Abstract

Simulations of cloud radiative properties for climate modeling and remote sensing rely on accurate knowledge of the complex refractive index (CRI) of water. Although conventional algorithms employ a temperature-independent assumption (TIA), recent infrared measurements of supercooled water have demonstrated that the CRI becomes increasingly ice-like at lower temperatures. Here, we assess biases that result from ignoring this temperature dependence. We show that TIA-based cloud retrievals introduce spurious ice into pure, supercooled clouds, or underestimate cloud optical thickness and droplet size. TIA-based downwelling radiative fluxes are lower than those for the temperature-dependent CRI by as much as 1.7 W m(-2) (in cold regions), while top-of-atmosphere fluxes are higher by as much as 3.4 W m(-2) (in warm regions). Proper accounting of the temperature dependence of the CRI, therefore, leads to significantly greater local greenhouse warming due to supercooled clouds than previously predicted. The current experimental uncertainty in the CRI at low temperatures must be reduced to account for supercooled clouds properly in both climate models and cloud-property retrievals.