GFDL’s ESM2 Global Coupled Climate–Carbon Earth System Models. Part II: Carbon System Formulation and Baseline Simulation Characteristics*

Dunne, John; John, Jasmin G.; Shevliakova, Elena; Stouffer, Ronald J.; Krasting, John P.; Malyshev, Sergey; Milly, P. C. D.; Sentman, Lori T.; Adcroft, Alistair; Cooke, William; Dunne, Krista A.; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Levy, H.; Wittenberg, Andrew T.; Phillips, Peter J.; Zadeh, Niki

doi:10.1175/jcli-d-12-00150.1

Cited by 578 publications

(434 citation statements)

References 54 publications

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“…The estimates were grouped into four submodels: GFDL-ESM2M [57], IPSL-CM5A-LR [58], MIROC-ESM-CHEM [59], and NorESM1-M [60], at two extremes of the concentrations of GHGs, representative concentration pathway (RCP) 2.6 and RCP 8.5, which cover the daily average temperature and precipitation data at a resolution of 0.5 • for the period 1950-2099 (the present research considered only the 2015-2099 subset). Given the greater uncertainty in estimating values from a single set, the average of the four climate models was used.…”

Section: Meteorological Drought Indexmentioning

confidence: 99%

Stock Volume Dependency of Forest Drought Responses in Yunnan, China

Luo

Zhou

et al. 2018

Forests

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Abstract:Revealing forest drought response characteristics and the potential impact factors is quite an important scientific issue against the background of global climate change, which is the foundation to reliably evaluate and predict the effects of future drought. Due to the high spatial heterogeneity of forest properties such as biomass, forest age, and height, and the distinct differences in drought stress in terms of frequency, intensity, and duration, current studies still contain many uncertainties. In this research, we used the forests in Yunnan Province in Southwest China as an example and aimed to reveal the potential impacts of forest properties (i.e., stock volume) on drought response characteristics. Specifically, we divided the forest into five groups of stock volume density values and then analyzed their drought response differences. To depict forest response to drought intensity, the standardized precipitation evapotranspiration index (SPEI) was chosen as the explanatory variable, and the change in remote sensing-based enhanced vegetation index (deficit of MODIS-EVI, dEVI) was chosen as the response variable of drought stress. Given that the SPEI has different time scales, we first analyzed the statistical dependency of SPEIs with different time scales (1 to 36 months) to the response variable (i.e., dEVI). The optimal time scale of SPEI (SPEI opt ) to interpret the maximum variation of dEVI (R-square) was then chosen to build the ultimate statistical models for the five groups of stock volume density. The main findings were as follows: (1) the impacts of drought showed hysteresis and cumulative effects, and the length of the hysteresis increased with stock volume densities; (2) forests with high stock volume densities required more soil water and were therefore more sensitive to the changes in water deficit; (3) compared with the optimal time scale of SPEI (SPEI opt ), the SPEI with the commonly used time scale (e.g., 1, 6, and 12 months) could not well reflect the impacts of drought on forests and the simulation error of dEVI increased with stock volume densities; and (4) forests with higher stock volume densities were likely to experience a greater risk of degradation following higher atmospheric concentrations of greenhouse gases (Representative Concentration Pathway (RCP) 8.5). As a result, both the time scale of the meteorological drought index and the spatial difference in forest stock volumes should be considered when evaluating forest drought responses at regional and global scales.

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Section: Meteorological Drought Indexmentioning

confidence: 99%

Stock Volume Dependency of Forest Drought Responses in Yunnan, China

Luo

Zhou

et al. 2018

Forests

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“…The radiative forcing of the RCPs is likely to correspond to global mean temperature increases between 0.3 and 4.8 • C by the end of the 21st century (2081-2100) relative to the 1986-2005 global mean temperature (in the 5 to 95 % range; Collins et al, 2013). The impact of climate change on crop yields was estimated for all RCPs by running LPJ-GUESS with climate inputs derived from five different general circulation models (GCMs; Collins et al, 2011;Dufresne et al, 2013;Dunne et al, 2013;Iversen et al, 2013;Watanabe et al, 2011).…”

Section: Conditional Probabilistic Futures Within the Ssp-rcp Scenarimentioning

confidence: 99%

Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework

et al. 2016

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Abstract. We present a modelling framework to simulate probabilistic futures of global cropland areas that are conditional on the SSP (shared socio-economic pathway) scenarios. Simulations are based on the Parsimonious Land Use Model (PLUM) linked with the global dynamic vegetation model LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator) using socio-economic data from the SSPs and climate data from the RCPs (representative concentration pathways). The simulated range of global cropland is 893-2380 Mha in 2100 (± 1 standard deviation), with the main uncertainties arising from differences in the socio-economic conditions prescribed by the SSP scenarios and the assumptions that underpin the translation of qualitative SSP storylines into quantitative model input parameters. Uncertainties in the assumptions for population growth, technological change and cropland degradation were found to be the most important for global cropland, while uncertainty in food consumption had less influence on the results. The uncertainties arising from climate variability and the differences between climate change scenarios do not strongly affect the range of global cropland futures. Some overlap occurred across all of the conditional probabilistic futures, except for those based on SSP3. We conclude that completely different socio-economic and climate change futures, although sharing low to medium population development, can result in very similar cropland areas on the aggregated global scale.

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“…N:P is based on optimal allocation after Klausmeier et al [2004]. Complete technical details are available in the supplementary information of Dunne et al [2013]. Results contributed to this study were conducted with the MOM (version 4p1) [Griffies, 2012] with sea ice dynamics as described in Winton [2000].…”

Section: A6 Modelmentioning

confidence: 99%

Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models

Lee

Matrai

Friedrichs

et al. 2016

J. Geophys. Res. Oceans

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The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO3), mixed layer depth (MLD), euphotic layer depth (Zeu), and sea ice concentration, by comparing results against a newly updated, quality‐controlled in situ NPP database for the Arctic Ocean (1959–2011). The models broadly captured the spatial features of integrated NPP (iNPP) on a pan‐Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO3, MLD, and Zeu throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice‐free versus ice‐influenced) and bottom depth (shelf versus deep ocean). The models performed relatively well for the most recent decade and toward the end of Arctic summer. In the Barents and Greenland Seas, regional model skill of surface NO3 was best associated with how well MLD was reproduced. Regionally, iNPP was relatively well simulated in the Beaufort Sea and the central Arctic Basin, where in situ NPP is low and nutrients are mostly depleted. Models performed less well at simulating iNPP in the Greenland and Chukchi Seas, despite the higher model skill in MLD and sea ice concentration, respectively. iNPP model skill was constrained by different factors in different Arctic Ocean regions. Our study suggests that better parameterization of biological and ecological microbial rates (phytoplankton growth and zooplankton grazing) are needed for improved Arctic Ocean biogeochemical modeling.

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GFDL’s ESM2 Global Coupled Climate–Carbon Earth System Models. Part II: Carbon System Formulation and Baseline Simulation Characteristics*

Cited by 578 publications

References 54 publications

Stock Volume Dependency of Forest Drought Responses in Yunnan, China

Stock Volume Dependency of Forest Drought Responses in Yunnan, China

Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework

Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models

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