Interactions between land use change and carbon cycle feedbacks

Mahowald, N. M.; Randerson, James T.; Lindsay, Keith; Munoz, E.; Doney, Scott C.; Lawrence, David M.; Schlunegger, Sarah; Ward, D. S.; Hoffman, Forrest M.

doi:10.1002/2016gb005374

Cited by 55 publications

(54 citation statements)

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“…The LASC, which is an indirect LULCC flux, occurs when conversion of land from natural lands (forests) to managed lands (crops or pasture) reduces the capacity of the land biosphere to take up anthropogenic carbon dioxide in the future (e.g., Gitz and Ciais, 2003). While small historically it may be of the same order as the net LULCC flux without LASC for future scenarios of strong CO 2 in-crease (Gerber et al, 2013;Mahowald et al, 2016;Pongratz et al, 2014). The land-use carbon feedback can be assessed in emission-driven simulations where LULCC carbon fluxes alter the atmospheric CO 2 concentration and the land-use changes also affect the climate through biogeophysical responses, both of which can then feed back onto the productivity of both natural and managed vegetation.…”

Section: Net Lulcc Carbon Flux: Loss Of Additional Sink Capacity and mentioning

confidence: 99%

The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: rationale and experimental design

et al. 2016

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Abstract. Human land-use activities have resulted in large changes to the Earth's surface, with resulting implications for climate. In the future, land-use activities are likely to expand and intensify further to meet growing demands for food, fiber, and energy. The Land Use Model Intercomparison Project (LUMIP) aims to further advance understanding of the impacts of land-use and land-cover change (LULCC) on climate, specifically addressing the following questions.(1) What are the effects of LULCC on climate and biogeochemical cycling (past-future)? (2) What are the impacts of land management on surface fluxes of carbon, water, and energy, and are there regional land-management strategies with the promise to help mitigate climate change? In addressing these questions, LUMIP will also address a range of more detailed science questions to get at process-level attribution, uncertainty, data requirements, and other related issues in more depth and sophistication than possible in a multi-model context to date. There will be particular focus on the separation and quantification of the effects on climate from LULCC relative to all forcings, separation of biogeochemical from biogeophysical effects of land use, the unique impacts of land-cover change vs. land-management change, modulation of land-use impact on climate by land-atmosphere coupling strength, and the extent to which impacts of enhanced CO 2 concentrations on plant photosynthesis are modulated by past and future land use.LUMIP involves three major sets of science activities: (1) development of an updated and expanded historical and future land-use data set, (2) an experimental protocol for specific LUMIP experiments for CMIP6, and (3) definition of metrics and diagnostic protocols that quantify model performance, and related sensitivities, with respect to LULCC. In this paper, we describe LUMIP activity (2), i.e., the LU-MIP simulations that will formally be part of CMIP6. These experiments are explicitly designed to be complementary to simulations requested in the CMIP6 DECK and historical simulations and other CMIP6 MIPs including ScenarioMIP, C4MIP, LS3MIP, and DAMIP. LUMIP includes a twophase experimental design. Phase one features idealized coupled and land-only model simulations designed to advance process-level understanding of LULCC impacts on climate, as well as to quantify model sensitivity to potential landcover and land-use change. Phase two experiments focus on quantification of the historic impact of land use and the poPublished by Copernicus Publications on behalf of the European Geosciences Union. tential for future land management decisions to aid in mitigation of climate change. This paper documents these simulations in detail, explains their rationale, outlines plans for analysis, and describes a new subgrid land-use tile data request for selected variables (reporting model output data separately for primary and secondary land, crops, pasture, and urban land-use types). It is essential that modeling groups participating in LUMIP adhere to t...

