PubAg

Main content area

The sensitivity of the atmospheric branch of the global water cycle to temperature fluctuations at synoptic to decadal time-scales in different satellite- and model-based products

Author:
Nogueira, Miguel
Source:
Climate dynamics 2019 v.52 no.1-2 pp. 617-636
ISSN:
0930-7575
Subject:
climate, data collection, evaporation, hydrologic cycle, oceans, satellites, spectral analysis, surface temperature, uncertainty, weather
Abstract:
Spectral analysis of global-mean precipitation, P, evaporation, E, precipitable water, W, and surface temperature, Tₛ, revealed significant variability from sub-daily to multi-decadal time-scales, superposed on high-amplitude diurnal and yearly peaks. Two distinct regimes emerged from a transition in the spectral exponents, β. The weather regime covering time-scales < ~ 10 days with β ≥ 1; and the macroweather regime extending from a few months to a few decades with 0 <β <1. Additionally, the spectra showed a generally good statistical agreement amongst several different model- and satellite-based datasets. Detrended cross-correlation analysis (DCCA) revealed three important results which are robust across all datasets: (1) Clausius–Clapeyron (C–C) relationship is the dominant mechanism of W non-periodic variability at multi-year time-scales; (2) C–C is not the dominant control of W, P or E non-periodic variability at time-scales below about 6 months, where the weather regime is approached and other mechanisms become important; (3) C–C is not a dominant control for P or E over land throughout the entire time-scale range considered. Furthermore, it is suggested that the atmosphere and oceans start to act as a single coupled system at time-scales > ~ 1–2 years, while at time-scales < ~ 6 months they are not the dominant drivers of each other. For global-ocean and full-globe averages, ρDCCA showed large spread of the C–C importance for P and E variability amongst different datasets at multi-year time-scales, ranging from negligible (< 0.3) to high (~ 0.6–0.8) values. Hence, state-of-the-art climate datasets have significant uncertainties in the representation of macroweather precipitation and evaporation variability and its governing mechanisms.
Agid:
6316656