(top) Scatterplot of AHT_{EQ} vs the mass overturning streamfunction at 500 hPa over the equator over the blendr 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 operates made use of and strategy

I use model efficiency off stage 3 of your Combined Model Intercomparison Project (CMIP3) multimodel databases (Meehl et al. 2007): a getup from standardized paired weather simulations of twenty five additional climate designs that have been used in the newest Intergovernmental Committee on Climate Change’s Last Assessment Declaration. I get to know the new preindustrial (PI) simulations right here. When it comes to those simulations, greenhouse gas concentrations, sprays, and you may solar pushing are repaired from the preindustrial accounts in addition to patterns are run to have 400 years. The final 20 years of PI simulations are accustomed to estimate climatological fields. The fresh new 16 activities used in this research are listed in Desk step 1.

Patterns found in this study in addition to their quality. The fresh new horizontal resolution is the latitudinal and you may longitudinal grid spacing and/or spectral truncation. The straight solution is the quantity of straight levels.

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) Overall performance

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_{Cent} 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_{Penny} 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_{Penny} 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. _{Cent} 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.