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Section: Net Lulcc Carbon Flux: Loss Of Additional Sink Capacity and mentioning

confidence: 99%

The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: rationale and experimental design

et al. 2016

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“…If we continue on our present course, life on the Earth will be modified to such an extent that it will be very difficult to fix (IPCC, 2000; Mahowald et al, 2017). Due to several activities like combustion of fossil fuels and deforestation during the last 100 years, chemical makeup of this flimsy layer of the atmosphere has been extremely altered (Pold et al, 2017).…”

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confidence: 99%

Agroforestry Status and Its Role to Sequester Atmospheric Co2 Under Semi-Arid Climatic Conditions in Pakistan

Nawaz¹

2018

Appl. Ecol. Env. Res.

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. Furthermore, the study area has the potential of stocking 3607.61 Mt and sequestering 13214.67 Mt of CO 2 at the rate of 327232.46 t CO 2 per year. According to calculations, increasing the number of farm trees/ha, average CO 2 sequestration rate of the study area can be increased from 2.05 t CO 2 ha -1 yr -1 to 3.59 t CO 2 ha -1 yr -1 . The role of agroforestry as C sink in not negligible and it should be given a dire consideration in policies, especially, in low forest countries like Pakistan to meets the melinium goals of atmospheric C reduction.

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“…However since these methods operate sequentially, the metric and relative weighting of observational products have to be chosen beforehand, which can prove difficult if multiple observational data sets are to be assimilated simultaneously. The assessment of the performance of a given model version using observational benchmarks has also been actively discussed in the literature (Hoffman et al, 2017;Peng et al, 2014;Kelley et al, 2013;Luo 15 et al, 2012;Blyth et al, 2011;Randerson et al, 2009) and different frameworks have been proposed. Here we employ the Latin Hypercube Sampling (LHS) (McKay et al, 1979) approach, as used successfully in previous studies Battaglia et al, 2016;Steinacher and Joos, 2016;Battaglia and Joos, 2017;Zaehle et al, 2005).…”

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confidence: 99%

“…Furthermore, it can also be used in model intercomparison studies, where single realizations of different models are compared. benchmarking (Hoffman et al, 2017;Kelley et al, 2013;Luo et al, 2012;Blyth et al, 2011) by comparing different models or model versions graphically and using statistical metrics (Stow et al, 2009) to a broad and diverse range of observations.…”

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A Bayesian Ensemble Data Assimilation to Constrain Model Parameters and Land Use Carbon Emissions

Lienert¹,

Joos²

2018

Preprint

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Abstract.A dynamic global vegetation model (DGVM) is applied in a probabilistic framework and benchmarking system to constrain uncertain model parameters by observations and to quantify carbon emissions from land-use and land-cover change (LULCC).Processes featured in DGVMs include parameters which are prone to substantial uncertainty. To cope with these uncertainties Latin Hypercube Sampling (LHS) is used to create a 1000-member perturbed parameter ensemble, which is then evaluated with 5 a diverse set of global and spatio-temporaly resolved observational constraints. We discuss the performance of the constrained ensemble and use it to formulate a new best-guess version of the model (LPX-Bern v1.4). The observationally constrained ensemble is used to investigate historical emissions due to LULCC (E LUC ) and their sensitivity to model parametrization.We find a global E LUC estimate of 158 (108, 211) PgC (median and 90% confidence interval) between 1800 and 2016. We compare E LUC to other estimates both globally and regionally. Spatial patterns are investigated and estimates of E LUC of the 10 ten countries with the largest contribution to the flux over the historical period are reported. We consider model versions with and without additional land-use processes (shifting cultivation and wood harvest) and find that the difference in global E LUC is on the same order of magnitude as parameter induced uncertainty and in some cases could potentially even be offset with appropriate parameter choice.

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Interactions between land use change and carbon cycle feedbacks

Cited by 55 publications

References 94 publications

The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: rationale and experimental design

The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: rationale and experimental design

Agroforestry Status and Its Role to Sequester Atmospheric Co2 Under Semi-Arid Climatic Conditions in Pakistan

A Bayesian Ensemble Data Assimilation to Constrain Model Parameters and Land Use Carbon Emissions

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