Measurement Requirements for Climate Monitoring of Upper-Air Temperature Derived from Reanalysis Data

Dian J. Seidel NOAA/Air Resources Laboratory, Silver Spring, Maryland

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Melissa Free NOAA/Air Resources Laboratory, Silver Spring, Maryland

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Abstract

Using a reanalysis of the climate of the past half century as a model of temperature variations over the next half century, tests of various data collection protocols are made to develop recommendations for observing system requirements for monitoring upper-air temperature. The analysis focuses on accurately estimating monthly climatic data (specifically, monthly average temperature and its standard deviation) and multidecadal trends in monthly temperatures at specified locations, from the surface to 30 hPa. It does not address upper-air network size or station location issues.

The effects of reducing the precision of temperature data, incomplete sampling of the diurnal cycle, incomplete sampling of the days of the month, imperfect long-term stability of the observations, and changes in observation schedule are assessed. To ensure accurate monthly climate statistics, observations with at least 0.5-K precision, made at least twice daily, at least once every two or three days are sufficient. Using these same criteria, and maintaining long-term measurement stability to within 0.25 (0.1) K, for periods of 20 to 50 yr, errors in trend estimates can be avoided in at least 90% (95%) of cases. In practical terms, this requires no more than one intervention (e.g., instrument change) over the period of record, and its effect must be to change the measurement bias by no more than 0.25 (0.1) K. The effect of the first intervention dominates the effects of subsequent, uncorrelated interventions. Changes in observation schedule also affect trend estimates. Reducing the number of observations per day, or changing the timing of a single observation per day, has a greater potential to produce errors in trends than reducing the number of days per month on which observations are made.

These findings depend on the validity of using reanalysis data to approximate the statistical nature of future climate variations, and on the statistical tests employed. However, the results are based on conservative assumptions, so that adopting observing system requirements based on this analysis should result in a data archive that will meet climate monitoring needs over the next 50 yr.

Corresponding author address: Dr. Dian J. Seidel, NOAA/Air Resources Laboratory (R/ARL), 1315 East–West Highway, Silver Spring, MD 20910. Email: Dian.Seidel@noaa.gov

Abstract

Using a reanalysis of the climate of the past half century as a model of temperature variations over the next half century, tests of various data collection protocols are made to develop recommendations for observing system requirements for monitoring upper-air temperature. The analysis focuses on accurately estimating monthly climatic data (specifically, monthly average temperature and its standard deviation) and multidecadal trends in monthly temperatures at specified locations, from the surface to 30 hPa. It does not address upper-air network size or station location issues.

The effects of reducing the precision of temperature data, incomplete sampling of the diurnal cycle, incomplete sampling of the days of the month, imperfect long-term stability of the observations, and changes in observation schedule are assessed. To ensure accurate monthly climate statistics, observations with at least 0.5-K precision, made at least twice daily, at least once every two or three days are sufficient. Using these same criteria, and maintaining long-term measurement stability to within 0.25 (0.1) K, for periods of 20 to 50 yr, errors in trend estimates can be avoided in at least 90% (95%) of cases. In practical terms, this requires no more than one intervention (e.g., instrument change) over the period of record, and its effect must be to change the measurement bias by no more than 0.25 (0.1) K. The effect of the first intervention dominates the effects of subsequent, uncorrelated interventions. Changes in observation schedule also affect trend estimates. Reducing the number of observations per day, or changing the timing of a single observation per day, has a greater potential to produce errors in trends than reducing the number of days per month on which observations are made.

These findings depend on the validity of using reanalysis data to approximate the statistical nature of future climate variations, and on the statistical tests employed. However, the results are based on conservative assumptions, so that adopting observing system requirements based on this analysis should result in a data archive that will meet climate monitoring needs over the next 50 yr.

Corresponding author address: Dr. Dian J. Seidel, NOAA/Air Resources Laboratory (R/ARL), 1315 East–West Highway, Silver Spring, MD 20910. Email: Dian.Seidel@noaa.gov

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