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1. DCENT ‐I: A Globally Infilled Extension of the Dynamically Consistent ENsemble of Temperature Dataset

   https://doi.org/10.1002/gdj3.70054  

   Chan, Duo; Chan, Steven C; Siddons, Joseph T; Cable, Archie; Faulkner, Agnieszka; Cornes, Richard C; Kent, Elizabeth C; Gebbie, Geoffrey; Huybers, Peter (April 2026, Geoscience Data Journal)

   ABSTRACT A spatially infilled Dynamically Consistent Ensemble of surface Temperature (DCENT‐I) has been created by infilling land‐air and sea‐surface temperatures from DCENT using ordinary kriging with anisotropic and heterogeneous kernels. By incorporating air temperature anomalies over sea‐ice areas, DCENT‐I provides spatially complete monthly temperature fields at 5° resolution from 1850 to the present (currently the end of 2024) as a 200‐member ensemble. Uncertainty estimates that account for the need to infill for missing observations are made using a Multivariate Gaussian Process, and these are consistent with estimates derived from masking climate model simulations. The use of anisotropic and heterogeneous kernels leads to a reconstruction of El Niño variability whose spatial pattern and temporal variance are generally consistent throughout the record. As compared with taking the unfilled average, infilling increases the global mean surface temperature (GMST) warming estimate over 2005–2024 relative to a 1850–1900 baseline from 1.09 [0.96, 1.18] (95% range across members) to 1.17 [1.05, 1.26]°C, largely because of infilling in rapidly warming high‐latitude regions. Compared with HadCRUT5, GISTEMP v4, NOAA Global Temp v6, and Berkeley Earth, DCENT‐I shows a steadier and slightly faster GMST warming trend, reflecting the bias‐adjustments inherited from DCENT. 

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2. An Improved Ensemble of Land Surface Air Temperatures Since 1880 Using Revised Pair-Wise Homogenization Algorithms Accounting for Autocorrelation

   https://doi.org/10.1175/JCLI-D-23-0338.1  

   Chan, Duo; Gebbie, Geoffrey; Huybers, Peter (April 2024, Journal of Climate)

   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 PHA₀by 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 PHA₁and PHA₂both similarly outperform PHA₀in 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 StatementAccurately 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⁻¹greater 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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3. Re‐Evaluating Historical Sea Surface Temperature Data Sets: Insights From the Diurnal Cycle, Coral Proxy Data, and Radiative Forcing

   https://doi.org/10.1029/2025GL116615  

   Chan, Duo; Gebbie, Geoffrey; Huybers, Peter (July 2025, Geophysical Research Letters)

   Abstract Discrepancies in historical global mean surface temperature (GMST) estimates largely stem from differences in bias corrections applied to sea surface temperature (SST) records. Here, using the amplitude of the diurnal cycle in SST, we provide evidence that wooden‐to‐canvas bucket transitions were mostly complete by the early 1900s, earlier than commonly assumed by two decades, resulting in strong early 20th century cold biases. We then use this diurnal evidence, together with coral O and Sr/Ca proxies and expected GMST responses to external radiative forcing, to evaluate four different SST estimates: HadSST4, ERSST5, COBESST2, and DCSST. Of these, DCSST, developed by our team through adjustment of SSTs to match coastal land surface air temperatures, shows the closest overall agreement with all three lines of evidence. DCSST features a larger and steadier warming since the 1900s relative to the other estimates and indicates somewhat higher transient climate sensitivity and smaller decadal variability. 

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4. The influence of internal variability on Earth's energy balance framework and implications for estimating climate sensitivity

   https://doi.org/10.5194/acp-18-5147-2018  

   Dessler, Andrew E.; Mauritsen, Thorsten; Stevens, Bjorn (January 2018, Atmospheric Chemistry and Physics)

   Abstract. Our climate is constrained by the balance between solar energy absorbed by the Earth and terrestrial energy radiated tospace. This energy balance has been widely used to infer equilibrium climate sensitivity (ECS) from observations of20th-century warming. Such estimates yield lower values than other methods, and these have been influential in pushing downthe consensus ECS range in recent assessments. Here we test the method using a 100-member ensemble of the Max Planck Institute Earth System Model(MPI-ESM1.1) simulations of the period 1850–2005 with known forcing. We calculate ECS in each ensemble member usingenergy balance, yielding values ranging from 2.1 to 3.9K. The spread in the ensemble is related to the centralassumption in the energy budget framework: that global average surface temperature anomalies are indicative of anomaliesin outgoing energy (either of terrestrial origin or reflected solar energy). We find thatthis assumption is not well supportedover the historical temperature record in the model ensemble or more recent satellite observations. We find that framingenergy balance in terms of 500hPa tropical temperature better describes the planet's energy balance. 

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5. Monthly Sea-Surface Temperature, Sea Ice, and Sea-Level Pressure over 1850–2023 from Coupled Data Assimilation

   https://doi.org/10.1175/JCLI-D-25-0021.1  

   Cooper, Vincent T; Hakim, Gregory J; Armour, Kyle C (July 2025, Journal of Climate)

   Abstract Historical observations of Earth’s climate underpin our knowledge and predictions of climate variability and change. However, the observations are incomplete and uncertain, and existing datasets based on these observations typically do not assimilate observations simultaneously across different components of the climate system, yielding inconsistencies that limit understanding of coupled climate dynamics. Here we use coupled data assimilation, which synthesizes observational and dynamical constraints across all climate fields simultaneously, to reconstruct globally resolved sea-surface temperature (SST), near-surface air temperature (T), sea-level pressure (SLP), and sea-ice concentration (SIC), over 1850–2023. We use a Kalman filter and forecasts from an efficient emulator (Linear Inverse Model; LIM) to assimilate observations of SST, land T, marine SLP, and satellite-era SIC. We account for model error by training LIMs on eight CMIP6 models, and we use the LIMs to generate eight independent reanalyses with 200 ensemble members, yielding 1600 total members. Key findings in the Tropics include post-1980 trends in the Walker circulation that are consistent with past variability, whereas the tropical SST contrast (the difference between warmer and colder SSTs) shows a distinct strengthening since 1975. ENSO amplitude exhibits substantial low-frequency variability and a local maximum in variance over 1875–1910. In polar regions, we find a muted cooling trend in the Southern Ocean post-1980 and substantial uncertainty. Changes in Antarctic sea ice are relatively small between 1850 and 2000, while Arctic sea ice declines by 0.5±0.1 (1σ) million km²during the 1920s. 

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