Climate Policy

Getting real about capturing carbon from the air (Commentary)

Author's Summary: Many modeling studies depend on direct air capture (DAC) in their 1.5°C stabilization scenarios. These studies rely on assumptions that are overly optimistic regarding the cost and scaling-up of DAC systems. This can lead to highly misleading results that can ultimately impact the ability to reach climate stabilization goals.

Abstract: Despite the commitments to the Paris Agreement’s goal of pursuing efforts to limit the global temperature increase to 1.5°C, the world exceeded this target for most if not all of 2023, raising questions about its longer-term feasibility. Most modeling studies rely on carbon dioxide removal (CDR) or negative emission technologies, such as direct air capture (DAC), bioenergy with carbon capture and storage (BECCS) and afforestation/reforestation, to keep long-term temperature targets in reach.1 DAC, in particular, has drawn substantial interest in recent years because it can generate high-quality carbon removal credits. Specifically, (1) the removal is immediate as opposed to over time as in, for example, afforestation/reforestation projects, (2) it is straightforward to measure and verify the “net” amount of carbon removed, and (3) when coupled with geologic storage, the CO2 will remain out of the atmosphere for millennia or more. 

While these advantages are compelling, there are also many practical challenges associated with real-world deployment of DAC that affect its cost and potential deployment, including challenges related to scaling-up, energy usage and siting. However, many modeling studies diminish or neglect these challenges, assuming costs of DAC deployment that do not align with the engineering realities of the technology.

Overly simplified or optimistic consideration of these challenges can lead to highly misleading results related to mitigation and adaptation strategies and their associated costs, and ultimately impact the ability to reach climate stabilization goals.

Anthropogenic Fingerprint Detectable in Upper Tropospheric Ozone Trends Retrieved from Satellite

Synopsis: A clear signal of human influence on upper tropospheric ozone trends is identifiable with high statistical confidence in a 17-year satellite dataset.

Abstract: Tropospheric ozone (O3) is a strong greenhouse gas, particularly in the upper troposphere (UT). Limited observations point to a continuous increase in UT O3 in recent decades, but the attribution of UT O3 changes is complicated by large internal climate variability.

We show that the anthropogenic signal (“fingerprint”) in the patterns of UT O3 increases is distinguishable from the background noise of internal variability. The time-invariant fingerprint of human-caused UT O3 changes is derived from a 16-member initial-condition ensemble performed with a chemistry-climate model (CESM2-WACCM6). The fingerprint is largest between 30°S and 40°N, especially near 30°N. In contrast, the noise pattern in UT O3 is mainly associated with the El Niño–Southern Oscillation (ENSO). The UT O3 fingerprint pattern can be discerned with high confidence within only 13 years of the 2005 start of the OMI/MLS satellite record. Unlike the UT O3 fingerprint, the lower tropospheric (LT) O3 fingerprint varies significantly over time and space in response to large-scale changes in anthropogenic precursor emissions, with the highest signal-to-noise ratios near 40°N in Asia and Europe.

Our analysis reveals a significant human effect on Earth’s atmospheric chemistry in the UT and indicates promise for identifying fingerprints of specific sources of ozone precursors.

 

Zero hunger. Affordable and clean energy. Reduced inequalities. These are among the  sustainable development goals that the United Nations has established in pursuit of the long-term well-being of the Earth and its inhabitants. But achieving goals like these—whether by the UN’s 2030 deadline or beyond—requires a detailed understanding of the many complex, interconnected, co-evolving natural, social and technological systems upon which all life depends.

Upgrading computational tools for sustainable development
Spatially Resolved Temperature Response Functions to CO2 Emissions

Abstract

Carbon dioxide (CO2) emissions affect local temperature; quantifying that local response is important for learning about the earth system, the impacts of mitigation, and adaptation needs. We assume the climate system can be represented as a time-dependent linear system, diagnosing Green's Functions for the spatial temperature response to CO2 emissions based on CMIP6 earth system models. This allows us to emulate the linear component of the temperature response to CO2. This approach is sufficient to capture the spatial temperature response of CMIP6 experiments within one standard deviation of the multimodel spread across most regions, though accuracy is lower in the Southern Ocean and the Arctic. Our approach reveals where nonlinear feedbacks are important in current CMIP6 models, and where the local system response is well represented by a time-dependent linear differential operator. It incorporates emissions path dependency and may be useful for evaluating large ensembles of emission scenarios.

Key Points

 

  • With a Green's Function approach, we emulate the linear component of the spatially resolved temperature response to CO2 emissions

  • We reproduce the temperature response well within multi-model uncertainty except in the Arctic and Southern Ocean

  • This approach allows expedient quantification of the spatial and temporal temperature response to varying CO2 emissions pathways

 

Plain Language Summary

Carbon dioxide (CO2) emissions impact surface temperature. It is well established that the global mean temperature change is proportional to the cumulative emissions of CO2. This has led to the creation of carbon budgets to reach temperature goals. We test this relationship at the spatio-temporal scale, quantifying a simple approach that estimates the local temperature response to CO2 emissions alone. We use an approach built from the Climate Model Intercomparison Project Phase 6 (CMIP6) Earth System Models, based on the concept that an additional unit of CO2 can be scaled for larger emissions and summed over time to estimate cumulative impacts. We evaluate this with additional CMIP6 experiments, showing that this approach captures the temperature response in most locations with lower accuracy in the Arctic and Southern Ocean. This type of approach may be useful to evaluate many policy scenarios and to better understand earth system processes that are represented in the models, as it takes around one second to quantify 90 years' worth of temperature change on a local computer, while Earth System Models can require weeks of runtime on supercomputers.

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