Abstract Historically, humans have cleared many forests for agriculture. While this substantially reduced ecosystem carbon storage, the impacts of these land cover changes on terrestrial gross primary productivity (GPP) have not been adequately resolved yet. Here, we combine high-resolution datasets of satellite-derived GPP and environmental predictor variables to estimate the potential GPP of forests, grasslands, and croplands around the globe. With a mean GPP of 2.0 kg C m −2 yr −1 forests represent the most productive land cover on two thirds of the total area suitable for any of these land cover types, while grasslands and croplands on average reach 1.5 and 1.8 kg C m −2 yr −1 , respectively. Combining our potential GPP maps with a historical land-use reconstruction indicates a 4.4% reduction in global GPP from agricultural expansion. This land-use-induced GPP reduction is amplified in some future scenarios as a result of ongoing deforestation (e.g., the large-scale bioenergy scenario SSP4-3.4) but partly reversed in other scenarios (e.g., the sustainability scenario SSP1-1.9) due to agricultural abandonment. Comparing our results to simulations from state-of-the-art Earth System Models, we find that all investigated models deviate substantially from our estimates and from each other. Our maps could be used as a benchmark to reduce this inconsistency, thereby improving projections of land-based climate mitigation potentials.
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Carbon storage and sequestration in Southeast Asian urban clusters under future land cover change scenarios (2015–2050)
Land-use land-cover (LULC) changes are occurring rapidly in Southeast Asia (SEA), generally associated with population growth, economic development and competing demands for land. Land cover change is one of the vital factors affecting carbon dynamics and emissions. SEA is an important region to study urban-caused LULC emissions and the potential for nature-based solutions (NBS) and nature climate solutions (NCS), as it is home to nearly 15% of the world’s tropical forests and has some of the world’s fastest rates of urban growth. We present a fine-scale urban cluster level assessment for SEA of current (2015) and future (2050) scenarios for carbon sequestration service and climate mitigation potential. We identified 956 urban clusters distributed across 11 countries of SEA. Considering the urban expansion projected and decline in forests, this region could see a carbon loss of up to 0.11 Gigatonnes (Scenario SSP4 RCP 3.4). Comparing carbon change values to urban emissions, we found that the average offset value ranging from −2% (Scenario SSP1 RCP 2.6) to −21%. We also found that a few medium and large urban clusters could add to more than double the existing carbon emissions in 2050 in the SSP3 and SSP4 RCP 3.4 scenarios, while a minority of clusters could offset their emissions under SSP1. Our study confirms that NCS, and particularly reforestation, are in many cases able to offset the direct emissions from land cover conversion from SEA urban clusters. Hence, documenting the plausible LULC transitions and the associated impacts gains significance in the SEA region as the results can be useful for informing policy and sustainable land management.
more » « less- Award ID(s):
- 2020635
- NSF-PAR ID:
- 10483115
- Publisher / Repository:
- Frontiers Media S.A..
- Date Published:
- Journal Name:
- Frontiers in Environmental Science
- Volume:
- 11
- ISSN:
- 2296-665X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The ocean has absorbed about 25% of the carbon emitted by humans to date. To better predict how much climate will change, it is critical to understand how this ocean carbon sink will respond to future emissions. Here, we examine the ocean carbon sink response to low emission (SSP1-1.9, SSP1-2.6), intermediate emission (SSP2-4.5, SSP5-3.4-OS), and high emission (SSP5-8.5) scenarios in CMIP6 Earth System Models and in MAGICC7, a reduced-complexity climate carbon system model. From 2020–2100, the trajectory of the global-mean sink approximately parallels the trajectory of anthropogenic emissions. With increasing cumulative emissions during this century (SSP5-8.5 and SSP2-4.5), the cumulative ocean carbon sink absorbs 20%–30% of cumulative emissions since 2015. In scenarios where emissions decline, the ocean absorbs an increasingly large proportion of emissions (up to 120% of cumulative emissions since 2015). Despite similar responses in all models, there remains substantial quantitative spread in estimates of the cumulative sink through 2100 within each scenario, up to 50 PgC in CMIP6 and 120 PgC in the MAGICC7 ensemble. We demonstrate that for all but SSP1-2.6, approximately half of this future spread can be eliminated if model results are adjusted to agree with modern observation-based estimates. Considering the spatial distribution of air-sea CO2fluxes in CMIP6, we find significant zonal-mean divergence from the suite of newly-available observation-based constraints. We conclude that a significant portion of future ocean carbon sink uncertainty is attributable to modern-day errors in the mean state of air-sea CO2fluxes, which in turn are associated with model representations of ocean physics and biogeochemistry. Bringing models into agreement with modern observation-based estimates at regional to global scales can substantially reduce uncertainty in future role of the ocean in absorbing anthropogenic CO2from the atmosphere and mitigating climate change.
