Islscp Ii Modis (Collection 4) Igpb Land Cover, 2000-2001

Friedl,; Strahler, A.H.; Hodges, J.C.F.; Hall, Forrest G.; Collatz, G. J.; Meeson, B.W.; Los, S.O.; Colstoun, E. Brown De; Landis, D.R.

doi:10.3334/ornldaac/968

Cited by 56 publications

(45 citation statements)

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“…Although fossil fuel emissions typically increase monotonically from year to year for hemispheric means in contrast to the atmospheric XCO 2 growth, they contribute perceptibly to the interannual variability of the growth rates in the Northern Hemisphere. This is because fossil fuel emissions slightly decreased by 1.3 % in 2009 as an exception to the rule due to the global economic crisis (Friedlingstein et al, 2010). In the Southern Hemisphere biospheric fluxes are also the largest contributor to the covariation, although the net biospheric sink is considerably smaller than in the Northern Hemisphere and hardly exceeds the fire emissions.…”

Section: Atmospheric Growth Ratementioning

confidence: 95%

“…3 also shows the covariation of annual atmospheric growth rate and warm season surface temperature anomaly for the Northern Hemisphere. To focus on the active biosphere, the surface temperature anomalies throughout this manuscript are calculated excluding ocean, snow/ice, and barren or sparsely vegetated regions as provided by the MODIS IGBP Land Cover product (Friedl et al, 2010). Both the SCIAMACHY and CarbonTracker growth rates correlate significantly with the temperature anomaly at the 98 % significance level, exhibiting larger growth rates for warmer years.…”

Section: Atmospheric Growth Ratementioning

confidence: 99%

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Terrestrial carbon sink observed from space: variation of growth rates and seasonal cycle amplitudes in response to interannual surface temperature variability

et al. 2014

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Abstract. The terrestrial biosphere is currently acting as a net carbon sink on the global scale, exhibiting significant interannual variability in strength. To reliably predict the future strength of the land sink and its role in atmospheric CO 2 growth, the underlying biogeochemical processes and their response to a changing climate need to be well understood. In particular, better knowledge of the impact of key climate variables such as temperature or precipitation on the biospheric carbon reservoir is essential.It is demonstrated using nearly a decade of SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) nadir measurements that years with higher temperatures during the growing season can be robustly associated with larger growth rates in atmospheric CO 2 and smaller seasonal cycle amplitudes for northern midlatitudes. We find linear relationships between warming and CO 2 growth as well as seasonal cycle amplitude at the 98 % significance level. This suggests that the terrestrial carbon sink is less efficient at higher temperatures during the analysed time period. Unless the biosphere has the ability to adapt its carbon storage under warming conditions in the longer term, such a temperature response entails the risk of potential future sink saturation via a positive carbon-climate feedback.Quantitatively, the covariation between the annual CO 2 growth rates derived from SCIAMACHY data and warm season surface temperature anomaly amounts to 1.25 ± 0.32 ppm yr −1 K −1 for the Northern Hemisphere, where the bulk of the terrestrial carbon sink is located. In comparison, this relationship is less pronounced in the Southern Hemisphere. The covariation of the seasonal cycle amplitudes retrieved from satellite measurements and temperature anomaly is −1.30 ± 0.31 ppm K −1 for the north temperate zone. These estimates are consistent with those from the CarbonTracker data assimilated CO 2 data product, indicating that the temperature dependence of the model surface fluxes is realistic.

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Section: Atmospheric Growth Ratementioning

confidence: 95%

Section: Atmospheric Growth Ratementioning

confidence: 99%

Terrestrial carbon sink observed from space: variation of growth rates and seasonal cycle amplitudes in response to interannual surface temperature variability

et al. 2014

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“…The land cover and soil textures are also obtained from the NLDAS‐2 data set. The former is based on the IGBP‐Modis classification using 20 PFTs [ Friedl et al , ] of which four are no real vegetation but urban builtup, water surfaces, permanent snow and ice, and unvegetated bare soil. The soils are split into 19 texture classes based on the STATSGO soil database [ Miller and White , ].…”

