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Reanalyses-Based Tropospheric Temperature Estimates: Uncertainties in the Context of Global Climate Change Detection

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  • 1 Climate Prediction Center, NCEP/NWS/NOAA, Camp Springs, Maryland
  • | 2 International Research Institute for Climate Prediction, Lamont–Doherty Earth Observatory of Columbia University, Palisades, New York
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Abstract

Uncertainties in estimates of tropospheric mean temperature were investigated in the context of climate change detection through comparisons of the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) 40-yr reanalysis (1958–97), the National Aeronautics and Space Administration Data Assimilation Office (NASA/DAO) 14-yr reanalysis (1980–93), the European Centre for Medium-Range Weather Forecasts Reanalysis Project (ERA) 15-yr reanalysis (1979–94), and the satellite microwave sounding unit channel 2 (MSU Ch2) (1979–97) temperature data. The maximum overlap period for comparison among these datasets is the 14 full years January 1980 to December 1993. This study documents similar shifts in the relative bias between the MSU Ch2 and the ERA and the NCEP–NCAR reanalyses in the 1991–97 period suggesting changes in the satellite analysis. However, the intercomparisons were not able to rule out the changes in the reanalysis systems and/or the input data on which the reanalyses are based as prime factors for the changes in the relative bias between the MSU and ERA and NCEP–NCAR reanalyses.

These temporal changes in the relative bias among the reanalyses suggest their limitations for global change studies. Nonetheless, the analysis also shows that the pattern correlations (r) between the MSU Ch2 monthly mean fields and each of the reanalyses are very high, r > 0.96, and remain relatively high for the anomaly fields, r > 0.8, generally >0.9. This result suggests that reanalysis may be used for comparisons to numerical model–generated forecast fields (from GCM simulation runs) and in conjunction with “fingerprint” techniques to identify climate change.

In comparisons of the simple linear trends present in each dataset for the 1980–90 period, each of the reanalyses had spatial patterns similar to MSU Ch2 except that the NCEP–NCAR reanalysis showed smaller “positive” (warming) trends in comparison with the MSU while the ERA reanalysis showed larger positive trends. The NASA/DAO reanalysis showed a mixed pattern. Many regions of the globe are identified that showed consistent warming/cooling patterns among the major reanalyses and MSU, even though there were disagreements in the exact magnitude among the analyses. The spatial patterns of linear trends changed, however, with the addition of three years of data to extend the trend analysis to the 1980–93 period. This result suggests that such simple linear trend analyses are very sensitive to the temporal span in these relatively short datasets and thus are of limited usefulness in the context of climate change detection except, however, when the signal is large and shows consistency among all datasets.

The long record (1958–96) of seasonal mean 2-m temperature anomalies from NCEP–NCAR reanalysis is well correlated with gridded analyses of station-based observed surface temperature, with correlations between 0.65 and 0.85. It is argued that these correlations might suggest an upper limit to the magnitudes of the pattern correlations that might be obtained by correlating observed surface temperature analyses with those from multiyear GCM simulation runs made in the context of fingerprint climate change detection.

Corresponding author address: Dr. Muthuvel Chelliah, Climate Prediction Center, NCEP/NWS/NOAA, 5200 Auth Road, Camp Springs, MD 20746.

Email: muthuvel.chelliah@ncep.noaa.gov

Abstract

Uncertainties in estimates of tropospheric mean temperature were investigated in the context of climate change detection through comparisons of the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) 40-yr reanalysis (1958–97), the National Aeronautics and Space Administration Data Assimilation Office (NASA/DAO) 14-yr reanalysis (1980–93), the European Centre for Medium-Range Weather Forecasts Reanalysis Project (ERA) 15-yr reanalysis (1979–94), and the satellite microwave sounding unit channel 2 (MSU Ch2) (1979–97) temperature data. The maximum overlap period for comparison among these datasets is the 14 full years January 1980 to December 1993. This study documents similar shifts in the relative bias between the MSU Ch2 and the ERA and the NCEP–NCAR reanalyses in the 1991–97 period suggesting changes in the satellite analysis. However, the intercomparisons were not able to rule out the changes in the reanalysis systems and/or the input data on which the reanalyses are based as prime factors for the changes in the relative bias between the MSU and ERA and NCEP–NCAR reanalyses.

These temporal changes in the relative bias among the reanalyses suggest their limitations for global change studies. Nonetheless, the analysis also shows that the pattern correlations (r) between the MSU Ch2 monthly mean fields and each of the reanalyses are very high, r > 0.96, and remain relatively high for the anomaly fields, r > 0.8, generally >0.9. This result suggests that reanalysis may be used for comparisons to numerical model–generated forecast fields (from GCM simulation runs) and in conjunction with “fingerprint” techniques to identify climate change.

In comparisons of the simple linear trends present in each dataset for the 1980–90 period, each of the reanalyses had spatial patterns similar to MSU Ch2 except that the NCEP–NCAR reanalysis showed smaller “positive” (warming) trends in comparison with the MSU while the ERA reanalysis showed larger positive trends. The NASA/DAO reanalysis showed a mixed pattern. Many regions of the globe are identified that showed consistent warming/cooling patterns among the major reanalyses and MSU, even though there were disagreements in the exact magnitude among the analyses. The spatial patterns of linear trends changed, however, with the addition of three years of data to extend the trend analysis to the 1980–93 period. This result suggests that such simple linear trend analyses are very sensitive to the temporal span in these relatively short datasets and thus are of limited usefulness in the context of climate change detection except, however, when the signal is large and shows consistency among all datasets.

The long record (1958–96) of seasonal mean 2-m temperature anomalies from NCEP–NCAR reanalysis is well correlated with gridded analyses of station-based observed surface temperature, with correlations between 0.65 and 0.85. It is argued that these correlations might suggest an upper limit to the magnitudes of the pattern correlations that might be obtained by correlating observed surface temperature analyses with those from multiyear GCM simulation runs made in the context of fingerprint climate change detection.

Corresponding author address: Dr. Muthuvel Chelliah, Climate Prediction Center, NCEP/NWS/NOAA, 5200 Auth Road, Camp Springs, MD 20746.

Email: muthuvel.chelliah@ncep.noaa.gov

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