On the evaluation of temperature trends in the tropical troposphere
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A series of model experiments with the coupled Max-Planck-Institute ECHAM5/OM climate model have been investigated and compared with microwave measurements from the Microwave Sounding Unit (MSU) and re-analysis data for the period 1979–2008. The evaluation is carried out by computing the Temperature in the Lower Troposphere (TLT) and Temperature in the Middle Troposphere (TMT) using the MSU weights from both University of Alabama (UAH) and Remote Sensing Systems (RSS) and restricting the study to primarily the tropical oceans. When forced by analysed sea surface temperature the model reproduces accurately the time-evolution of the mean outgoing tropospheric microwave radiation especially over tropical oceans but with a minor bias towards higher temperatures in the upper troposphere. The latest reanalyses data from the 25 year Japanese re-analysis (JRA25) and European Center for Medium Range Weather Forecasts Interim Reanalysis are in very close agreement with the time-evolution of the MSU data with a correlation of 0.98 and 0.96, respectively. The re-analysis trends are similar to the trends obtained from UAH but smaller than the trends from RSS. Comparison of TLT, computed from observations from UAH and RSS, with Sea Surface Temperature indicates that RSS has a warm bias after 1993. In order to identify the significance of the tropospheric linear temperature trends we determined the natural variability of 30-year trends from a 500 year control integration of the coupled ECHAM5 model. The model exhibits natural unforced variations of the 30 year tropospheric trend that vary within ±0.2 K/decade for the tropical oceans. This general result is supported by similar results from the Geophysical Fluid Dynamics Laboratory (GFDL) coupled climate model. Present MSU observations from UAH for the period 1979–2008 are well within this range but RSS is close to the upper positive limit of this variability. We have also compared the trend of the vertical lapse rate over the tropical oceans assuming that the difference between TLT and TMT is an approximate measure of the lapse rate. The TLT–TMT trend is larger in both the measurements and in the JRA25 than in the model runs by 0.04–0.06 K/decade. Furthermore, a calculation of all 30 year TLT–TMT trends of the unforced 500-year integration vary between ±0.03 K/decade suggesting that the models have a minor systematic warm bias in the upper troposphere.
KeywordsTropospheric temperature Trend MSU Reanalyses
The authors would like to thank both Dr. C. Mears and Dr. J. Christy for providing the MSU weights. The JRA25 data where produced from the JRA25 long-term reanalysis cooperative research project carried out by the Japan Meteorological Agency (JMA) and the Central Research Institute of Electric Power Industry (CRIEPI). The ERA-Interim reanalysis data was obtained from the ECMWF data server. We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, US Department of Energy. We also would like to thank one of the reviewers for constructive comments on the manuscript.
- Bengtsson L, Hodges KI, Hagemann S (2004b) Sensitivity of large scale atmospheric analyses to humidity observations and its impact on the global water cycle and tropical and extratropical weather systems. Tellus 56A:202–217Google Scholar
- Dee DP, Uppala S (2009) Variational bias correction of satellite radiance data in the ERA-Interim reanalysis. Q J R Meteorol Soc. doi: 10.1002/qj.493
- Gibson JK, Kallberg P, Uppala S, Hernandez A, Nomura A, Serrano E (1997) The ECMWF re-analysis (ERA) 1. ERA description. ECMWF reanalysis project report series 1, ECMWF, (Available from the European centre for Medium-range weather forecasts, reading, UK), pp 71Google Scholar
- Hegerl GC, Zwiers FW, Braconnot P, Gillett NP, Luo Y, Marengo Orsini JA, Nicholls N, Penner JE, Stott PA (2007) Understanding and attributing climate change. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis. Contribution of Working Group 1 to the Fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
- Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A, Reynolds R, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo K, Ropelewski C, Wang J, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteor Soc 77:437–471CrossRefGoogle Scholar
- Karl TR, Hassol SJ, Miller CD, Murray WL (eds) (2006) Temperature trends in the lower atmosphere: steps for understanding and reconciling differences. A report by the US Climate Change Science Program and the Subcommittee on Global Change Research. National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC; p 164Google Scholar
- Lanzante JR, Peterson TC, Wentz FJ, Vinnikov KY (2006) What do observations indicate about the change of temperatures in the atmosphere and at the surface since the advent of measuring temperatures vertically? In: Karl TR, Hassol SJ, Miller CD, Murray WL (eds) Temperature trends in the lower atmosphere: steps for understanding and reconciling differences. A report by the US Climate Change Science Program and the Subcommittee on Global Change Research, Washington DCGoogle Scholar
- Mears CA, Wentz FJ (2009) Construction of the remote sensing systems V3.2 atmospheric temperature records from the MSU and AMSU microwave sounders. J Atmos Oceanic Technol (in press)Google Scholar
- Randall RM, Hermann M (2008) Using limited time period trends as a means to determine attribution of discrepencies in microwave sounding unit derived tropospheric temperature time series. J Geophys Res 113. doi: 10.1029/2007/JD008864
- Roeckner E, Brasseur GP, Giorgetta M, Jacob D, Jungclaus J, Reick C, Sillman J (2006) Climate projections for the 21st century. Max Planck Institute for Meteorology Internal Rep., 32 pp. Available online at http://www.mpimet.mpg.de/fileadmin/grafik/presse/ClimateProjections2006.pdf.
- Sakamoto M, Christy JR (2009) The influences of TOVS radiance assimilation on temperature and moisture tendencies in JRA-25 and ERA-40. J Atmos Oc Tech 26:1435–1455Google Scholar
- Santer BD, Thorne PW, Haimberger L, Taylor KE, Wigley TML, Lanzante JR, Solomon S, Free M, Gleckler PJ, Jones PD, Karl TR, Klein SA, Mears C, Nychka D, Schmidt GA, Sherwood SC, Wentz FJ (2008) Consistency of modelled and observed temperature trends in the tropical troposphere. Int J Climatol 28:1703–1722CrossRefGoogle Scholar
- Simmons AJ, Willett KM, Jones PD, Thorne PW, Dee DP (2009) Low-frequency variations in surface atmospheric humidity, temperature and precipitation: inferences from reanalyses and monthly gridded observational datasets. J Geophys Res. doi: 10:1029/2009JD012511
- Uppala SM, Kållberg PW, Simmons AJ, Andrae U, Da Costa Bechtold V, Fiorino M, Gibson JK, Haseler J, Hernandez A, Kelly GA, Li X, Onogi K, Saarinen S, Sokka N, Allan RP, Andersson E, Arpe K, Balmaseda MA, Beljaars ACM, Van De Berg L, Bidlot J, Bormann N, Caires S, Chevallier F, Dethof A, Dragosavac M, Fisher M, Fuentes M, Hagemann S, Hólm E, Hoskins BJ, Isaksen L, Janssen PAEM, Jenne R, Mcnally AP, Mahfouf J-F, Morcrette J-J, Rayner NA, Saunders RW, Simon P, Sterl A, Trenberth KE, Untch A, Vasiljevic D, Viterbo P, Woollen J (2005) The ERA-40 re-analysis. Quart J R Meterol Soc 131:2961–3012CrossRefGoogle Scholar