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  1. Abstract

    In recent decades, Arctic-amplified warming and sea-ice loss coincided with a prolonged wintertime Eurasian cooling trend. This observed Warm Arctic–Cold Eurasia pattern has occasionally been attributed to sea-ice forced changes in the midlatitude atmospheric circulation, implying an anthropogenic cause. However, comprehensive climate change simulations do not produce Eurasian cooling, instead suggesting a role for unforced atmospheric variability. This study seeks to clarify the source of this model-observation discrepancy by developing a statistical approach that enables direct comparison of Arctic-midlatitude interactions. In both historical simulations and observations, we first identify Ural blocking as the primary causal driver of sea ice, temperature, and circulation anomalies consistent with the Warm Arctic–Cold Eurasia pattern. Next, we quantify distinct transient responses to this Ural blocking, which explain the model-observation discrepancy in historical Eurasian temperature. Observed 1988–2012 Eurasian cooling occurs in response to a pronounced positive trend in Ural sea-level pressure, temporarily masking long-term midlatitude warming. This observed sea-level pressure trend lies at the outer edge of simulated variability in a fully coupled large ensemble, where smaller sea-level pressure trends have little impact on the ensemble mean temperature trend over Eurasia. Accounting for these differences bring observed and simulated trends into remarkable agreement. Finally, we quantify the influence of sea-ice loss on the magnitude of the observed Ural sea-level pressure trend, an effect that is absent in historical simulations. These results illustrate that sea-ice loss and tropospheric variability can both play a role in producing Eurasian cooling. Furthermore, by conducting a direct model-observation comparison, we reveal a key difference in the causal structures characterizing the Warm Arctic–Cold Eurasia Pattern, which will guide ongoing efforts to explain the lack of Eurasian cooling in climate change simulations.

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    Free, publicly-accessible full text available February 5, 2025
  2. Free, publicly-accessible full text available December 5, 2024
  3. Abstract

    Climate changepoint (homogenization) methods abound today, with a myriad of techniques existing in both the climate and statistics literature. Unfortunately, the appropriate changepoint technique to use remains unclear to many. Further complicating issues, changepoint conclusions are not robust to perturbations in assumptions; for example, allowing for a trend or correlation in the series can drastically change changepoint conclusions. This paper is a review of the topic, with an emphasis on illuminating the models and techniques that allow the scientist to make reliable conclusions. Pitfalls to avoid are demonstrated via actual applications. The discourse begins by narrating the salient statistical features of most climate time series. Thereafter, single- and multiple-changepoint problems are considered. Several pitfalls are discussed en route and good practices are recommended. While most of our applications involve temperatures, a sea ice series is also considered.

    Significance Statement

    This paper reviews the methods used to identify and analyze the changepoints in climate data, with a focus on helping scientists make reliable conclusions. The paper discusses common mistakes and pitfalls to avoid in changepoint analysis and provides recommendations for best practices. The paper also provides examples of how these methods have been applied to temperature and sea ice data. The main goal of the paper is to provide guidance on how to effectively identify the changepoints in climate time series and homogenize the series.

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  4. Abstract This paper presents a statistical analysis of structural changes in the Central England temperature series, one of the longest surface temperature records available. A changepoint analysis is performed to detect abrupt changes, which can be regarded as a preliminary step before further analysis is conducted to identify the causes of the changes (e.g., artificial, human-induced or natural variability). Regression models with structural breaks, including mean and trend shifts, are fitted to the series and compared via two commonly used multiple changepoint penalized likelihood criteria that balance model fit quality (as measured by likelihood) against parsimony considerations. Our changepoint model fits, with independent and short-memory errors, are also compared with a different class of models termed long-memory models that have been previously used by other authors to describe persistence features in temperature series. In the end, the optimal model is judged to be one containing a changepoint in the late 1980s, with a transition to an intensified warming regime. This timing and warming conclusion is consistent across changepoint models compared in this analysis. The variability of the series is not found to be significantly changing, and shift features are judged to be more plausible than either short- or long-memory autocorrelations. The final proposed model is one including trend-shifts (both intercept and slope parameters) with independent errors. The analysis serves as a walk-through tutorial of different changepoint techniques, illustrating what can be statistically inferred. 
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  5. null (Ed.)
    Abstract. The strength of the Atlantic meridional overturning circulation(AMOC) at 26∘ N has now been continuously measured by the RAPIDarray over the period April 2004–September 2018. This record provides uniqueinsight into the variability of the large-scale ocean circulation,previously only measured by sporadic snapshots of basin-wide transport fromhydrographic sections. The continuous measurements have unveiled strikingvariability on timescales of days to a decade, driven largely bywind forcing, contrasting with previous expectations about a slowly varyingbuoyancy-forced large-scale ocean circulation. However, these measurementswere primarily observed during a warm state of the Atlantic multidecadalvariability (AMV) which has been steadily declining since a peak in2008–2010. In 2013–2015, a period of strong buoyancy forcing by theatmosphere drove intense water-mass transformation in the subpolar NorthAtlantic and provides a unique opportunity to investigate the response ofthe large-scale ocean circulation to buoyancy forcing. Modelling studiessuggest that the AMOC in the subtropics responds to such events with anincrease in overturning transport, after a lag of 3–9 years. At45∘ N, observations suggest that the AMOC may already beincreasing. Examining 26∘ N, we find that the AMOC is no longerweakening, though the recent transport is not above the long-term mean.Extending the record backwards in time at 26∘ N with oceanreanalysis from GloSea5, the transport fluctuations at 26∘ N areconsistent with a 0- to 2-year lag from those at 45∘ N, albeit withlower magnitude. Given the short span of time and anticipated delays in thesignal from the subpolar to subtropical gyres, it is not yet possible todetermine whether the subtropical AMOC strength is recovering nor how theAMOC at 26∘ N responds to intense buoyancy forcing. 
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