Huai-Min Zhang

Extended Reconstructed Sea Surface Temperature Version 4 (ERSST.v4). Part I: Upgrades and Intercomparisons

AbstractThe monthly Extended Reconstructed Sea Surface Temperature (ERSST) dataset, available on global 2° × 2° grids, has been revised herein to version 4 (v4) from v3b. Major revisions include updated and substantially more complete input data from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) release 2.5; revised empirical orthogonal teleconnections (EOTs) and EOT acceptance criterion; updated sea surface temperature (SST) quality control procedures; revised SST anomaly (SSTA) evaluation methods; updated bias adjustments of ship SSTs using the Hadley Centre Nighttime Marine Air Temperature dataset version 2 (HadNMAT2); and buoy SST bias adjustment not previously made in v3b.Tests show that the impacts of the revisions to ship SST bias adjustment in ERSST.v4 are dominant among all revisions and updates. The effect is to make SST 0.1°–0.2°C cooler north of 30°S but 0.1°–0.2°C warmer south of 30°S in ERSST.v4 than in ERSST.v3b before 1940. In comparison with the Met Office SST product...

Extended Reconstructed Sea Surface Temperature Version 4 (ERSST.v4): Part II. Parametric and Structural Uncertainty Estimations

Described herein is the parametric and structural uncertainty quantification for the monthly Extended Reconstructed Sea Surface Temperature (ERSST) version 4 (v4). A Monte Carlo ensemble approach was adoptedtocharacterizeparametricuncertainty,becauseinitialexperimentsindicatetheexistenceofsignificant nonlinear interactions. Globally, the resulting ensemble exhibits a wider uncertainty range before 1900, as well as an uncertainty maximum around World War II. Changes at smaller spatial scales in many regions, or for important features such as Nino-3.4 variability, are found to be dominated by particular parameter choices. Substantial differences in parametric uncertainty estimates are found between ERSST.v4 and the independently derived Hadley Centre SST version 3 (HadSST3) product. The largest uncertainties are over the mid and high latitudes in ERSST.v4but in the tropics in HadSST3. Overall, in comparison with HadSST3, ERSST.v4 has larger parametric uncertainties at smaller spatial and shorter time scales and smaller parametric uncertainties at longer time scales, which likely reflects the different sources of uncertainty quantified in the respective parametric analyses. ERSST.v4 exhibits a stronger globally averaged warming trend than HadSST3duringtheperiodof1910‐2012,butwithasmallerparametricuncertainty.Theseglobal-meantrend estimates and their uncertainties marginally overlap. Several additional SST datasetsare usedto infer the structuraluncertainty inherent in SST estimates. For the global mean, the structural uncertainty, estimated as the spread between available SST products, is more often than not larger than the parametric uncertainty in ERSST.v4. Neither parametric nor structural uncertainties call into question that on the global-mean level and centennial time scale, SSTs have warmed notably.

Further Exploring and Quantifying Uncertainties for Extended Reconstructed Sea Surface Temperature (ERSST) Version 4 (v4)

AbstractThe uncertainty in Extended Reconstructed SST (ERSST) version 4 (v4) is reassessed based upon 1) reconstruction uncertainties and 2) an extended exploration of parametric uncertainties. The reconstruction uncertainty (Ur) results from using a truncated (130) set of empirical orthogonal teleconnection functions (EOTs), which yields an inevitable loss of information content, primarily at a local level. The Ur is assessed based upon 32 ensemble ERSST.v4 analyses with the spatially complete monthly Optimum Interpolation SST product. The parametric uncertainty (Up) results from using different parameter values in quality control, bias adjustments, and EOT definition etc. The Up is assessed using a 1000-member ensemble ERSST.v4 analysis with different combinations of plausible settings of 24 identified internal parameter values. At the scale of an individual grid box, the SST uncertainty varies between 0.3° and 0.7°C and arises from both Ur and Up. On the global scale, the SST uncertainty is substantial...

Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons

AbstractThe monthly global 2° × 2° Extended Reconstructed Sea Surface Temperature (ERSST) has been revised and updated from version 4 to version 5. This update incorporates a new release of ICOADS release 3.0 (R3.0), a decade of near-surface data from Argo floats, and a new estimate of centennial sea ice from HadISST2. A number of choices in aspects of quality control, bias adjustment, and interpolation have been substantively revised. The resulting ERSST estimates have more realistic spatiotemporal variations, better representation of high-latitude SSTs, and ship SST biases are now calculated relative to more accurate buoy measurements, while the global long-term trend remains about the same. Progressive experiments have been undertaken to highlight the effects of each change in data source and analysis technique upon the final product. The reconstructed SST is systematically decreased by 0.077°C, as the reference data source is switched from ship SST in ERSSTv4 to modern buoy SST in ERSSTv5. Furthermore...