Section: Model Methods and Setupmentioning

confidence: 99%

The impact of standard and hard‐coded parameters on the hydrologic fluxes in the Noah‐MP land surface model

et al. 2016

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Land surface models incorporate a large number of process descriptions, containing a multitude of parameters. These parameters are typically read from tabulated input files. Some of these parameters might be fixed numbers in the computer code though, which hinder model agility during calibration. Here we identified 139 hard‐coded parameters in the model code of the Noah land surface model with multiple process options (Noah‐MP). We performed a Sobol' global sensitivity analysis of Noah‐MP for a specific set of process options, which includes 42 out of the 71 standard parameters and 75 out of the 139 hard‐coded parameters. The sensitivities of the hydrologic output fluxes latent heat and total runoff as well as their component fluxes were evaluated at 12 catchments within the United States with very different hydrometeorological regimes. Noah‐MP's hydrologic output fluxes are sensitive to two thirds of its applicable standard parameters (i.e., Sobol' indexes above 1%). The most sensitive parameter is, however, a hard‐coded value in the formulation of soil surface resistance for direct evaporation, which proved to be oversensitive in other land surface models as well. Surface runoff is sensitive to almost all hard‐coded parameters of the snow processes and the meteorological inputs. These parameter sensitivities diminish in total runoff. Assessing these parameters in model calibration would require detailed snow observations or the calculation of hydrologic signatures of the runoff data. Latent heat and total runoff exhibit very similar sensitivities because of their tight coupling via the water balance. A calibration of Noah‐MP against either of these fluxes should therefore give comparable results. Moreover, these fluxes are sensitive to both plant and soil parameters. Calibrating, for example, only soil parameters hence limit the ability to derive realistic model parameters. It is thus recommended to include the most sensitive hard‐coded model parameters that were exposed in this study when calibrating Noah‐MP.

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“…Vegetation changes were characterized by comparing mean model output for the periods 1961-1990 and 2051-2080 as the recent and the future states. For comparison to the model output, a composite map of observed pan-Arctic vegetation was constructed based on three data sources (supplementary figures S2, S3 and table S3 available at stacks.iop.org/ERL/8/034023/mmedia): a potential natural vegetation (PNV) map (Kaplan et al 2003); the Circumpolar Arctic Vegetation Map (CAVM) (Walker et al 2005); and the International-Geosphere-Biosphere-Program (IGBP) land cover dataset over the period 2000-2001(Friedl et al 2010. We classified the 15 modeled PFTs based on the biomes types in the observed vegetation map (supplementary table S3).…”

Section: Model Protocol and Evaluationmentioning

confidence: 99%

Tundra shrubification and tree-line advance amplify arctic climate warming: results from an individual-based dynamic vegetation model

Zhang

Miller

Smith

et al. 2013

Environ. Res. Lett.

137

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One major challenge to the improvement of regional climate scenarios for the northern high latitudes is to understand land surface feedbacks associated with vegetation shifts and ecosystem biogeochemical cycling. We employed a customized, Arctic version of the individual-based dynamic vegetation model LPJ-GUESS to simulate the dynamics of upland and wetland ecosystems under a regional climate model-downscaled future climate projection for the Arctic and Subarctic. The simulated vegetation distribution agreed well with a composite map of actual arctic vegetation. In the future (2051-2080), a poleward advance of the forest-tundra boundary, an expansion of tall shrub tundra, and a dominance shift from deciduous to evergreen boreal conifer forest over northern Eurasia were simulated. Ecosystems continued to sink carbon for the next few decades, although the size of these sinks diminished by the late 21st century. Hot spots of increased CH 4 emission were identified in the peatlands near Hudson Bay and western Siberia. In terms of their net impact on regional climate forcing, positive feedbacks associated with the negative effects of tree-line, shrub cover and forest phenology changes on snow-season albedo, as well as the larger sources of CH 4 , may potentially dominate over negative feedbacks due to increased carbon sequestration and increased latent heat flux.

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Islscp Ii Modis (Collection 4) Igpb Land Cover, 2000-2001

Cited by 56 publications

References 0 publications

Terrestrial carbon sink observed from space: variation of growth rates and seasonal cycle amplitudes in response to interannual surface temperature variability

Terrestrial carbon sink observed from space: variation of growth rates and seasonal cycle amplitudes in response to interannual surface temperature variability

The impact of standard and hard‐coded parameters on the hydrologic fluxes in the Noah‐MP land surface model

Tundra shrubification and tree-line advance amplify arctic climate warming: results from an individual-based dynamic vegetation model

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