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Martin P. Tingley
and
Peter Huybers

Abstract

presented a Bayesian algorithm for reconstructing climate anomalies in space and time (BARCAST). This method involves specifying simple parametric forms for the spatial covariance and temporal evolution of the climate field as well as “observation equations” describing the relationships between the data types and the corresponding true values of the climate field. As this Bayesian approach to reconstructing climate fields is new and different, it is worthwhile to compare it in detail to the more established regularized expectation–maximization (RegEM) algorithm, which is based on an empirical estimate of the joint data covariance matrix and a multivariate regression of the instrumental time series onto the proxy time series. The differing assumptions made by BARCAST and RegEM are detailed, and the impacts of these differences on the analysis are discussed. Key distinctions between BARCAST and RegEM include their treatment of spatial and temporal covariance, the prior information that enters into each analysis, the quantities they seek to impute, the end product of each analysis, the temporal variance of the reconstructed field, and the treatment of uncertainty in both the imputed values and functions of these imputations. Differences between BARCAST and RegEM are illustrated by applying the two approaches to various surrogate datasets. If the assumptions inherent to BARCAST are not strongly violated, then in scenarios comparable to practical applications BARCAST results in reconstructions of both the field and the spatial mean that are more skillful than those produced by RegEM, as measured by the coefficient of efficiency. In addition, the uncertainty intervals produced by BARCAST are narrower than those estimated using RegEM and contain the true values with higher probability.

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Karen A. McKinnon
and
Peter Huybers

Abstract

The seasonal cycle in temperature is a large and well-observed response to radiative forcing, suggesting its potential as a natural analog to human-caused climate change. Although there have been advances constraining some climate feedback parameters using seasonal observations, the seasonal cycle has not been used to inform about the local temperature sensitivity to greenhouse gas forcing. In this study, we uncover a nonlinear relationship between the amplitude and phase of the seasonal cycle and forced temperature trends in seven CMIP5-era large ensembles across the Northern Hemisphere extratropical continents. We develop a mixture energy balance model that reproduces this relationship and reveals the unexpected finding that the phasing of the seasonal cycle—in addition to the amplitude—contains information about local temperature sensitivity to seasonal forcing over land. Using this energy balance model framework, we compare the pattern and magnitude of the seasonally inferred sensitivity of the surface temperature response to anthropogenic radiative forcing. The seasonally constrained model largely reproduces the pattern of human-caused temperature trends seen in climate models (r = 0.81, p value < 0.01), including polar amplification, but the magnitude of the response is smaller by about a factor of 3. Our results show the relevance of both phasing and amplitude for constraining patterns of local feedbacks and suggest the utility of additional research to better understand the differences in sensitivity between seasonal and greenhouse gas forcing.

Significance Statement

Warming in response to increased greenhouse gases is not spatially uniform across land. We wanted to understand whether the familiar seasonal cycle in temperature could provide information about climate change. We found that climate models show a strong link between the seasonal cycle and future warming: places with a larger and more delayed temperature response to the seasonal cycle in solar forcing tend to warm more across the Northern Hemisphere midlatitudes. A very simple model for the climate system, whose parameters are based on the seasonal cycle, captures the pattern but not the magnitude of warming. Our findings suggest that there are some similarities between the processes that control temperature change on seasonal and climate change time scales, but that we must understand the difference between seasonal and longer-term sensitivity to warming before the seasonal cycle can be used to reduce uncertainty about climate change.

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Duo Chan
,
Geoffrey Gebbie
, and
Peter Huybers

