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- Author or Editor: Tom M. L. Wigley x
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Abstract
A bivariate time series regression approach is used to model observed variations in hemispheric mean temperature over the period 1900–96. The regression equations include deterministic predictor variables and lagged values of the two predictands, and two different forms of this basic structure are employed. The deterministic predictors considered are simple linear trends, various climate model–generated time series based on different combinations of greenhouse gas, sulfate aerosol, and solar forcing, and the Southern Oscillation index (SOI). With linear trends as the only predictors, the best model is a fourth-order bivariate autoregressive model including lagged Southern Hemisphere (SH) to Northern Hemisphere (NH) dependence, as in previous work by Kaufmann and Stern. The estimated NH and SH trends are both +0.67°C century−1, and both are highly statistically significant. If SOI is included as an additional predictor, however, a first-order time series model, with no SH to NH dependence, is an adequate fit to the data. This shows that SOI may be an important covariate in this kind of analysis. Further analysis uses climate model–generated forcing terms representing greenhouses gases, sulfate aerosols, and solar effects, as well as SOI. The statistical analysis makes extensive use of Bayes factors as a device for discriminating among a wide spectrum of possible models. The best fits to the data are obtained when all three forcing terms are included. Total sulfate aerosol forcing of −1.1 W m−2 (with a corresponding climate sensitivity of ΔT 2× = 4.2°C) is preferred to −0.7 W m−2 (with sensitivity of 2.3°C), but the Bayes factor discrimination between these cases is weak.
Abstract
A bivariate time series regression approach is used to model observed variations in hemispheric mean temperature over the period 1900–96. The regression equations include deterministic predictor variables and lagged values of the two predictands, and two different forms of this basic structure are employed. The deterministic predictors considered are simple linear trends, various climate model–generated time series based on different combinations of greenhouse gas, sulfate aerosol, and solar forcing, and the Southern Oscillation index (SOI). With linear trends as the only predictors, the best model is a fourth-order bivariate autoregressive model including lagged Southern Hemisphere (SH) to Northern Hemisphere (NH) dependence, as in previous work by Kaufmann and Stern. The estimated NH and SH trends are both +0.67°C century−1, and both are highly statistically significant. If SOI is included as an additional predictor, however, a first-order time series model, with no SH to NH dependence, is an adequate fit to the data. This shows that SOI may be an important covariate in this kind of analysis. Further analysis uses climate model–generated forcing terms representing greenhouses gases, sulfate aerosols, and solar effects, as well as SOI. The statistical analysis makes extensive use of Bayes factors as a device for discriminating among a wide spectrum of possible models. The best fits to the data are obtained when all three forcing terms are included. Total sulfate aerosol forcing of −1.1 W m−2 (with a corresponding climate sensitivity of ΔT 2× = 4.2°C) is preferred to −0.7 W m−2 (with sensitivity of 2.3°C), but the Bayes factor discrimination between these cases is weak.
Natural variability of the climate system imposes a large uncertainty on future climate change signals simulated by a single integration of any coupled ocean–atmosphere model. This is especially true for regional precipitation changes. Here, these uncertainties are reduced by using results from two ensembles of five integrations of a coupled ocean–atmosphere model forced by projected future greenhouse gas and sulfate aerosol changes. Under a business-as-usual scenario, the simulations show a global warming of ~1.9°C over the twenty-first century (continuing the trend observed since the late 1970s), accompanied by a ~3% increase in global precipitation. Stabilizing the CO2 level at 550 ppm reduces the warming only moderately (by ~0.4°C in 2100). The patterns of seasonal-mean temperature and precipitation change in the two cases are highly correlated (r ≈ 0.99 for temperature and r ≈ 0.93 for precipitation). Over the midlatitude North Atlantic Ocean, the model produces a moderate surface cooling (1°–2°C, mostly in winter) over the twenty-first century. This cooling is accompanied by changes in atmospheric lapse rates over the region (i.e., larger warming in the free troposphere than at the surface), which stabilizes the surface ocean. The resultant reduction in local oceanic convection contributes to a 20% slowdown in the thermohaline circulation.
Natural variability of the climate system imposes a large uncertainty on future climate change signals simulated by a single integration of any coupled ocean–atmosphere model. This is especially true for regional precipitation changes. Here, these uncertainties are reduced by using results from two ensembles of five integrations of a coupled ocean–atmosphere model forced by projected future greenhouse gas and sulfate aerosol changes. Under a business-as-usual scenario, the simulations show a global warming of ~1.9°C over the twenty-first century (continuing the trend observed since the late 1970s), accompanied by a ~3% increase in global precipitation. Stabilizing the CO2 level at 550 ppm reduces the warming only moderately (by ~0.4°C in 2100). The patterns of seasonal-mean temperature and precipitation change in the two cases are highly correlated (r ≈ 0.99 for temperature and r ≈ 0.93 for precipitation). Over the midlatitude North Atlantic Ocean, the model produces a moderate surface cooling (1°–2°C, mostly in winter) over the twenty-first century. This cooling is accompanied by changes in atmospheric lapse rates over the region (i.e., larger warming in the free troposphere than at the surface), which stabilizes the surface ocean. The resultant reduction in local oceanic convection contributes to a 20% slowdown in the thermohaline circulation.
