Realistic greenhouse gas forcing and seasonal forecasts

[1] This paper investigates the improvement of seasonal forecasts by including realistically varying greenhouse gas (GHG) concentrations. Forecasts starting every May and November are compared over the period 1958 until 2001. One set has constant GHG concentrations while an other has a realistic GHG trend. The large scale temperature trends derived at different lead times are compared between the forecast sets and observations over the entire 44 years. It is shown that after a few months the anthropogenic climate change signal is lost by up to 70% although it was present in the initial conditions. The differences in trends vary with lead times, seasons and regions. Strongest effects are found in the Tropics and the Summer Hemispheres, in particular the Northern one. On the local scale, the improvement is not widespread in trends and very weak in predicting detrended interannual variability. Both sets exhibit a strong absolute temperature bias.

[1]  Francisco J. Doblas-Reyes,et al.  A Debiased Ranked Probability Skill Score to Evaluate Probabilistic Ensemble Forecasts with Small Ensemble Sizes , 2005 .

[2]  Pattern and trend analysis of temperature in a set of seasonal ensemble simulations , 2004 .

[3]  Mark A. Liniger,et al.  Challenges posed by and approaches to the study of seasonal-to-decadal climate variability , 2006 .

[4]  David L. T. Anderson,et al.  Did the ECMWF seasonal forecast model outperform statistical ENSO forecast models over the last 15 years , 2005 .

[5]  Myles R. Allen,et al.  Incorporating model uncertainty into attribution of observed temperature change , 2006 .

[6]  Renate Hagedorn,et al.  The rationale behind the success of multi-model ensembles in seasonal forecasting — I. Basic concept , 2005 .

[7]  A. Sterl,et al.  The ERA‐40 re‐analysis , 2005 .

[8]  P. Stott,et al.  External control of 20th century temperature by natural and anthropogenic forcings. , 2000, Science.

[9]  Renate Hagedorn,et al.  The rationale behind the success of multi-model ensembles in seasonal forecasting — II. Calibration and combination , 2005 .

[10]  T. Palmer,et al.  Impact of increasing greenhouse gas concentrations in seasonal ensemble forecasts , 2006 .

[11]  F. Vitart,et al.  Seasonal Forecasting of Tropical Storms Using Coupled GCM Integrations , 2001 .

[12]  Andrew P. Morse,et al.  DEVELOPMENT OF A EUROPEAN MULTIMODEL ENSEMBLE SYSTEM FOR SEASONAL-TO-INTERANNUAL PREDICTION (DEMETER) , 2004 .

[13]  Nils Wedi,et al.  Comparison of trends and low-frequency variability in CRU, ERA-40, and NCEP//NCAR analyses of surface air temperature , 2004 .