(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 PPenny 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) Design runs used and you will methodology
We have fun with design yields from stage step three of your Paired Model Intercomparison Investment (CMIP3) multimodel database (Meehl et al. 2007): a dress from standard paired environment simulations out of 25 more weather activities that were included in the latest Intergovernmental Committee into Climate Change’s Last Evaluation Statement. We get to know this new preindustrial (PI) simulations right here. In those simulations, greenhouse fuel levels, sprays, and you may solar pushing is repaired from the preindustrial levels therefore the designs are run getting 400 years. The last 20 years of your own PI simulations are widely used to determine climatological industries. This new sixteen activities included in this study was listed in Desk step 1.
Activities found in this study in addition to their quality. Brand new horizontal quality refers to the latitudinal and longitudinal grid spacing and/or spectral truncation. The latest straight solution ‘s the number of straight membership.
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).
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 lonely milf hookup between PPenny 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. Penny 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 (<2 days) phase shift between PCent and AHTEQ. We further discuss this result later in this section.