(top) Scatterplot of AHT_{EQ} vs the mass overturning streamfunction at 500 hPa over the equator over the seasonal cycle in the observations. Each asterisk is a monthly average and the dashed line is the linear best fit. (bottom) Scatterplot of the location of the 0 mass overturning streamfunction ?_{?=0} at 500 hPa vs AHT_{EQ} (red asterisk and linear best fit dashed line) and P_{Penny} vs AHT_{EQ} (blue asterisk and linear best fit dashed line). The expected relationship between ?_{?=0} and AHT_{EQ} from Eq. (9) is shown by the dashed black line.

## 1) Design works made use of and strategy

We have fun with design productivity from stage 3 of your own Combined Model Intercomparison Opportunity (CMIP3) multimodel databases (Meehl mais aussi al. 2007): a clothes out-of standard paired weather simulations of twenty-five other weather activities that have been utilized in the latest Intergovernmental Committee into Environment Change’s Fourth Analysis Declaration. We become familiar with the newest preindustrial (PI) simulations right here. In those simulations, greenhouse gas levels, aerosols, and you will solar pressuring try repaired from the preindustrial account and patterns are run getting 400 years. The very last twenty years of your PI simulations are widely used to assess climatological industries. New sixteen designs used in this study is actually listed in Dining table step 1.

Models included in this research in addition to their resolution. The new horizontal quality refers to the latitudinal and you will longitudinal grid spacing or even the spectral truncation. Brand new vertical quality is the quantity of vertical accounts.

The turbulent and radiative energy fluxes at the surface and TOA are provided as model output fields. This allows ?SWABS? and ?SHF? to be directly calculated from Eqs. (6) and (7). The ?OLR? is directly calculated and ?STOR_{ATMOS}? is calculated from finite difference of the monthly averaged vertically integrated temperature and specific humidity fields; AHT_{EQ} is then calculated from the residual of the other terms in Eq. (5).

## 2) Results

We show the seasonal amplitude (given by half the length of the line) and the regression coefficient (given by the slope of the line) between P_{Penny} and AHT_{EQ} for each CMIP3 ensemble member in the upper panel of Fig. 6. We define the seasonal amplitude of P_{Cent} and AHT_{EQ} as the amplitude of the annual harmonic of each variable. The CMIP3 ensemble mean regression coefficient between P_{Penny} and AHT_{EQ} is ?2.4° ± 0.4° PW ?1 (the slope of the thick black line) and is slightly smaller but statistically indistinguishable from the value of ?2.7° ± 0.6° PW ?1 found in the observations (the thick purple line). Table 2 lists the seasonal statistics of P_{Cent} and AHT_{EQ} in observations and the models. Seasonal variations in P_{Cent} and AHT_{EQ} are significantly correlated with each other in all models with an ensemble average correlation coefficient of ?0.89. On average, the linear best fits in the models come closer to the origin than do the observations (thick black line in Fig. 6), conforming to our idealized expectation that when the precipitation is centered on the equator, the ascending branch of the Hadley cell will also be on the equator, resulting in zero cross-equatorial heat transport in the atmosphere. The relationship between P_{Cent} and AHT_{EQ} over the seasonal cycle is fairly consistent from one model to the next (all the slopes in Fig. 6 are similar) and is similar to the relationship found in the observations. _{Penny} and AHT_{EQ}, mainly the mutual relationship among the tropical precipitation maximum, AHT_{EQ}, and the location of the Hadley cell. The precipitation centroid lags the cross-equatorial atmospheric heat transport in the models by 29 days in the ensemble average (with a standard deviation of 6 days). This is in contrast to the observations where there is virtually no (<2 days) phase shift between P_{Cent} and AHT_{EQ}. We further discuss this result later in this section.