Monitoring Global Monthly Mean Surface Temperatures

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  • 1 National Center for Atmospheric Research, Boulder, Colorado
  • | 2 Atmospheric Science Program, University of Alabama in Huntsville, Huntsville, Alabama
  • | 3 National Center for Atmospheric Research, Boulder, Colorado
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Abstract

An assessment is made of how well the monthly mean surface temperatures for the decade of the 1980s are known. The sources of noise in the data, the numbers of observations, and the spatial coverage are appraised for comparison with the climate signal, and different analyzed results are compared to see how reproducible they are. The data are further evaluated by comparing anomalies of near-global monthly mean surface temperatures with those of global satellite channel 2 microwave sounding unit (MSU) temperatures for 144 months from 1979 to 1990. Very distinctive patterns are seen in the correlation coefficients, which range from high (>0.8) over the extratropical continents of the Northern Hemisphere, to moderate (∼0,5) over tropical and subtropical land areas, to very low over the southern oceans and tropical western Pacific. The physical difference between the two temperature measurements is one factor in these patterns. The correlation coefficient is a measure of the signal-to-noise ratio, and largest values are found where the climate signal is largest, but the spatial variation in the inherent noise in the surface observations over the oceans is the other major factor in accounting for the pattern.

Over the oceans, sea surface temperatures (SSTS) are used in the surface dataset in place of surface air temperature and the Comprehensive Ocean-Atmosphere Data Set (COADS) has been used to show that 80% of the monthly mean air temperature variance is accounted for in regions of good data coverage. A detailed analysis of the sources of errors in in situ SSTs and an overall estimate of the noise are obtained from the COADS by assessing the variability within 2° longitude by 2° latitude boxes within each month for 1979. In regions of small spatial gradient of mean SST, individual SST measurements are representative of the monthly mean in a 2° box to within a standard error of 1.0°C in the tropics and 1.2° to 1.4°C in the extratropics. The standard error is larger in the North Pacific than in the North Atlantic and much larger in regions of strong SST gradient, such as within the vicinity of the Gulf Stream, because both within-month temporal variability and within-2° box spatial variability are enhanced. The total standard error of the monthly mean in each box is reduced approximately by the square root of the number of observations available. The overall noise in SSTs ranges from less than 0.1°C over the North Atlantic to over 0.5deg;C over the oceans south of about 35°S. Greater daily variability in surface marine air temperatures than in SSTs means that two to three times as many observations are needed per month to reduce the noise in the monthly mean air temperature to the same level as for SST. Tests of the reproducibility of SSTs in analyses from the U.K. Meteorological Office (UKMO) and the U.S. Climate Analysis Center (CAC) and from COADS reveal monthly anomaly correlations on a 5° grid exceeding 0.9 over the northern oceans but less than 0.6 in the central tropical Pacific and south of about 35°S. Root-mean-square differences between CAC and UKMO monthly SST anomalies exceed 0.6°C in the regions where the correlation is lower than about 0.6.

With the marked exception of the eastern tropical Pacific, where the large El Niño signal is easily detected, there are insufficient numbers of SST observations to reliably define SST or surface air temperature monthly mean anomalies over most of the oceans south of about 10°N. The use of seasons rather than months can improve the signal-to-noise ratio if careful treatment of the annual cycle is included. For seasonal means, SST anomalies cannot be reliably defined south of 20°S in the eastern Pacific and south of ∼35°S elsewhere except near New Zealand. In light of the noise estimates and the much fewer numbers of observations in the past, difficulties in establishing temperatures from the historical record are discussed.

Abstract

An assessment is made of how well the monthly mean surface temperatures for the decade of the 1980s are known. The sources of noise in the data, the numbers of observations, and the spatial coverage are appraised for comparison with the climate signal, and different analyzed results are compared to see how reproducible they are. The data are further evaluated by comparing anomalies of near-global monthly mean surface temperatures with those of global satellite channel 2 microwave sounding unit (MSU) temperatures for 144 months from 1979 to 1990. Very distinctive patterns are seen in the correlation coefficients, which range from high (>0.8) over the extratropical continents of the Northern Hemisphere, to moderate (∼0,5) over tropical and subtropical land areas, to very low over the southern oceans and tropical western Pacific. The physical difference between the two temperature measurements is one factor in these patterns. The correlation coefficient is a measure of the signal-to-noise ratio, and largest values are found where the climate signal is largest, but the spatial variation in the inherent noise in the surface observations over the oceans is the other major factor in accounting for the pattern.

Over the oceans, sea surface temperatures (SSTS) are used in the surface dataset in place of surface air temperature and the Comprehensive Ocean-Atmosphere Data Set (COADS) has been used to show that 80% of the monthly mean air temperature variance is accounted for in regions of good data coverage. A detailed analysis of the sources of errors in in situ SSTs and an overall estimate of the noise are obtained from the COADS by assessing the variability within 2° longitude by 2° latitude boxes within each month for 1979. In regions of small spatial gradient of mean SST, individual SST measurements are representative of the monthly mean in a 2° box to within a standard error of 1.0°C in the tropics and 1.2° to 1.4°C in the extratropics. The standard error is larger in the North Pacific than in the North Atlantic and much larger in regions of strong SST gradient, such as within the vicinity of the Gulf Stream, because both within-month temporal variability and within-2° box spatial variability are enhanced. The total standard error of the monthly mean in each box is reduced approximately by the square root of the number of observations available. The overall noise in SSTs ranges from less than 0.1°C over the North Atlantic to over 0.5deg;C over the oceans south of about 35°S. Greater daily variability in surface marine air temperatures than in SSTs means that two to three times as many observations are needed per month to reduce the noise in the monthly mean air temperature to the same level as for SST. Tests of the reproducibility of SSTs in analyses from the U.K. Meteorological Office (UKMO) and the U.S. Climate Analysis Center (CAC) and from COADS reveal monthly anomaly correlations on a 5° grid exceeding 0.9 over the northern oceans but less than 0.6 in the central tropical Pacific and south of about 35°S. Root-mean-square differences between CAC and UKMO monthly SST anomalies exceed 0.6°C in the regions where the correlation is lower than about 0.6.

With the marked exception of the eastern tropical Pacific, where the large El Niño signal is easily detected, there are insufficient numbers of SST observations to reliably define SST or surface air temperature monthly mean anomalies over most of the oceans south of about 10°N. The use of seasons rather than months can improve the signal-to-noise ratio if careful treatment of the annual cycle is included. For seasonal means, SST anomalies cannot be reliably defined south of 20°S in the eastern Pacific and south of ∼35°S elsewhere except near New Zealand. In light of the noise estimates and the much fewer numbers of observations in the past, difficulties in establishing temperatures from the historical record are discussed.

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