1,721,000 research outputs found
Correcting Observational Biases in Sea-Surface Temperature Observations Removes Anomalous Warmth during World War II
No full textGridded monthly Sea-SurfaceTemperature estimates (R1--R5) as in Chan and Huybers (2021, Journal of Climate). Also provided are key results required to reproduce figures and tables in the paper
An improved ensemble of land-surface air temperatures since 1880 using revised pair-wise homogenization algorithms accounting for autocorrelation.
No full textAn 100-member of homogenized monthly land station temperatures from 1880--2023 (Chan et al., 2024).
To build this dataset, we apply two automated algorithms (Chan et al., 2024), which accounts for autocorrelation in temperature series, to raw station temperature records compiled under monthly Global Historical Climate Network version 4 (GHCNmV4). The first algorithm, which constitute the 50 members of the ensemble, uses improved standard homogenisation test for breakpoint detection. And the second algorithm, which contribute to the other 50 members, employs a technique called penalised likelihood. Both algorithms remove discontinuities in temperatures arising from changes in measurement approaches or environments.
The spread across ensemble members characterizes uncertainties associated with using different combinations of the algorithmic parameters, as well as errors in the magnitude estimate of individual discontinuities
Combining statistical, physical, and historical evidence to improve historical sea surface temperature records
No full textReconstructing past sea-surface temperatures (SSTs) from historical measurements containing more than 100 million ship-based observations taken by over 500,000 ships from more than 150 countries using a variety of methodologies creates a wide range of historical, scientific, and statistical challenges. The reconstruction of historical SSTs for studying climate change is particularly challenging because SST measurements are uncertain and contain systematic biases of order 0.1°C to 1°C—these systematic biases are in the range of the historical global warming signal of approximately 1°C. The biases are complicated and have generally been addressed using simplified corrections. In this review, I introduce a history of SST observations, review a statistical method developed for quantifying SST biases, and illustrate scientific insights obtained from adjusted SSTs. This article also documents the scientific journey of my Ph.D. work. As a result, I report personal stories on both successes, difficulties, and setbacks along the way. The statistical method for correcting SSTs (i.e., a linear-mixed-effect intercomparison framework) depends on identifying systematic offsets between intercomparable groups of SST observations. Combining estimated offsets with physical and historical evidence has allowed for correcting discrepancies associated with SSTs, including the North Atlantic warming twice as fast as the North Pacific in the early 20th century and anomalously warm SSTs during World War II. Corrections also permit better hindcasting of Atlantic hurricanes. I conclude with some discussion on how the SST records might be further improved. Given the importance of SSTs for understanding historical changes in climate, I hope that this review can help others appreciate challenges that are present and spark some interest and ideas for further improvement
Significant anthropogenic-induced changes of climate classes since 1950
No full textAnthropogenic forcings have contributed to global and regional warming in the last few decades and likely affected terrestrial precipitation. Here we examine changes in major Köppen climate classes from gridded observed data and their uncertainties due to internal climate variability using control simulations from Coupled Model Intercomparison Project 5 (CMIP5). About 5.7% of the global total land area has shifted toward warmer and drier climate types from 1950–2010 and significant changes include expansion of arid and high-latitude continental climate zones, shrinkage in polar and midlatitude continental climates, poleward shifts in temperate, continental and polar climates and increasing average elevation of tropical and polar climates. Using CMIP5 multi-model averaged historical simulations forced by observed anthropogenic and natural, or natural only, forcing components, we find that these changes of climate types since 1950 cannot be explained as natural variations but are driven by anthropogenic factors
Correcting observational biases in sea surface temperature observations removes anomalous warmth during World War II
No full textMost historical sea surface temperature (SST) estimates indicate warmer World War II SSTs than expected from forcing and internal climate variability. If real, this World War II warm anomaly (WW2WA) has important implications for decadal variability, but the WW2WA may also arise from incomplete corrections of biases associated with bucket and engine room intake (ERI) measurements. To better assess the origins of the WW2WA, we develop five different historical SST estimates (reconstructions R1–R5). Using uncorrected SST measurements from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) version 3.0 (R1) gives a WW2WA of 0.41°C. In contrast, using only buckets (R2) or ERI observations (R3) gives WW2WAs of 0.18° and 0.08°C, respectively, implying that uncorrected biases are the primary source of the WW2WA. We then use an extended linear-mixed-effect method to quantify systematic differences between subsets of SSTs and develop groupwise SST adjustments based on differences between pairs of nearby SST measurements. Using all measurements after applying groupwise adjustments (R4) gives a WW2WA of 0.13°C [95% confidence interval (c.i.): 0.01°–0.26°C] and indicates that U.S. and U.K. naval observations are the primary cause of the WW2WA. Finally, nighttime bucket SSTs are found to be warmer than their daytime counterparts during WW2, prompting a daytime-only reconstruction using groupwise adjustments (R5) that has a WW2WA of 0.09°C (95% c.i.: −0.01° to 0.18°C). R5 is consistent with the range of internal variability found in either the CMIP5 (95% c.i.: −0.10° to 0.10°C) or CMIP6 ensembles (95% c.i.: −0.11° to 0.10°C). These results support the hypothesis that the WW2WA is an artifact of observational biases, although further data and metadata analyses will be important for confirmation
Systematic differences in bucket sea surface temperatures caused by misclassification of engine room intake measurements
