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Constraining the low-frequency (LF) behavior of general circulation models (GCMs) requires reliable observational estimates of LF variability. This two-part paper presents multiproxy reconstructions of Niño-3.4 sea surface temperature over the last millennium, applying two techniques [composite plus scale (CPS) and hybrid regularized expectation maximization (RegEM) truncated total least squares (TTLS)] to a network of tropical, high-resolution proxy records. This first part presents the data and methodology before evaluating their predictive skill using frozen network analysis (FNA) and pseudoproxy experiments. The FNA results suggest that about half of the Niño-3.4 variance can be reconstructed back to A.D. 1000, but they show little LF skill during certain intervals. More variance can be reconstructed in the interannual band where climate signals are strongest, but this band is affected by dating uncertainties (which are not formally addressed here). The CPS reliably estimates interannual variability, while LF fluctuations are more faithfully reconstructed with RegEM, albeit with inevitable variance loss. The RegEM approach is also tested on representative pseudoproxy networks derived from two millennium-long integrations of a coupled GCM. The pseudoproxy study confirms that reconstruction skill is significant in both the interannual and LF bands, provided that sufficient variance is exhibited in the target Niño-3.4 index. It also suggests that FNA severely underestimates LF skill, even when LF variability is strong, resulting in overly pessimistic performance assessments. The centennial-scale variance of the historical Niño-3.4 index falls somewhere between the two model simulations, suggesting that the network and methodology presented here would be able to capture the leading LF variations in Niño-3.4 for much of the past millennium, with the caveats noted above. [ABSTRACT FROM AUTHOR]

While transient climate change experiments with coupled atmosphere–ocean general circulation models undoubtedly represent the most comprehensive tool for studying the climate response to increasing concentrations of greenhouse gases (GHGs), less computationally expensive time-slice experiments with atmospheric GCMs are still useful to test the robustness of the projected climate change. In the present study, three sets of time-slice experiments with prescribed sea surface temperature (SST) are compared to a reference climate scenario obtained with the Centre National de Recherches Météorologiques Coupled Climate Model (CCM). The main objective is to assess the sensitivity of the monsoon response to the magnitude or pattern of SST anomalies in two regions where such anomalies are highly model dependent, namely, the circumpolar Southern Ocean and the tropical Pacific Ocean. On the one hand, it is shown that the regional climate anomalies predicted by the CCM can be reproduced at least qualitatively by a pair of time-slice experiments in which the present-day SST biases of the CCM are removed. On the other hand, the results indicate that the Indian monsoon response to increasing amounts of GHG is sensitive to regional uncertainties in the prescribed SST warming. Increasing the sea surface warming in the southern high latitudes to compensate for the weak sea ice feedback simulated by the CCM around the Antarctic has a significant influence on the regional climate change simulated over India, through a perturbation of the regional Hadley circulation. Prescribing zonal mean rather than El Niño–like SST anomalies in the tropical Pacific has an even stronger impact on the monsoon response, through a modification of the Walker circulation. These results suggest that both deficiencies in simulating present-day climate (even at high latitudes) and uncertainties in the SST patterns caused by enhanced GHG concentrations (especially in the tropical Pacific) are major obstacles for predicting climate change at the regional scale. [ABSTRACT FROM AUTHOR]