Robustness and uncertainty in terrestrial ecosystem carbon response to CMIP5 climate change projections

Ahlström, Anders; Schurgers, Guy; Arneth, Almut; Smith, Benjamin

doi:10.1088/1748-9326/7/4/044008

Cited by 253 publications

(307 citation statements)

References 38 publications

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“…Thus, increasing complexity can mask a lack of understanding, resulting in a situation whereby models are tuned to perform well at standard tests but produce widely divergent results when projected beyond the domain of calibration. This seems to be precisely the situation currently observed with coupled carbon-cycle-climate models, as reported in AR5 (Ahlström et al, 2012;Anav et al, 2013;Arora et al, 2013;Jones et al, 2013;Todd-Brown et al, 2013;Ciais et al, 2014). Although it seems reasonable to expect that a model including a larger subset of processes that are known to be important should be more realistic than a simpler model, increases in reliability and robustness by no means automatically follow.…”

Section: Prentice Et Al: Next-generation Land-surface Modellinsupporting

confidence: 87%

Reliable, robust and realistic: the three R's of next-generation land-surface modelling

et al. 2015

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Abstract. Land-surface models (LSMs) are increasingly called upon to represent not only the exchanges of energy, water and momentum across the land-atmosphere interface (their original purpose in climate models), but also how ecosystems and water resources respond to climate, atmospheric environment, land-use and land-use change, and how these responses in turn influence land-atmosphere fluxes of carbon dioxide (CO 2 ), trace gases and other species that affect the composition and chemistry of the atmosphere. However, the LSMs embedded in state-of-the-art climate models differ in how they represent fundamental aspects of the hydrological and carbon cycles, resulting in large inter-model differences and sometimes faulty predictions. These "thirdgeneration" LSMs respect the close coupling of the carbon and water cycles through plants, but otherwise tend to be under-constrained, and have not taken full advantage of robust hydrological parameterizations that were independently developed in offline models. Benchmarking, combining multiple sources of atmospheric, biospheric and hydrological data, should be a required component of LSM development, but this field has been relatively poorly supported and intermittently pursued. Moreover, benchmarking alone is not sufficient to ensure that models improve. Increasing complexity may increase realism but decrease reliability and robustness, by increasing the number of poorly known model parameters. In contrast, simplifying the representation of complex processes by stochastic parameterization (the representation of unresolved processes by statistical distributions of values) has been shown to improve model reliability and realism in both atmospheric and land-surface modelling contexts. We provide examples for important processes in hydrology (the generation of runoff and flow routing in heterogeneous catchments) and biology (carbon uptake by speciesdiverse ecosystems). We propose that the way forward for next-generation complex LSMs will include: (a) representations of biological and hydrological processes based on the implementation of multiple internal constraints; (b) systematic application of benchmarking and data assimilation techniques to optimize parameter values and thereby test the structural adequacy of models; and (c) stochastic parameterization of unresolved variability, applied in both the hydrological and the biological domains.

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Section: Prentice Et Al: Next-generation Land-surface Modellinsupporting

confidence: 87%

Reliable, robust and realistic: the three R's of next-generation land-surface modelling

et al. 2015

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“…Model-based studies of transient changes in the global terrestrial ecosystem carbon balance over the 20th century indicate a switch from a neutral or source to a sink state around 1960 as a result of saturation of land use expansion, accompanied by increased greenhouse gas emissions, raising the production enhancement of vegetation due to atmospheric CO 2 (McGuire et al, 2001;Le Quéré et al, 2009;Ahlström et al, 2012b). Similar global transient responses were obtained by Bondeau et al (2007).…”

Section: Modelling Carbon Fluxes Of Natural Vegetation Pasture and Cmentioning

confidence: 64%

Implications of accounting for land use in simulations of ecosystem carbon cycling in Africa

et al. 2013

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Abstract. Dynamic global vegetation models (DGVMs) are important tools for modelling impacts of global change on ecosystem services. However, most models do not take full account of human land management and land use and land cover changes (LULCCs). We integrated croplands and pasture and their management and natural vegetation recovery and succession following cropland abandonment into the LPJ-GUESS DGVM. The revised model was applied to Africa as a case study to investigate the implications of accounting for land use on net ecosystem carbon balance (NECB) and the skill of the model in describing agricultural production and reproducing trends and patterns in vegetation structure and function. The seasonality of modelled monthly fraction of absorbed photosynthetically active radiation (FPAR) was shown to agree well with satellite-inferred normalised difference vegetation index (NDVI). In regions with a large proportion of cropland, the managed land addition improved the FPAR vs. NDVI fit significantly. Modelled 1991-1995 average yields for the seven most important African crops, representing potential optimal yields limited only by climate forcings, were generally higher than reported FAO yields by a factor of 2-6, similar to previous yield gap estimates. Modelled inter-annual yield variations during 1971-2005 generally agreed well with FAO statistics, especially in regions with pronounced climate seasonality. Modelled land-atmosphere carbon fluxes for Africa associated with land use change (0.07 PgC yr −1 release to the atmosphere for the 1980s) agreed well with previous estimates. Cropland management options (residue removal, grass as cover crop) were shown to be important to the landatmosphere carbon flux for the 20th century.

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“…A more detailed description of LPJ-GUESS is available in Smith et al (2001). We used LPJ-GUESS version 2.1, which includes the PFT set and modifications described in Ahlström et al (2012). LPJ-GUESS has already been successfully applied and validated to match present-day and mid-Holocene biome distributions of eastern Africa as suggested by data for both periods (Fer et al, 2016).…”

Section: The Lpj-guess Modelmentioning

confidence: 99%

The influence of El Niño–Southern Oscillation regimes on eastern African vegetation and its future implications under the RCP8.5 warming scenario

et al. 2017

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Abstract. The El Niño-Southern Oscillation (ENSO) is the main driver of the interannual variability in eastern African rainfall, with a significant impact on vegetation and agriculture and dire consequences for food and social security. In this study, we identify and quantify the ENSO contribution to the eastern African rainfall variability to forecast future eastern African vegetation response to rainfall variability related to a predicted intensified ENSO. To differentiate the vegetation variability due to ENSO, we removed the ENSO signal from the climate data using empirical orthogonal teleconnection (EOT) analysis. Then, we simulated the ecosystem carbon and water fluxes under the historical climate without components related to ENSO teleconnections. We found ENSO-driven patterns in vegetation response and confirmed that EOT analysis can successfully produce coupled tropical Pacific sea surface temperature-eastern African rainfall teleconnection from observed datasets. We further simulated eastern African vegetation response under future climate change as it is projected by climate models and under future climate change combined with a predicted increased ENSO intensity. Our EOT analysis highlights that climate simulations are still not good at capturing rainfall variability due to ENSO, and as we show here the future vegetation would be different from what is simulated under these climate model outputs lacking accurate ENSO contribution. We simulated considerable differences in eastern African vegetation growth under the influence of an intensified ENSO regime which will bring further environmental stress to a region with a reduced capacity to adapt effects of global climate change and food security.

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Robustness and uncertainty in terrestrial ecosystem carbon response to CMIP5 climate change projections

Cited by 253 publications

References 38 publications

Reliable, robust and realistic: the three R's of next-generation land-surface modelling

Reliable, robust and realistic: the three R's of next-generation land-surface modelling

Implications of accounting for land use in simulations of ecosystem carbon cycling in Africa

The influence of El Niño–Southern Oscillation regimes on eastern African vegetation and its future implications under the RCP8.5 warming scenario

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