Fig 1.
Representation of the dynamic and thermodynamic changes associated with the intensified water cycle.
This diagram depicts changes with an El Niño-like pattern. As the climate warms, the atmosphere’s water-holding capacity increases further (reddish colors). That causes higher mass convergence and lower mass divergence (dashed gray lines), increasing the contrast between areas dominated by a convergent flow (e.g., equatorial Pacific Ocean) and the opposite in areas dominated by a divergent flow (subtropics). That, in turn, may increase the intensity of hydroclimate extremes: first, higher mass convergence is associated with higher precipitable water (e.g., [13]), and second, lower relative humidity in a warmer climate could increase the risk of drought, especially flash droughts [112]. The implications of such changes are critical to understanding the effects and teleconnection patterns of climate modes of variability, such as El Niño-Southern Oscillation (ENSO) in a changing climate.
Fig 2.
Trends in observed precipitation and Palmer Drought Severity Index (PDSI).
(A) trends from version 2020 of the Global Precipitation Climatology Centre (GPCCv2020), (B) trends from the “self-calibrated” PDSI dataset from Dai [18], (C) trends from the Climatic Research Unit version TSv 4.01 (CRU TSv.4.01), and (D) “self-calibrated” PDSI dataset from van der Schrier et al. [147]. The trends were calculated over a common period from 1950–2018. While GPCC and CRU precipitation datasets indicate similar patterns in their trends, the PDSI datasets differ in their trends, but most importantly in their magnitudes. In addition to using different input climate data, those PDSI data sets use different calibration periods. For example, the CRU PDSI product uses the whole period (i.e., 1901–2021), and Dai PDSI uses 1950–2000. This Figure was made with Natural Earth. Free vector and raster map data https://www.naturalearthdata.com.
Fig 3.
High and low Equilibrium Climate Sensitivity (ECS) models and projected soil moisture changes.
(A) ECS of the phase 6th of the Coupled Project Intercomparison Project (CMIP6) from Table 1. (B) Historical surface soil moisture anomalies (baseline 1950–1980) appended to the SSP5-8.5 scenario from high and low ECS models. Simulated soil moisture anomalies are normalized as z-scores from CanESM5, CNRM-CM6-1, CESM2, and IPSL-CM6A-LR as high ECS models, and CAMS-CSM1-0, MIROC6, MRI-ESM2-0, and BCC-CSM2-MR as low ECS models. Shading represents the maximum and minimum z-scores in each of the CMIP6 models we used, while the dark lines represent the multimodel mean. (C) Kernel density estimates of z-scores from the high and low ECS models. Dashed lines represent the mean of each density curve.
Table 1.
The CMIP6 models used in Fig 3.
We classified as “hot models” those with Equilibrium Climate Sensitivity (ECS) below 4.7 K/4xCO2.