Climate Field Reconstruction under Stationary and Nonstationary Forcing

The fidelity of climate reconstructions employing covariance-based calibration techniques is tested with varying levels of sparseness of available data during intervals of relatively constant (stationary) and increasing (nonstationary) forcing. These tests employ a regularized expectation-maximization algorithm using surface temperature data from both the instrumental record and coupled ocean–atmosphere model integrations. The results indicate that if radiative forcing is relatively constant over a data-rich calibration period and increases over a data-sparse reconstruction period, the imputed temperatures in the reconstruction period may be biased and may underestimate the true temperature trend. However, if radiative forcing is stationary over a data-sparse reconstruction period and increases over a data-rich calibration period, the imputed values in the reconstruction period are nearly unbiased. These results indicate that using the data-rich part of the twentieth-century instrumental record (which contains an increasing temperature trend plausibly associated with increasing radiative forcing) for calibration does not significantly bias reconstructions of prior climate.