The influence of climate feedbacks on regional hydrological changes under warming is poorly understood. Here, a moist energy balance model (MEBM) with a Hadley Cell parameterization is used to isolate the influence of climate feedbacks on changes in zonal-mean precipitation-minus-evaporation (P-E) under greenhouse-gas forcing. It is shown that cloud feedbacks act to narrow bands of tropical P-E and increase P-E in the deep tropics. The surface-albedo feedback shifts the location of maximum tropical P-E and increases P-E in the polar regions. The intermodel spread in the P-E changes associated with feedbacks arises mainly from cloud feedbacks, with the lapse-rate and surface-albedo feedbacks playing important roles in the polar regions. The P-E change associated with cloud feedback locking in the MEBM is similar to that of a climate model with inactive cloud feedbacks. This work highlights the unique role that climate feedbacks play in causing deviations from the “wet-gets-wetter, dry-gets-drier” paradigm.

The response of precipitation minus evaporation (P-E) to global warming is investigated using a moist energy balance model (MEBM) with a simple Hadley-Cell parameterization. The MEBM accurately emulates P-E changes simulated by a suite of global climate models (GCMs) under greenhouse-gas forcing. The MEBM also accounts for most of the intermodel differences in GCM P-E changes and better emulates GCM P-E changes when compared to the “wet-gets-wetter, dry-gets-drier” thermodynamic mechanism. The intermodel spread in P-E changes are attributed to intermodel differences in radiative feedbacks, which account for 60-70% of the intermodel variance, with smaller contributions from radiative forcing and ocean heat uptake. Isolating the intermodel spread of feedbacks to specific regions shows that tropical feedbacks are the primary source of intermodel spread in P-E changes. The ability of the MEBM to emulate GCM P-E changes is further investigated using idealized feedback patterns. A less negative and narrowly peaked feedback pattern near the equator results in more atmospheric heating, which strengthens the Hadley Cell circulation in the deep tropics through an enhanced poleward heat flux. This pattern also increases gross moist stability, which weakens the subtropical Hadley Cell circulation. These two processes in unison increase P-E in the deep tropics, decrease P-E in the subtropics, and narrow the Intertropical Convergence Zone. Additionally, a feedback pattern that produces polar-amplified warming reduces the poleward moisture flux by weakening the meridional temperature gradient and the Clausius-Clapeyron relation. It is shown that changes to the Hadley Cell circulation and the poleward moisture flux are crucial for understanding the pattern of GCM P-E changes under warming.

The Miocene epoch, spanning 23.03-5.33Ma, was a dynamic climate of sustained, polar amplified warmth. Miocene atmospheric CO2 concentrations are typically reconstructed between 300-600ppm and were potentially higher during the Miocene Climatic Optimum (16.75-14.5Ma). With surface temperature reconstructions pointing to substantial midlatitude and polar warmth, it is unclear what processes maintained the much weaker-than-modern equator-to-pole temperature difference. Here we synthesize several Miocene climate modeling efforts together with available terrestrial and ocean surface temperature reconstructions. We evaluate the range of model-data agreement, highlight robust mechanisms operating across Miocene modelling efforts, and regions where differences across experiments result in a large spread in warming responses. Prescribed CO2 is the primary factor controlling global warming across the ensemble. On average, elements other than CO2, such as Miocene paleogeography and ice sheets, raise global mean temperature by ~ 2℃, with the spread in warming under a given CO2 concentration (due to a combination of the spread in imposed boundary conditions and climate feedback strengths) equivalent to ~1.2 times a CO2 doubling. This study uses an ensemble of opportunity: models, boundary conditions, and reference datasets represent the state-of-art for the Miocene, but are inhomogeneous and not ideal for a formal intermodel comparison effort. Acknowledging this caveat, this study is nevertheless the first Miocene multi-model, multi-proxy comparison attempted so far. This study serves to take stock of the current progress towards simulating Miocene warmth while isolating remaining challenges that may be well served by community-led efforts to coordinate modelling and data activities within a common analysis framework.