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Abstract This study seeks to understand how Argentina's energy, water, and land (EWL) systems will co‐evolve under a representative array of human and earth system influences, including socioeconomic change, climate change, and climate policy. To capture Argentina's sub‐national EWL dynamics in the context of global change, we couple the Global Change Analysis Model with a suite of consistent, gridded sectoral downscaling models to explore multiple stakeholder‐engaged scenarios. Across scenarios, Argentina has the economic opportunity to use its vast land resources to satisfy growing domestic and international demand for crops, such as oil (e.g., soy) and biomass. The human (rather than earth) system produces the most dominant changes in mid‐century EWL resource use. A Reference scenario characterized by modest socioeconomic growth projects a 40% increase in Argentina's agricultural production by 2050 (relative to 2020) by using 50,000 km2of additional cropland and 40% more water. A Climate Policy scenario designed to achieve net‐zero carbon emissions globally shortly after mid‐century projects that Argentina could use 100,000 km2of additional land (and 65% more water) to grow biomass and other crops. The burden of navigating these national opportunities and challenges could fall disproportionately on a subset of Argentina's river basins. The Colorado and Negro basins could experience moderate‐to‐severe water scarcity as they simultaneously navigate substantial irrigated crop demand growth and climate‐induced declines in natural water availability. Argentina serves as a generalizable testbed to demonstrate that multi‐scale EWL planning challenges can be identified and managed more effectively via integrated analysis of coupled human‐earth systems.
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Abstract We use six Earth system models (ESMs) run under SSP3-7.0, a scenario characterized by a relatively large land use change (LUC) over the 21st century, and under a variant of the same scenario where a significantly different pattern of LUC, taken from SSP1-2.6, was used, all else being equal. Our goal is to identify changes in climate extremes between the two scenarios that are statistically significant and robust across the ESMs. The motivation for this study is to test a long-held assumption of the shared socio-economic pathway-representative concentration pathway (SSP-RCP) scenario framework: that the signal from LUC can be safely disregarded when pairing different SSPs to the compatible RCPs, where compatibility only considers global radiative forcing, predominantly determined by well-mixed greenhouse gasses emissions. We analyze extremes of daily minimum and maximum temperatures and precipitation, after fitting non-stationary generalized extreme value distributions in a way that borrows strength along the length of the simulation (2015–2100) and across initial condition ensembles. We consider changes in the 20 year return levels (RL20s) of these metrics by 2100, and focus on eight locations where LUC is large within each scenario, and strongly differs between scenarios, averaging the RL20s over a neighborhood characterized by the same LUC to enhance the signal to noise. We find that precipitation extremes do not show significant differences attributable to LUC differences. For temperature extremes (cold and hot) results are mixed, with some location-index combination showing significant results for some of the ESMs but not all, and not many coherent changes appearing for indices across regions, or regions across indices. These ESMs are representative of what is typically adopted as the source of climate information for impact studies, when the SSP-RCP framework is put to use. Overall, our analysis suggests that the hypothesis to pair SSPs to RCPs in a flexible fashion is overall defensible. However, the appearance of some coherence in a few locations and for some indices invites further investigation.
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Abstract Aim Previous work demonstrated the global variability of synchrony in tree growth within populations, that is, the covariance of the year‐to‐year variability in growth of individual neighbouring trees. However, there is a lack of knowledge about the causes of this variability and its trajectories through time. Here, we examine whether climate can explain variation in within‐population synchrony (WPS) across space but also through time and we develop models capable of explaining this variation. These models can be applied to the global tree cover under current and future climate change scenarios.
Location Global.
Time period 1901–2012.
Major taxa studied Trees.
Methods We estimated WPS values from a global tree‐ring width database consisting of annual growth increment measurements from multiple trees at 3,579 sites. We used generalized linear mixed effects models to infer the drivers of WPS variability and temporal trends of global WPS. We then predicted WPS values across the global extent of tree cover. Finally, we applied our model to predict future WPS based on the RCP 8.5 (2045–2065 period) emission scenario.
Results Areas with the highest WPS are characterized by a combination of environments with both high mean annual temperature (>10°C) and low precipitation (<300 mm). Average WPS across all temperate forests has decreased historically and will continue to decrease. Potential implications of these patterns include changes in forest dynamics, such as higher tree growth and productivity and an increase in carbon sequestration. In contrast, the WPS of tropical forests of Central and South America will increase in the near future owing to reduced annual precipitation.
Main conclusions Climate explains WPS variability in space and time. We suggest that WPS might have value as an integrative ecological measure of the level of environmental stress to which forests are subjected and therefore holds potential for diagnosing effects of global climate change on tree growth.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fenvs.2023.1105759
