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  1. Projecting climate change is a generalization problem: We extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model grid size, which have been the main source of model projection uncertainty. Recent machine learning (ML) algorithms hold promise to improve such process representations but tend to extrapolate poorly to climate regimes that they were not trained on. To get the best of the physical and statistical worlds, we propose a framework, termed “climate-invariant” ML, incorporating knowledge of climate processes into ML algorithms, and show that it can maintain high offline accuracy across a wide range of climate conditions and configurations in three distinct atmospheric models. Our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes can improve their consistency, data efficiency, and generalizability across climate regimes.

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    Free, publicly-accessible full text available February 7, 2025
  2. The factors controlling the present-day pattern of temperature variance are poorly understood. In particular, it is unclear why the variance of wintertime near-surface temperatures on daily and synoptic time scales is roughly twice as high over North America as over Eurasia. In this study, continental geometry’s role in shaping regional wintertime temperature variance is investigated using idealized climate model simulations run with midlatitude continents of different shapes. An isolated, rectangular midlatitude continent suggests that in the absence of other geographic features, the highest temperature variance will be located in the northwest of the continent, roughly collocated with the region of largest meridional temperature gradients, and just north of the maximum near-surface wind speeds. Simulations with other geometries, mimicking key features of North America and Eurasia, investigate the impacts of continental length and width, sloping coastlines, and inland bodies of water on regional temperature variance. The largest effect comes from tapering the northwest corner of the continent, similar to Eurasia, which substantially reduces the maximum temperature variance. Narrower continents have smaller temperature variance in isolation, implying that the high variances over North America must be due to the nonlocal influence of stationary waves. Support for this hypothesis is provided by simulations with two midlatitude continents, which show how continental geometry and stationary waves can combine to shape regional temperature variance. 
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  3. Abstract

    The influence of El Niño–Southern Oscillation (ENSO) in the Asian monsoon region can persist through the post-ENSO summer, after the sea surface temperature (SST) anomalies in the tropical Pacific have dissipated. The long persistence of coherent post-ENSO anomalies is caused by a positive feedback due to interbasin ocean–atmospheric coupling, known as the Indo-western Pacific Ocean capacitor (IPOC) effect, although the feedback mechanism itself does not necessarily rely on the antecedence of ENSO events, suggesting the potential for substantial internal variability independent of ENSO. To investigate the respective role of ENSO forcing and non-ENSO internal variability, we conduct ensemble “forecast” experiments with a full-physics, globally coupled atmosphere–ocean model initialized from a multidecadal tropical Pacific pacemaker simulation. The leading mode of internal variability as represented by the forecast-ensemble spread resembles the post-ENSO IPOC, despite the absence of antecedent ENSO forcing by design. The persistent atmospheric and oceanic anomalies in the leading mode highlight the positive feedback mechanism in the internal variability. The large sample size afforded by the ensemble spread allows us to identify robust non-ENSO precursors of summer IPOC variability, including a cool SST patch over the tropical northwestern Pacific, a warming patch in the tropical North Atlantic, and downwelling oceanic Rossby waves in the tropical Indian Ocean south of the equator. The pathways by which the precursors develop into the summer IPOC mode and the implications for improved predictability are discussed.

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  4. Abstract

    The northeastern United States (NEUS) is a densely populated region with a number of major cities along the climatological storm track. Despite its economic and social importance, as well as the area’s vulnerability to flooding, there is significant uncertainty around future trends in extreme precipitation over the region. Here, we undertake a regional study of the projected changes in extreme precipitation over the NEUS through the end of the twenty-first century using an ensemble of high-resolution, dynamically downscaled simulations from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) project. We find that extreme precipitation increases throughout the region, with the largest changes in coastal regions and smaller changes inland. These increases are seen throughout the year, although the smallest changes in extreme precipitation are seen in the summer, in contrast to earlier studies. The frequency of heavy precipitation also increases such that there are relatively fewer days with moderate precipitation and relatively more days with either no or strong precipitation. Averaged over the region, extreme precipitation increases by +3%–5% °C−1of local warming, with the largest fractional increases in southern and inland regions and occurring during the winter and spring seasons. This is lower than the +7% °C−1rate expected from thermodynamic considerations alone and suggests that dynamical changes damp the increases in extreme precipitation. These changes are qualitatively robust across ensemble members, although there is notable intermodel spread associated with models’ climate sensitivity and with changes in mean precipitation. Together, the NA-CORDEX simulations suggest that this densely populated region may require significant adaptation strategies to cope with the increase in extreme precipitation expected at the end of the next century.

    Significance Statement

    Observations show that the northeastern United States has already experienced increases in extreme precipitation, and prior modeling studies suggest that this trend is expected to continue through the end of the century. Using high-resolution climate model simulations, we find that coastal regions will experience large increases in extreme precipitation (+6.0–7.5 mm day−1), although there is significant intermodel spread in the trends’ spatial distribution and in their seasonality. Regionally averaged, extreme precipitation will increase at a rate of ∼2% decade−1. Our results also suggest that the frequency of extreme precipitation will increase, with the strongest storms doubling in frequency per degree of warming. These results, taken with earlier studies, provide guidance to aid in resiliency preparation and planning by regional stakeholders.

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  5. null (Ed.)
    Abstract Southern Ocean (SO) surface winds are essential for ventilating the upper ocean by bringing heat and CO 2 to the ocean interior. The relationships between mixed-layer ventilation, the Southern Annular Mode (SAM), and the storm tracks remain unclear because processes can be governed by short-term wind events as well as long-term means. In this study, observed time-varying 5-day probability density functions (PDFs) of ERA5 surface winds and stresses over the SO are used in a singular value decomposition to derive a linearly independent set of empirical basis functions. The first modes of wind (72% of the total wind variance) and stress (74% of the total stress variance) are highly correlated with a standard SAM index ( r = 0.82) and reflect SAM’s role in driving cyclone intensity and, in turn, extreme westerly winds. This Joint PDFs of zonal and meridional wind show that southerly and less westerly winds associated with strong mixed-layer ventilation are more frequent during short and distinct negative SAM phases. The probability of these short-term events might be related to mid-latitude atmospheric circulation. The second mode describes seasonal changes in the wind variance (16% of the total variance) that are uncorrelated with the first mode. The analysis produces similar results when repeated using 5-day PDFs from a suite of scatterometer products. Differences between wind product PDFs resemble the first mode of the PDFs. Together, these results show a strong correlation between surface stress PDFs and the leading modes of atmospheric variability, suggesting that empirical modes can serve as a novel pathway for understanding differences and variability of surface stress PDFs. 
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  6. Abstract

    The rate of transient warming is determined by a number of factors, notably the radiative forcing from increasing CO2concentrations and the net radiative feedback. Uncertainty in transient warming comes from both the uncertainty in each factor and from the warming's sensitivity to uncertainty in each factor. An energy balance model is used to untangle these two components of uncertainty in transient warming, which is shown to be most sensitive to uncertainty in the forcing and not to uncertainty in radiative feedbacks. Additionally, uncertainty in the efficacy of ocean heat uptake is more important than uncertainty in the rate of ocean heat uptake. Three further implications are as follows: (1) transient warming is highly sensitive to uncertainty in emissions, (2) caution is warranted when extrapolating future warming trends from short‐lived climate perturbations, and (3) climate models tuned using the historical record are highly sensitive to assumptions made about the historical forcing.

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