Abstract

Land surface air temperatures (LSAT) inferred from weather station data differ among major research groups. The estimate by NOAA’s monthly Global Historical Climatology Network (GHCNm) averages 0.02°C cooler between 1880 and 1940 than Berkeley Earth’s and 0.14°C cooler than the Climate Research Unit estimates. Such systematic offsets can arise from differences in how poorly documented changes in measurement characteristics are detected and adjusted. Building upon an existing pairwise homogenization algorithm used in generating the fourth version of NOAA’s GHCNm(V4), PHA0, we propose two revisions to account for autocorrelation in climate variables. One version, PHA1, makes minimal modification to PHA0 by extending the threshold used in breakpoint detection to be a function of LSAT autocorrelation. The other version, PHA2, uses penalized likelihood to detect breakpoints through optimizing a model-selection problem globally. To facilitate efficient optimization for series with more than 1000 time steps, a multiparent genetic algorithm is proposed for PHA2. Tests on synthetic data generated by adding breakpoints to CMIP6 simulations and realizations from a Gaussian process indicate that PHA1 and PHA2 both similarly outperform PHA0 in recovering accurate climatic trends. Applied to unhomogenized GHCNmV4, both revised algorithms detect breakpoints that correspond with available station metadata. Uncertainties are estimated by perturbing algorithmic parameters, and an ensemble is constructed by pooling 50 PHA1- and 50 PHA2-based members. The continental-mean warming in this new ensemble is consistent with that of Berkeley Earth, despite using different homogenization approaches. Relative to unhomogenized data, our homogenization increases the 1880–2022 trend by 0.16 [0.12, 0.19]°C century−1 (95% confidence interval), leading to continental-mean warming of 1.65 [1.62, 1.69]°C over 2010–22 relative to 1880–1900.

Significance Statement

Accurately correcting for systematic errors in observational records of land surface air temperature (LSAT) is critical for quantifying historical warming. Existing LSAT estimates are subject to systematic offsets associated with processes including changes in instrumentation and station movement. This study improves a pairwise homogenization algorithm by accounting for the fact that climate signals are correlated over time. The revised algorithms outperform the original in identifying discontinuities and recovering accurate warming trends. Applied to monthly station temperatures, the revised algorithms adjust trends in continental mean LSAT since the 1880s to be 0.16°C century−1 greater relative to raw data. Our estimate is most consistent with that from Berkeley Earth and indicates lesser and greater warming than estimates from NOAA and the Met Office, respectively.

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Duo Chan
,
Geoffrey Gebbie
, and
Peter Huybers

Abstract

A major uncertainty in reconstructing historical sea surface temperature (SST) before the 1990s involves correcting for systematic offsets associated with bucket and engine-room intake temperature measurements. A recent study used a linear scaling of coastal station-based air temperatures (SATs) to infer nearby SSTs, but the physics in the coupling between SATs and SSTs generally gives rise to more complex regional air–sea temperature differences. In this study, an energy-balance model (EBM) of air–sea thermal coupling is adapted for predicting near-coast SSTs from coastal SATs. The model is shown to be more skillful than linear-scaling approaches through cross-validation analyses using instrumental records after the 1960s and CMIP6 simulations between 1880 and 2020. Improved skill primarily comes from capturing features reflecting air–sea heat fluxes dominating temperature variability at high latitudes, including damping high-frequency wintertime SAT variability and reproducing the phase lag between SSTs and SATs. Inferred near-coast SSTs allow for intercalibrating coastal SAT and SST measurements at a variety of spatial scales. The 1900–40 mean offset between the latest SST estimates available from the Met Office (HadSST4) and SAT-inferred SSTs range between −1.6°C (95% confidence interval: [−1.7°, −1.4°C]) and 1.2°C ([0.8°, 1.6°C]) across 10° × 10° grids. When further averaged along the global coastline, HadSST4 is significantly colder than SAT-inferred SSTs by 0.20°C ([0.07°, 0.35°C]) over 1900–40. These results indicate that historical SATs and SSTs involve substantial inconsistencies at both regional and global scales. Major outstanding questions involve the distribution of errors between our intercalibration model and instrumental records of SAT and SST as well as the degree to which coastal intercalibrations are informative of global trends.

Significance Statement

To evaluate the consistency of instrumental surface temperature estimates before the 1990s, we develop a coupled energy-balance model to intercalibrate measurements of sea surface temperature (SST) and station-based air temperature (SAT) near global coasts. Our model captures geographically varying physical regimes of air–sea coupling and outperforms existing methods in inferring regional SSTs from SAT measurements. When applied to historical temperature records, the model indicates significant discrepancies between inferred and observed SSTs at both global and regional scales before the 1960s. Our findings suggest remaining data issues in historical temperature archives and opportunities for further improvements.