No full textDifferences in sea surface temperature (SST) biases among groups of bucket measurements in the International Comprehensive Ocean–Atmosphere Dataset, version 3.0 (ICOADS3.0), were recently identified that introduce offsets of as much as 1°C and have first-order implications for regional temperature trends. In this study, the origin of these groupwise offsets is explored through covariation between offsets and diurnal cycle amplitudes. Examination of an extended bucket model leads to expectations for offsets and amplitudes to covary in either sign, whereas misclassified engine room intake (ERI) temperatures invariably lead to negative covariance on account of ERI measurements being warmer and having a smaller diurnal amplitude. An analysis of ICOADS3.0 SST measurements that are inferred to come from buckets indicates that offsets after the 1930s primarily result from the misclassification of ERI measurements in points of five lines of evidence. 1) Prior to when ERI measurements become available in the 1930s, offset–amplitude covariance is weak and generally positive, whereas covariance is stronger and generally negative subsequently. 2) The introduction of ERI measurements in the 1930s is accompanied by a wider range of offsets and diurnal amplitudes across groups, with 3) approximately 20% of estimated diurnal amplitudes being significantly smaller than buoy and drifter observations. 4) Regressions of offsets versus amplitudes intersect independently determined end-member values of ERI measurements. 5) Offset-amplitude slopes become less negative across all regions and seasons between 1960 and 1980, when ERI temperatures were independently determined to become less warmly biased. These results highlight the importance of accurately determining measurement procedures for bias corrections and reducing uncertainty in historical SST estimates
Systematic differences in bucket sea surface temperature measurements amongst nations identified using a linear-mixed-effect method
No full textThe International Comprehensive Ocean–Atmosphere Dataset (ICOADS) is a cornerstone for estimating changes in sea surface temperatures (SST) over the instrumental era. Interest in determining SST changes to within 0.1°C makes detecting systematic offsets within ICOADS important. Previous studies have corrected for offsets among engine room intake, buoy, and wooden and canvas bucket measurements, as well as noted discrepancies among various other groupings of data. In this study, a systematic examination of differences in collocated bucket SST measurements from ICOADS3.0 is undertaken using a linear-mixed-effect model according to nations and more-resolved groupings. Six nations and a grouping for which nation metadata are missing, referred to as “deck 156,” together contribute 91% of all bucket measurements and have systematic offsets among one another of as much as 0.22°C. Measurements from the Netherlands and deck 156 are colder than the global average by −0.10° and −0.13°C, respectively, both at p < 0.01, whereas Russian measurements are offset warm by 0.10°C at p < 0.1. Furthermore, of the 31 nations whose measurements are present in more than one grouping of data (i.e., deck), 14 contain decks that show significant offsets at p < 0.1, including all major collecting nations. Results are found to be robust to assumptions regarding the independence and distribution of errors as well as to influences from the diurnal cycle and spatially heterogeneous noise variance. Correction for systematic offsets among these groupings should improve the accuracy of estimated SSTs and their trends
Attributing observed SST trends and sub-continental land warming to Anthropogenic dorcing during 1979 to 2005
No full textAttribution studies conclude that it is extremely likely that most observed global- and continental-scale surface air temperature (SAT) warming since 1950 was caused by anthropogenic forcing, but some difficulties and uncertainties remain in attribution of warming in subcontinental regions and at time scales less than 50 years. This study uses global observations and CMIP5 simulations with various forcings, covering 1979–2005, and control runs to develop confidence intervals, to attribute regional trends of SAT and sea surface temperature (SST) to natural and anthropogenic causes.Observations show warming, significantly different from natural variations at the 95% confidence level, over one-third of all grid boxes, and averaged over 15 of 21 subcontinental regions and 6 of 10 ocean basins. Coupled simulations forced with all forcing factors, or greenhouse gases only, reproduce observed SST and SAT patterns. Uncoupled AMIP-like atmosphere-only (prescribed SST and atmospheric radiative forcing) simulations reproduce observed SAT patterns. All of these simulations produce consistent net downward longwave radiation patterns. Simulations with natural-only forcing simulate weak warming. Anthropogenic forcing effects are clearly detectable at the 5% significance level at global, hemispheric, and tropical scales and in nine ocean basins and 15 of 21 subcontinental land regions. Attribution results indicate that ocean warming during 1979–2005 for the globe and individual basins is well represented in the CMIP5 multimodel ensemble mean historical simulations. While land warming may occur as an indirect response to oceanic warming, increasing greenhouse gas concentrations tend to be the ultimate source of land warming in most subcontinental regions during 1979–2005
Data and replication code for 'Global and Regional Discrepancies between Early 20th Century Coastal Air and Sea-Surface Temperature Detected by a Coupled Energy-Balance Analysis'
No full textData and code to reproduce the conversion of coastal station air temperature into near-coast sea-surface temperatures, as documented in Chan et al. (JClim, 2022).
In addition to intermediate and final output of our analysis, we also provide the version of the raw land and ocean temperatures we use, which may be different from what is currently available from the data providers due to random numbers used in data generating algorithms, to guarantee the reproducibility of our results.
After downloading the package, please refer to "Code/Readme.txt" to get started
Correcting datasets leads to more homogeneous early 20th century sea surface warming
No full textData and supporting data associated with the paper "Correcting datasets leads to more homogeneous early 20th century sea surface warming" by Duo Chan, Elizabeth C. Kent, David I. Berry, and Peter Huybers
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