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Abstract
It is increasingly recognized that metadata can significantly improve the quality of scientific analyses and that the availability of metadata is particularly important for the study of climate variability. The International Comprehensive Ocean–Atmosphere Data Set (ICOADS) contains in situ observations frequently used in climate studies, and this paper describes the ship metadata that are available to complement ICOADS. This paper highlights the metadata available in World Meteorological Organization Publication No. 47 that include information on measurement methods and observation heights. Changing measurement methods and heights are known to be a cause of spurious change in the climate record. Here the authors focus on identifying measurement heights for air temperature and wind speed and also give information on SST measurement depths.
Abstract
It is increasingly recognized that metadata can significantly improve the quality of scientific analyses and that the availability of metadata is particularly important for the study of climate variability. The International Comprehensive Ocean–Atmosphere Data Set (ICOADS) contains in situ observations frequently used in climate studies, and this paper describes the ship metadata that are available to complement ICOADS. This paper highlights the metadata available in World Meteorological Organization Publication No. 47 that include information on measurement methods and observation heights. Changing measurement methods and heights are known to be a cause of spurious change in the climate record. Here the authors focus on identifying measurement heights for air temperature and wind speed and also give information on SST measurement depths.
Development is described of a Comprehensive Ocean-Atmosphere Data Set (COADS)—the result of a cooperative project to collect global weather observations taken near the ocean's surface since 1854, primarily from merchant ships, into a compact and easily used data set. As background, a historical overview is given of how archiving of these marine data has evolved from 1854, when systematic recording of shipboard meteorological and oceanographic observations was first established as an international activity. Input data sets used for COADS are described, as well as the processing steps used to pack input data into compact binary formats and to apply quality controls for identification of suspect weather elements and duplicate marine reports. Seventy-million unique marine reports for 1854–1979 were output from initial processing. Further processing is described, which created statistical summaries for each month of each year of the period, using 2° latitude × 2° longitude boxes. Monthly summary products are available giving 14 statistics (such as the median and the mean) for each of eight observed variables (air and sea-surface temperatures, scalar and vector wind, pressure, humidity, and cloudiness), plus 11 derived variables. Examples of known temporal, spatial, and methodological inhomogeneities in marine data, and plans for periodic updates to COADS, including an update through 1986 scheduled for completion by early 1988, are presented.
Development is described of a Comprehensive Ocean-Atmosphere Data Set (COADS)—the result of a cooperative project to collect global weather observations taken near the ocean's surface since 1854, primarily from merchant ships, into a compact and easily used data set. As background, a historical overview is given of how archiving of these marine data has evolved from 1854, when systematic recording of shipboard meteorological and oceanographic observations was first established as an international activity. Input data sets used for COADS are described, as well as the processing steps used to pack input data into compact binary formats and to apply quality controls for identification of suspect weather elements and duplicate marine reports. Seventy-million unique marine reports for 1854–1979 were output from initial processing. Further processing is described, which created statistical summaries for each month of each year of the period, using 2° latitude × 2° longitude boxes. Monthly summary products are available giving 14 statistics (such as the median and the mean) for each of eight observed variables (air and sea-surface temperatures, scalar and vector wind, pressure, humidity, and cloudiness), plus 11 derived variables. Examples of known temporal, spatial, and methodological inhomogeneities in marine data, and plans for periodic updates to COADS, including an update through 1986 scheduled for completion by early 1988, are presented.
Abstract
The 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 [the Hadley Centre Sea Surface Temperature dataset, version 3 (HadSST3)], the ship SST bias adjustment in ERSST.v4 is 0.1°–0.2°C cooler in the tropics but 0.1°–0.2°C warmer in the midlatitude oceans both before 1940 and from 1945 to 1970. Comparisons highlight differences in long-term SST trends and SSTA variations at decadal time scales among ERSST.v4, ERSST.v3b, HadSST3, and Centennial Observation-Based Estimates of SST version 2 (COBE-SST2), which is largely associated with the difference of bias adjustments in these SST products. The tests also show that, when compared with v3b, SSTAs in ERSST.v4 can substantially better represent the El Niño/La Niña behavior when observations are sparse before 1940. Comparisons indicate that SSTs in ERSST.v4 are as close to satellite-based observations as other similar SST analyses.
Abstract
The 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 [the Hadley Centre Sea Surface Temperature dataset, version 3 (HadSST3)], the ship SST bias adjustment in ERSST.v4 is 0.1°–0.2°C cooler in the tropics but 0.1°–0.2°C warmer in the midlatitude oceans both before 1940 and from 1945 to 1970. Comparisons highlight differences in long-term SST trends and SSTA variations at decadal time scales among ERSST.v4, ERSST.v3b, HadSST3, and Centennial Observation-Based Estimates of SST version 2 (COBE-SST2), which is largely associated with the difference of bias adjustments in these SST products. The tests also show that, when compared with v3b, SSTAs in ERSST.v4 can substantially better represent the El Niño/La Niña behavior when observations are sparse before 1940. Comparisons indicate that SSTs in ERSST.v4 are as close to satellite-based observations as other similar SST analyses.
Abstract
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 adopted to characterize parametric uncertainty, because initial experiments indicate the existence of significant 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 Niño-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.v4 but 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 HadSST3 during the period of 1910–2012, but with a smaller parametric uncertainty. These global-mean trend estimates and their uncertainties marginally overlap.
Several additional SST datasets are used to infer the structural uncertainty 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.
Abstract
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 adopted to characterize parametric uncertainty, because initial experiments indicate the existence of significant 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 Niño-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.v4 but 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 HadSST3 during the period of 1910–2012, but with a smaller parametric uncertainty. These global-mean trend estimates and their uncertainties marginally overlap.
Several additional SST datasets are used to infer the structural uncertainty 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.
