An Improved Covariate for Projecting Future Rainfall Extremes?. Issue 8 (2nd August 2020) - Roderick, Thomas P.; Wasko, Conrad; Sharma, Ashish
Abstract: Projection of extreme rainfall under climate change remains an area of considerable uncertainty. In the absence of geographically consistent simulations of extreme rainfall for the future, alternatives relying on physical relationships between a warmer atmosphere and its moisture carrying capacity are projected, scaling with a known atmospheric covariate. The most common atmospheric covariate adopted is surface air temperature, as it exhibits great consistency across climate model simulations into the future and, as per the Clausius‐Clapeyron relationship, has a well‐established link to atmospheric moisture capacity. However, empirical assessments of this relationship show that it varies with latitude, surface temperature, atmospheric temperature, and other factors, suggesting there may be more stable "global" atmospheric covariates that could be used instead. We argue that a better‐suited covariate would be one that captures the relationship between extreme rainfall and temperature but exhibits greater consistency in the relationship across regions as well as climatic zones. Our analysis identifies plausible atmospheric indicators of changes to future extreme rainfall, which now proliferate literature and compare their suitability based on the variability they exhibit across multiple geographical, topographic, and climatic zones within Australia. It is shown that surface air temperature exhibits a regionally inconsistent relationship with extreme rainfall and hence is not suitable for projecting to future conditions. The study identified integrated water vapor and surface dew point temperature as promising alternatives, with the former showing greater consistency in space but at the cost of reduced temporal coverage. Plain Language Summary: Studies of extreme rainfall sensitivity to temperature (termed scaling) improve our understanding of how extreme rainfall can be expected to change under global warming. The widespread and intuitive use of surface air temperature in rainfall scaling studies is likely the result of its observational availability and its apparent physical relationship with extreme rainfall. This study investigates the reliability of surface air temperature, integrated water vapor, surface dew point temperature, and 850 hPa atmospheric temperature as possible covariates for rainfall projection. Consistent with observed increases in extreme rainfall, a covariate that exhibits consistency across varying climatic conditions will result in more robust projections of future extreme rainfall. Here, we investigate the susceptibility of each covariate to varying conditions exhibited by the broad climates found across Australia. Integrated water vapor, retrieved from satellites, is found to be the most suitable scaling covariate for extreme rainfall projection. Key Points: Extreme rainfall does not scale consistently with surface air temperature Integrated water vapor is the most stable covariate when correlated to rainfall It is suggested integrated water be used as a covariate for rainfall projection