(top) Scatterplot of AHTEQ 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 AHTEQ (red asterisk and linear best fit dashed line) and PCent vs AHTEQ (blue asterisk and linear best fit dashed line). The expected relationship between ??=0 and AHTEQ from Eq. (9) is shown by the dashed black line.
1) Model operates utilized and you can strategy
We play with design productivity from stage step three of your own Combined Design Intercomparison Project (CMIP3) multimodel databases (Meehl mais aussi al. 2007): a dress regarding standard combined environment simulations out-of twenty five more climate patterns that were utilized in the fresh Intergovernmental Panel into Environment Change’s 4th Research Report. We become familiar with the fresh preindustrial (PI) simulations here. When it comes to those simulations, greenhouse gas density, aerosols, and solar pushing was fixed at preindustrial profile therefore the designs are running to own 400 decades. The very last two decades of your own PI simulations are used to determine climatological areas. Brand new sixteen activities used in this study are listed in Dining table step 1.
Designs found in this study and their resolution. The fresh lateral solution is the latitudinal and you will longitudinal grid spacing and/or spectral truncation. New straight resolution ‘s the number of straight profile.
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 ?STORATMOS? is calculated from finite difference of the monthly averaged vertically integrated temperature and specific humidity fields; AHTEQ is then calculated from the residual of the other terms in Eq. (5).
2) 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 PCent and AHTEQ for each CMIP3 ensemble member in the upper panel of Fig. 6. We define the seasonal amplitude of PCent and AHTEQ as the amplitude of the annual harmonic of each variable. The CMIP3 ensemble mean regression coefficient between PCent and AHTEQ 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 PPenny and AHTEQ in observations and the models. Seasonal variations in PCent and AHTEQ 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 PCent and AHTEQ 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 AHTEQ, mainly the mutual relationship among the tropical precipitation maximum, AHTEQ, 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 find lesbian hookup apps (<2 days) phase shift between PCent and AHTEQ. We further discuss this result later in this section.