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Daytime and nighttime validation experiments

Homogeneous HadAM3 climate simulation data from a run with prescribed historical SSTs and natural and anthropogenic forcings were used for the validation experiments. Daytime and nighttime seasonal anomalies (relative to a 1981-2000 climatology) for the period 1958 to 2003 at 10 pressure levels (850, 700, 500, 400, 300, 250, 200, 150, 100, 50hPa) were calculated. Temporal and spatial sampling characteristics of the daytime and nighttime observed radiosonde data were imposed on the model data. The two resulting data sets are known as the 'control data sets'. Four error (or breakpoint) models based on different, complex assumptions were derived in order to produce breakpoint profiles that could exist in the real world. These were applied to the control data sets. For more information about the control data sets and error models see Titchner et al. (2009).

An ensemble of 100 validation experiments was performed on each of the daytime and nighttime error models using the parameter settings listed here. The same 100 experimental setups were also used in the observational experiments. For more information about the validation experiments and system settings see Titchner et al. (2009) and McCarthy et al. (2008).

As the original model 'truth' is known, the ability of the system to homogenise data can be assessed and inferences about the real world observations can be made. For a reassessment of the tropical tropospheric trends see Titchner et al. (2009).

Daytime and nighttime station control model data used for the validation experiments
Daytime and nighttime breakpoints that were applied to the control data in each of the four error models for use in the validation experiments.
Daytime and nighttime adjustments applied during each of the 100 validation experiments on the four error error models and control data sets - COMING SOON
Daytime and nighttime large scale trend estimates produced from each of the 100 validation experiments for the four error error models and control data sets as well as the trends in the unadjusted input data.

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