Realistic greenhouse gas forcing and seasonal forecasts

Abstract

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.

Methodological aspects of the validation of decadal predictions

“…Often model forecasts are modified and refined by statistical methods prior to being issued. Such a modification could, for example, be the correction of a systematic bias due to model drift as discussed above, or the removal of a linear trend as discussed in Section 5.2 (and in Liniger et al 2007), but also more sophisticated statistical post-processing techniques such as a re calibration of ensemble spread ). While calibration and statistical-post-processing have become common practice in weather and seasonal forecasting, the verification of such postprocessed forecasts remains a challenging issue.…”

Climate trends in a seasonal forecasting system

“…The analysis performed here serves to identify and partially correct for missing or badly represented trends but cannot unambiguously identify their source. Liniger et al (2007) investigated the differences between seasonal forecasts with and without GHG forcing in the European Centre for Medium-range Weather Forecasts (ECMWF) one-tier forecast system in which a coupled atmosphere/ocean model is used to produce the forecasts. The comparison with the two-tier second Historical Forecasting Project (HFP2) forecasts is not direct since in the HFP2 case SSTs do not react to the presence or absence of GHG forcing as they may in a one-tier forecast case.…”

Improved Estimates of the European Winter Windstorm Climate and the Risk of Reinsurance Loss Using Climate Model Data

“…These models are initialized with perturbed initial conditions based on observations at the beginning of each season and then integrated freely with boundary conditions thereafter (such as anthropogenic forcings; Liniger et al 2007). At the point in forecast lead time where the skill of forecasts is close to zero the ensemble members can be treated as independent samples of the model climate system (i.e., ergodic samples).…”