6 research outputs found

    Decomposing the drivers of polar amplification with a single-column model

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    This is the final version. Available from the American Meteorological Society via the DOI in this record. The code and data needed to reproduce all figures, tables, and supplemental figures are available at https:// github.com/matthewjhenry/HMLR19_SCM. Documentation for the Python ClimLab package can be found at https://climlab. readthedocs.io/. The top-of-atmosphere albedo data from the Cloud and the Earth’s Radiant Energy System (CERES) can be found at https://ceres.larc.nasa.gov/. The CMIP6 data are available on the Earth System Grid Federation database. TThe precise mechanisms driving Arctic amplification are still under debate. Previous attribution methods compute the vertically uniform temperature change required to balance the top-of-atmosphere energy imbalance caused by each forcing and feedback, with any departures from vertically uniform warming collected into the lapse-rate feedback. We propose an alternative attribution method using a single-column model that accounts for the forcing dependence of high-latitude lapse-rate changes. We examine this method in an idealized general circulation model (GCM), finding that, even though the column-integrated carbon dioxide (CO2) forcing and water vapor feedback are stronger in the tropics, they contribute to polar-amplified surface warming as they produce bottom-heavy warming in high latitudes. A separation of atmospheric temperature changes into local and remote contributors shows that, in the absence of polar surface forcing (e.g., sea ice retreat), changes in energy transport are primarily responsible for the polar-amplified pattern of warming. The addition of surface forcing substantially increases polar surface warming and reduces the contribution of atmospheric dry static energy transport to the warming. This physically based attribution method can be applied to comprehensive GCMs to provide a clearer view of the mechanisms behind Arctic amplification.Natural Sciences and Engineering Research Council of CanadaNational Science Foundation (USA

    Progressing emergent constraints on future climate change

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    In recent years, an evaluation technique for Earth System Models (ESMs) has arisen—emergent constraints (ECs)—which rely on strong statistical relationships between aspects of current climate and future change across an ESM ensemble. Combining the EC relationship with observations could reduce uncertainty surrounding future change. Here, we articulate a framework to assess ECs, and provide indicators whereby a proposed EC may move from a strong statistical relationship to confirmation. The primary indicators are verified mechanisms and out-of-sample testing. Confirmed ECs have the potential to improve ESMs by focusing attention on the variables most relevant to climate projections. Looking forward, there may be undiscovered ECs for extremes and teleconnections, and ECs may help identify climate system tipping points
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