Weather and climate models require knowledge about the surface temperature and surface heat flux to make predictions for climatology, farming, wind power prediction, etc. Most current models make use of Monin-Obukhov Similarity Theory (MOST). This theory expresses multiple turbulent fluxes in terms of their mean gradients with height. MOST results in an accurate universal form for the non-dimensional wind, temperature and humidity profiles from just above the surface and up to the top of the Atmospheric Surface Layer. The description of the near-surface temperature profile above a rough surface is, however, non-robust and ill-defined in the lowest layer of the atmosphere, the Roughness Sublayer. This thesis presents a new flux-gradient framework based on surface geometry. We apply it to the near-surface temperature profile of high-resolution experimental data from fibre optic measurements over a grass surface. The framework was developed to universally compare 3 different types of models at the surface: a Direct Numerical Simulation in a smooth pipe validated model; a constant surface gradient over grass model and a tall canopy model. Where the temperature is scaled with the already in use turbulent heat flux scale, the new length scale scales the height based on a finite surface gradient which is observed in experimental data. The experimental data can in this framework collapse onto one universal profile with the use of a reference temperature taken at the top of the grass. The framework thus extends MOST with a universal non-dimensional temperature profile in the roughness sublayer.