High sensitivity of tropical precipitation to local sea surface temperature

Precipitation and atmospheric circulation are the coupled processes through which tropical ocean surface temperatures drive global weather and climate^1–5. Local ocean surface warming tends to increase precipitation, but this local control is hard to disentangle from remote effects of conditions elsewhere. Such remote effects occur, for example, from El Niño Southern Oscillation (ENSO) events in the equatorial Pacific, which alter precipitation across the tropics. Atmospheric circulations associated with tropical precipitation are predominantly deep, extending up to the tropopause. Shallow atmospheric circulations^6–8, impacting the lower troposphere, also occur, but the importance of their interaction with precipitation is unclear. Uncertainty in precipitation observations^9,10, and limited observations of shallow circulations¹¹, further obstruct understanding of the ocean’s influence on weather and climate. Despite decades of research, persistent biases remain in many numerical model simulations^12–18, including excessively-wide tropical rainbands^14,18, the ‘double-intertropical convergence zone (ITCZ) problem’^12,16,17 and too-weak responses to ENSO¹⁵. These demonstrate stubborn gaps in our understanding, reducing confidence in forecasts and projections. Here we show that the real world has a high sensitivity of seasonal tropical precipitation to local sea-surface temperature. Our best observational estimate is 80% precipitation change per g/kg change in the saturation specific humidity (itself a function of the ocean surface temperature). This observed sensitivity is higher than in 43 of the 47 climate models studied, and is associated with strong shallow circulations. Models with more realistic sensitivity have smaller biases across a wide range of metrics. Our results apply to both temporal and spatial variation, over regions where climatological precipitation is around 1 mm/day or greater. Novel analysis of multiple independent observations, physical constraints and model data, underpin these findings. The spread in model behaviour is further linked to differences in shallow convection, providing a focus for accelerated research, to improve seasonal forecasts through multidecadal climate projections.

Vertical structure and physical processes of the Madden-Julian Oscillation: Biases and uncertainties at short range

An analysis of diabatic heating and moistening processes from 12 to 36 h lead time forecasts from 12 Global Circulation Models are presented as part of the "Vertical structure and physical processes of the Madden-Julian Oscillation (MJO)" project. A lead time of 12-36 h is chosen to constrain the large-scale dynamics and thermodynamics to be close to observations while avoiding being too close to the initial spin-up of the models as they adjust to being driven from the Years of Tropical Convection (YOTC) analysis. A comparison of the vertical velocity and rainfall with the observations and YOTC analysis suggests that the phases of convection associated with the MJO are constrained in most models at this lead time although the rainfall in the suppressed phase is typically overestimated. Although the large-scale dynamics is reasonably constrained, moistening and heating profiles have large intermodel spread. In particular, there are large spreads in convective heating and moistening at midlevels during the transition to active convection. Radiative heating and cloud parameters have the largest relative spread across models at upper levels during the active phase. A detailed analysis of time step behavior shows that some models show strong intermittency in rainfall and differences in the precipitation and dynamics relationship between models. The wealth of model outputs archived during this project is a very valuable resource for model developers beyond the study of the MJO. In addition, the findings of this study can inform the design of process model experiments, and inform the priorities for field experiments and future observing systems.