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Alexander R. Stine
and
Peter Huybers

Abstract

The vast majority of variability in the instrumental surface temperature record is at annual frequencies. Systematic changes in the yearly Fourier component of surface temperature have been observed since the midtwentieth century, including a shift toward earlier seasonal transitions over land. Here it is shown that the variability in the amplitude and phase of the annual cycle of surface temperature in the northern extratropics is related to Northern Hemisphere atmospheric circulation as represented by the northern annular mode (NAM) and the Pacific–North America mode (PNA). The phase of the seasonal cycle is most strongly influenced by changes in spring atmospheric circulation, whereas amplitude is most strongly influenced by winter circulation. A statistical model is developed based on the NAM and PNA values in these seasons and it successfully predicts the interdecadal trends in the seasonal cycle using parameters diagnosed only at interannual time scales. In particular, 70% of the observed amplitude trends and 68% of the observed phase trends are predicted over land, and the residual trends are consistent with internal variability. The strong relationship between atmospheric circulation and the structure of the seasonal cycle indicates that physical explanations for changes in atmospheric circulation also extend to explaining changes in the structure of the seasonal cycle.

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Andrew Rhines
and
Peter J. Huybers

Abstract

Greenland has experienced large changes since the last glacial with its summit warming by approximately 21°C, average accumulation rates tripling, and annual amplitudes of temperature and accumulation seemingly declining. The altered seasonal cycle of accumulation has been attributed to a combination of the large-scale dynamical response of the North Atlantic storm track to surface boundary conditions and the modulation of moisture availability due to changes in winter sea ice cover. Using atmospheric simulations of preindustrial and glacial climate, the contributions of these two mechanisms are evaluated. Estimates of moisture source footprints make it possible to distinguish between long-range transport related to the storm track and regional transport from the ocean surface near Greenland. It is found that the contribution of both mechanisms varies significantly with the background climate. With greater ice cover and the North Atlantic storm track locked to the topographically enhanced stationary wave during the glacial, seasonal migration of the sea ice edge becomes relatively important in controlling moisture availability. In contrast, the preindustrial simulation has relatively greater transient eddy activity and is less moisture limited by sea ice extent, so accumulation is more strongly related to synoptic variability in the North Atlantic. These results highlight how changes in atmospheric circulation and sea ice together explain the shifts in annual mean and seasonal moisture supply to Greenland. Also discussed are some implications of the inferred narrow source distribution of accumulation during the glacial for the interpretation of stable isotopes derived from the central Greenland ice cores.

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Peter Huybers
,
Geoffrey Gebbie
, and
Olivier Marchal

Abstract

The ability of paleoceanographic tracers to constrain rates of transport is examined using an inverse method to combine idealized observations with a geostrophic model. Considered are the spatial distribution, accuracy, and types of tracers required to constrain changes in meridional transport within an idealized single-hemisphere basin. Measurements of density and radioactive tracers each act to constrain rates of transport. Conservative tracers, while not of themselves able to inform regarding rates of transport, improve constraints when coupled with density or radioactive observations. It is found that the tracer data would require an accuracy one order of magnitude better than is presently available for paleo-observations to conclusively rule out factor-of-2 changes in meridional transport, even when assumed available over the entire model domain. When data are available only at the margins and bottom of the model, radiocarbon is unable to constrain transport while density remains effective only when a reference velocity level is assumed. The difficulty in constraining the circulation in this idealized model indicates that placing firm bounds on past meridional transport rates will prove challenging.

