Diagnosing ENSO and global warming tropical precipitation shifts using 2 surface relative humidity and temperature
Journal of Climate
American Meteorological Society
© 2017 American Meteorological Society
Reason for embargo
Currently under an indefinite embargo pending publication of the Version of Record by American Meteorological Society. On publication of VoR, this AAM to be replaced with VoR under a 6 month embargo
Large uncertainty remains in future projections of tropical precipitation change under global warming. A simplified method for diagnosing tropical precipitation change is tested here on present day El Niño-Southern Oscillation (ENSO) precipitation shifts. This method, based on the weak temperature gradient approximation, assumes precipitation is associated with local surface relative humidity (RH) and air temperature (SAT), relative to the tropical mean. Observed and simulated changes in RH and SAT are subsequently used to diagnose changes in precipitation. Present day ENSO precipitation shifts are successfully diagnosed using observations (r = 0:69), and an ensemble of atmosphere-only (0:51 ≤ r ≤ 0:8) and coupled (0:5 ≤ r ≤ 0:87) climate model simulations. RH (r = 0:56) is much more influential than SAT (r = 0:27) in determining ENSO precipitation shifts for observations and climate model simulations over both land and ocean. Using inter-model differences, a significant relationship is demonstrated between method performance over ocean for present day ENSO and projected global warming (r = 0:68). As a caveat, we note that mechanisms leading to ENSO-related precipitation changes are not a direct analogue for global warming-related precipitation changes. The diagnosis method presented here demonstrates plausible mechanisms which relate changes in precipitation, RH and SAT under different climate perturbations. Therefore, uncertainty in future tropical precipitation changes may be linked with uncertainty in future RH and SAT changes.
AT was supported by a NERC studentship NE/M009599/1 and CASE funding from the Met Office. FHL was part supported by the UK-China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. RC was supported by the Newton Fund through the Met Office Climate Science for Service Partnership Brazil (CSSP Brazil).
This is the author accepted manuscript.
Published online 27 November 2017
- Mathematics