Ranking the strongest ENSO events while incorporating SST uncertainty

The strength of El Nino-Southern Oscillation (ENSO) is often measured using a single, discrete value of the Nino index. However, this method does not consider the sea surface temperature (SST) uncertainty associated with the observations and data processing. On the basis of the Nino3.4 index and its uncertainty, we find that the strength of the three strongest ENSO events is not separable at 95% confidence level. The monthly peak SST anomalies in the most recent 2015-16 El Nino is tied with 1997-98 and 1982-83 El Nino as the strongest. The three most negative monthly Nino values occur within the 1955-56, 1973-74, and 1975-76 La Nina events, which cannot be discriminated by rank. The histograms of 1000-member ensemble analysis support the conclusion that the strength of the three strongest ENSO events is not separable. These results highlight that the ENSO ranking has to include the SST uncertainty.

A call for new approaches to quantifying biases in observations of sea-surface temperature

AbstractGlobal surface temperature changes are a fundamental expression of climate change. Recent, much-debated variations in the observed rate of surface temperature change have highlighted the importance of uncertainty in adjustments applied to sea surface temperature (SST) measurements. These adjustments are applied to compensate for systematic biases and changes in observing protocol. Better quantification of the adjustments and their uncertainties would increase confidence in estimated surface temperature change and provide higher-quality gridded SST fields for use in many applications.Bias adjustments have been based on either physical models of the observing processes or the assumption of an unchanging relationship between SST and a reference dataset, such as night marine air temperature. These approaches produce similar estimates of SST bias on the largest space and time scales, but regional differences can exceed the estimated uncertainty. We describe challenges to improving our understanding of ...

Why Did Large Differences Arise in the Sea Surface Temperature Datasets across the Tropical Pacific during 2012

AbstractDuring June–November 2012, pronounced differences in tropical Pacific sea surface temperature (SST) anomalies were observed between three widely used SST products: the extended reconstructed SST version 3b (ERSSTv3b), and the optimum interpolation SST version 2 analyses (OISST), produced weekly (OISSTwk) and daily (OISSTdy). During June–August 2012, the Nino-3.4 SST anomaly (SSTA) index was 0.2°–0.3°C lower in ERSSTv3b than in OISSTwk and OISSTdy, while it was 0.3°–0.4°C higher from September to November 2012. Such differences in the Nino-3.4 SSTA index can impact the assessment of the status of the El Nino–Southern Oscillation, which is determined using a threshold of ±0.5°C in the Nino-3.4 SSTA index.To investigate the reasons for the differences between ERSSTv3b and OISSTdy/OISSTwk, an experimental analysis (called ERSSTsat) is created that is similar to ERSSTv3b but includes satellite-derived SSTs. However, significant differences in the Nino-3.4 SSTA index remained between ERSSTsat and OISSTd...

Adequacy of the In Situ Observing System in the Satellite Era for Climate SST

Abstract A method is presented to evaluate the adequacy of the recent in situ network for climate sea surface temperature (SST) analyses using both in situ and satellite observations. Satellite observations provide superior spatiotemporal coverage, but with biases; in situ data are needed to correct the satellite biases. Recent NOAA/U.S. Navy operational Advanced Very High Resolution Radiometer (AVHRR) satellite SST biases were analyzed to extract typical bias patterns and scales. Occasional biases of 2°C were found during large volcano eruptions and near the end of the satellite instruments’ lifetime. Because future biases could not be predicted, the in situ network was designed to reduce the large biases that have occurred to a required accuracy. Simulations with different buoy density were used to examine their ability to correct the satellite biases and to define the residual bias as a potential satellite bias error (PSBE). The PSBE and buoy density (BD) relationship was found to be nearly exponential...

Bias Adjustment of AVHRR SST and Its Impacts on Two SST Analyses

AbstractSea surface temperature (SST) observations from satellite-based Advanced Very High Resolution Radiometer (AVHRR) instrument exhibit biases. Adjustments necessary for removing the AVHRR biases have been studied by progressive experiments. These experiments show that the biases are sensitive to various parameters, including the length of the input data window, the base-function empirical orthogonal teleconnections (EOTs), the ship–buoy SST adjustment, and a shift in grid system. The difference in bias adjustments due to these parameters can be as large as 0.3°–0.5°C in the tropical Pacific at the monthly time scale.The AVHRR bias adjustments were designed differently in the daily optimum interpolation SST (DOISST) and the Extended Reconstructed SST datasets that ingest AVHRR SSTs (ERSSTsat). The different AVHRR bias adjustments result in the differences in SST datasets in DOISST and ERSSTsat. Comparisons show that the SST difference between these two datasets results largely from the difference in t...