HadGEM3 underestimates interannual variations in heat fluxes, zonal winds and thermocline displacements in the tropical Pacific

There are large interannual variations in the area integral of the Pacific-wide annual-mean net surface heat fluxes within 5^o of the equator. They are shown to be very well correlated (r² = 0.75) with the zonal-mean, annual-mean, zonal component of the surface wind stress on the equator, both in UK-HadGEM3 coupled climate simulations and in the ERA5 wind-stress and DEEPC net surface heat flux re-analyses. For the model data the corresponding correlations are small for monthly means (r² = 0.25) but are large (r² > 0.6) for time-mean periods between 6 months and 10 years (the latter being calculated from 700 year pre-industrial control simulations). The amplitude of these annual mean fluctuations in the DEEPC net surface heat fluxes is almost twice as large as that in the UK-HadGEM3 simulations. Comparison of the area-mean fields in the Nino3 and Nino4 regions from 4 member ensembles of N216O025 historical simulations with the ERA5 winds, DEEPC heat fluxes and EN4 ocean re-analyses shows that the model’s mean values and seasonal cycle of the zonal wind stress and net surface heat flux agree well with the re-analyses. In the Nino3 region however the model’s surface temperature is 1.5^oC colder than the re-analyses and the depth of the 20^oC isotherm (t20d) is between 10 and 15 m shallower than that in EN4.  Comparison of the amplitudes of El Nino and La Nina composite anomalies in the Nino3 and Nino4 regions shows that the surface temperature anomalies are well simulated but that the amplitudes of the wind stress anomalies in Nino4 and the t20d anomalies and surface heat flux anomalies in Nino3 are about half those in ERA5, EN4 and DEEPC respectively. These findings are somewhat similar to those from the (lower resolution)  Kiel Climate Model. The characteristic spatial patterns of the surface fields might be used to attribute the differences between the model and re-analysis net surface fluxes to particular component fluxes (e.g. the surface latent heat flux and the surface solar flux). It is also a plausible hypothesis that the under-estimation of these variations in the net surface heat fluxes is a significant contributor to the signal-to-noise paradox.