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Anna Lea Albright
,
Cristian Proistosescu
, and
Peter Huybers

Abstract

A variety of empirical estimates have been published for the lower bounds on aerosol radiative forcing, clustered around −1.0 or −2.0 W m−2. The reasons for obtaining such different constraints are not well understood. In this study, we explore bounds on aerosol radiative forcing using a Bayesian model of aerosol forcing and Earth’s multi-time-scale temperature response to radiative forcing. We first demonstrate the ability of a simple aerosol model to emulate aerosol radiative forcing simulated by 10 general circulation models. A joint inference of climate sensitivity and effective aerosol forcing from historical surface temperatures is then made over 1850–2019. We obtain a maximum likelihood estimate of aerosol radiative forcing of −0.85 W m−2 (5%–95% credible interval from −1.3 to −0.50 W m−2) for 2010–19 relative to 1750 and an equilibrium climate sensitivity of 3.4°C (5%–95% credible interval from 1.8° to 6.1°C). The wide range of climate sensitivity reflects difficulty in empirically constraining long-term responses using historical temperatures, as noted elsewhere. A relatively tight bound on aerosol forcing is nonetheless obtained from the structure of temperature and aerosol precursor emissions and, particularly, from the rapid growth in emissions between 1950 and 1980. Obtaining a 5th percentile lower bound on aerosol forcing around −2.0 W m−2 requires prescribing internal climate variance that is a factor of 5 larger than the CMIP6 mean and assuming large, correlated errors in global temperature observations. Ocean heat uptake observations may further constrain aerosol radiative forcing but require a better understanding of the relationship between time-variable radiative feedbacks and radiative forcing.

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Andrew Rhines
,
Karen A. McKinnon
,
Martin P. Tingley
, and
Peter Huybers

Abstract

There is considerable interest in determining whether recent changes in the temperature distribution extend beyond simple shifts in the mean. The authors present a framework based on quantile regression, wherein trends are estimated across percentiles. Pointwise trends from surface station observations are mapped into continuous spatial fields using thin-plate spline regression. This procedure allows for resolving spatial dependence of distributional trends, providing uncertainty estimates that account for spatial covariance and varying station density. The method is applied to seasonal near-surface temperatures between 1979 and 2014 to unambiguously assess distributional changes in the densely sampled North American region. Strong seasonal differences are found, with summer trends exhibiting significant warming throughout the domain with little distributional dependence, while the spatial distribution of spring and fall trends show a dipole structure. In contrast, the spread between the 95th and 5th percentile in winter has decreased, with trends of −0.71° and −0.85°C decade−1, respectively, for daily maximum and minimum temperature, a contraction that is statistically significant over 84% of the domain. This decrease in variability is dominated by warming of the coldest days, which has outpaced the median trend by approximately a factor of 4. Identical analyses using ERA-Interim and NCEP-2 yield consistent estimates for winter (though not for other seasons), suggesting that reanalyses can be reliably used for relating winter trends to circulation anomalies. These results are consistent with Arctic-amplified warming being strongest in winter and with the influence of synoptic-scale advection on winter temperatures. Maps for all percentiles, seasons, and datasets are provided via an online tool.

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Karen A. McKinnon
,
Alexander R. Stine
, and
Peter Huybers

Abstract

The climatological annual cycle in surface air temperature, defined by its amplitude and phase lag with respect to solar insolation, is one of the most familiar aspects of the climate system. Here, the authors identify three first-order features of the spatial structure of amplitude and phase lag and explain them using simple physical models. Amplitude and phase lag 1) are broadly consistent with a land and ocean end-member mixing model but 2) exhibit overlap between land and ocean and, despite this overlap, 3) show a systematically greater lag over ocean than land for a given amplitude. Based on previous work diagnosing relative ocean or land influence as an important control on the extratropical annual cycle, the authors use a Lagrangian trajectory model to quantify this influence as the weighted amount of time that an ensemble of air parcels has spent over ocean or land. This quantity explains 84% of the space–time variance in the extratropical annual cycle, as well as features 1 and 2. All three features can be explained using a simple energy balance model with land and ocean surfaces and an advecting atmosphere. This model explains 94% of the space–time variance of the annual cycle in an illustrative midlatitude zonal band when incorporating the results of the trajectory model. The aforementioned features of annual variability in surface air temperature thus appear to be explained by the coupling of land and ocean through mean atmospheric circulation.

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