Characteristics of human-climate feedbacks differ at different radiative forcing levels

Abstract

The human and Earth systems are intricately linked: climate influences agricultural production, renewable energy potential, and water availability, for example, while anthropogenic emissions from industry and land use change alter temperature and precipitation. Such feedbacks have the potential to significantly alter future climate change. These feedbacks may also exert significant changes on 21 st-century energy, agriculture, land use and carbon cycle projections, but little is known about their possible magnitudes, or about regional and sector dynamics under different forcing scenarios. Here we use an integrated Earth System Model (ESM) featuring bidirectional information exchange between an economically-oriented integrated assessment model and the ESM to examine how human-natural feedbacks operate under high and medium radiative forcing (RF) scenarios. Specifically, we examine the effect of changing land productivity on human systems, and the effect of changing land use/land cover and CO 2 emissions on the Earth system. We find that the effect of coupling differs across radiative forcing levels and across regions, due to differences in the climate signal, human responses to those signals, and regional characteristics. In particular, we find reductions in cropland area due to feedbacks in both the medium and high RF scenarios. In the medium RF scenario, these reductions result in increased area for bioenergy and afforestation and reduced energy system CO 2 emissions, as the carbon price in this scenario incentivizes low carbon energy sources and terrestrial carbon storage; these incentives are absent in the high RF scenario. These differences are key to understanding the possible future evolution pathways of the integrated Earth system in response to 21 st century climate change. Additional models and hypothesis testing are needed to determine exactly when and how bidirectional feedbacks between human and Earth systems should be considered in future assessments.

Quantifying the effects of multiple land management practices, land cover change, and wildfire on the California landscape carbon budget with an empirical model

“…Sources of CH 4 emissions include anaerobic decomposition of soil organic carbon in wetlands and wood products in landfills, while biomass burning due to wildfire, prescribed burning, or bioenergy production generates both CH 4 and BC emissions. CALAND combines California-specific empirical data on carbon states and dynamics with externally modeled (or otherwise estimated) wildfire [ 16 ], climate [ 17 ], and LULCC [ 18 – 20 ] drivers specific to California.…”

“…The three main differences between the LULCC options are that 1) the default, land-use driven data show a relatively small annual loss of Cultivated land (-3,444 ha yr -1 ) while the remote-sensing data show a small gain (6,343 ha yr -1 ), and 2) the remote-sensing data show substantially greater annual losses of Shrubland (-210,189 ha yr -1 ) and Woodland (-32,804 ha yr -1 ) and greater annual gains to Grassland (144,260 ha yr -1 ) and Sparse (20,548 ha yr -1 ), attributed mainly to wildfire (Gonzalez 2015), while the land-use driven data show relatively small annual decreases in Shrubland (-2,913 ha yr -1 ), Grassland (-4,156 ha yr -1 ), Woodland (-1,291 ha yr -1 ), and Sparse (-373 ha yr -1 ), and 3) the remote-sensing data show a considerable gain in Water (5,511 ha yr -1 ) while the land-use driven data have constant Water area (see Table 1 for CALAND land categories). The potential effects of climate change on carbon dynamics [ 12 , 14 , 17 ] and wildfire [ 16 ] are optional, with three choices: historical (no climate change effects), Representative Concentration Pathway (RCP) 4.5, or RCP 8.5.…”

Identifying key processes and sectors in the interaction between climate and socio-economic systems: a review toward integrating Earth–human systems

“…In terms of impacts on GDP, Fujimori et al (2018) concluded that the negative impact of changes in crop yield on GDP would be 0.02-0.06% (globally, the first and third quartiles) in 2100 even for RCP8.5, and the socio-economic assumption (choice of Shared Socioeconomic Pathway [SSP]) had an impact one order larger. A change in crop productivity could affect cropland area and crop price (Thornton et al 2017;Calvin et al 2019).…”

Dynamic projection of anthropogenic emissions in China: methodology and 2015–2050 emission pathways under a range of socioeconomic, climate policy, and pollution control scenarios

“…GCAM is stewarded by the Joint Global Change Research Institute (JGCRI) (GCAM, 2019, http://jgcri.github.io/gcam-doc/index.html), and more detailed documentation on GCAM can be found at http://www.globalchange.umd.edu/models/gcam/. Over time, GCAM is increasingly used in climate (Zhou et al, 2013;Fawcett et al, 2015;Calvin et al, 2019;Shinha et al, 2019), energy (Belete et al, 2019;Silva Herran et al, 2019;, land use (Dong et al, 2018;Turner et al, 2018;Vittorio et al, 2018) and modeling studies. In addition, GCAM also provides a number of scenarios and assessments for various organizations and reports, such as the Energy Modeling Forum (EMF), the U.S.…”