between the years 1972-2000, with Pearson rank correlation coefficients between 0.58-0.65. Model analyses also indicate a trend towards an earlier thaw date of frozen soils and the onset of the growing season in the spring by approximately 2-4 days from 1988-2000. Between 1988 and 2000, satellite records yield a slightly stronger trend in thaw and the onset of the growing season, averaging between 5-8 days earlier. In both the TEM simulations and satellite records, trends in day of freeze in the autumn are weaker, such that overall increases in growing season length are due primarily to earlier thaw. Although regions with the longest snow cover duration displayed the greatest increase in growing season length, these regions maintained smaller increases in productivity and heterotrophic respiration than those regions with shorter duration of snow cover and less of an increase in growing season length. Concurrent with increases in growing season length, we found a reduction in soil carbon and increases in vegetation carbon, with greatest losses of soil carbon occurring in those areas with more vegetation, but simulations also suggest that this trend could reverse in the future. Our results reveal noteworthy changes in snow, permafrost, growing season length, productivity, and net carbon uptake, indicating that prediction of terrestrial carbon dynamics from one decade to the next will require that large-scale models adequately take into account the corresponding changes in soil thermal regimes.
    Description: Funds were provided by the NSF for the Arctic Biota/Vegetation portion of the ‘Climate of the Arctic: Modeling and Processes’ project (OPP- 0327664), and by the USGS ‘Fate of Carbon in Alaska Landscapes’ project.
    Keywords: Growing season ; Carbon sequestration ; Productivity ; Respiration ; Snow cover ; Permafrost ; Climate change ; Terrestrial ecosystem model
    Repository Name: Woods Hole Open Access Server
    Type: Preprint
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