Search for: All records

Changes in tropical (30 S–30 N) land hydroclimate following CO₂‐induced global warming are organized according to climatological aridity index (AI) and daily soil moisture (SM) percentiles. The transform from geographical space to this novel process‐oriented phase space allows for interpretation of local, daily mechanistic relationships between key hydroclimatic variables in the context of time‐mean and/or global‐mean energetic constraints and the wet‐get‐wetter/dry‐get‐drier paradigm. Results from 16 CMIP models show coherent patterns of change in the AI/SM phase space that are aligned with the established soil‐moisture/evapotranspiration regimes. We introduce an active‐rain regime as a special case of the energy‐limited regime. Rainfall shifts toward larger rain totals in this active‐rain regime, with less rain on other days, resulting in an overall SM reduction. Consequently, the regimes where SM constrains evapotranspiration become more frequently occupied, and corresponding hydroclimatic changes align with the position of the critical SM value in the AI/SM phase space.

The extension of a cloud‐resolving model, the System for Atmospheric Modeling (SAM), to global domains is described. The resulting global model, gSAM, is formulated on a latitude‐longitude grid. It uses an anelastic dynamical core with a single reference profile (as in SAM), but its governing equations differ somewhat from other anelastic models. For quasihydrostatic flows, they are isomorphic to the primitive equations (PE) in pressure coordinates but with the globally uniform reference pressure playing the role of actual pressure. As a result, gSAM can exactly maintain steady zonally symmetric baroclinic flows that have been specified in pressure coordinates, produces accurate simulations when initialized or nudged with global reanalyses, and has a natural energy conservation equation despite the drawbacks of using the anelastic system to model global scales. gSAM employs a novel treatment of topography using a type of immersed boundary method, the Quasi‐Solid Body Method, where the instantaneous flow velocity is forced to stagnate in grid cells inside a prescribed terrain. The results of several standard tests designed to evaluate the accuracy of global models with and without topography as well as results from real Earth simulations are presented.

El Niño–Southern Oscillation (ENSO) variability is accompanied by out‐of‐phase anomalies in the top‐of‐atmosphere tropical radiation budget, with anomalous downward flux (i.e., net radiative heating) before El Niño and anomalous upward flux thereafter (and vice versa for La Niña). Here, we show that these radiative anomalies result mainly from a sea surface temperature (SST) “pattern effect,” mediated by changes in tropical‐mean tropospheric stability. These stability changes are caused by SST anomalies migrating from climatologically cool to warm regions over the ENSO cycle. Our results are suggestive of a two‐way coupling between SST variability and radiation, where ENSO‐induced radiative changes may in turn feed back onto SST during ENSO.

Pervasive cirrus clouds in the upper troposphere and tropical tropopause layer (TTL) influence the climate by altering the top‐of‐atmosphere radiation balance and stratospheric water vapor budget. These cirrus are often associated with deep convection, which global climate models must parameterize and struggle to accurately simulate. By comparing high‐resolution global storm‐resolving models from the Dynamics of the Atmospheric general circulation Modeled On Non‐hydrostatic Domains (DYAMOND) intercomparison that explicitly simulate deep convection to satellite observations, we assess how well these models simulate deep convection, convectively generated cirrus, and deep convective injection of water into the TTL over representative tropical land and ocean regions. The DYAMOND models simulate deep convective precipitation, organization, and cloud structure fairly well over land and ocean regions, but with clear intermodel differences. All models produce frequent overshooting convection whose strongest updrafts humidify the TTL and are its main source of frozen water. Intermodel differences in cloud properties and convective injection exceed differences between land and ocean regions in each model. We argue that, with further improvements, global storm‐resolving models can better represent tropical cirrus and deep convection in present and future climates than coarser‐resolution climate models. To realize this potential, they must use available observations to perfect their ice microphysics and dynamical flow solvers.

Cirrus clouds of various thicknesses and radiative characteristics extend over much of the tropics, especially around deep convection. They are difficult to observe due to their high altitude and sometimes small optical depths. They are also difficult to simulate in conventional global climate models, which have coarse grid spacings and simplified parameterizations of deep convection and cirrus formation. We investigate the representation of tropical cirrus in global storm‐resolving models (GSRMs), which have higher spatial resolution and explicit convection and could more accurately represent cirrus cloud processes. This study uses GSRMs from the DYnamics of the Atmospheric general circulation Modeled On Non‐hydrostatic Domains (DYAMOND) project. The aggregate life cycle of tropical cirrus is analyzed using joint albedo and outgoing longwave radiation (OLR) histograms to assess the fidelity of models in capturing the observed cirrus cloud populations over representative tropical ocean and land regions. The proportions of optically thick deep convection, anvils, and cirrus vary across models and are portrayed in the vertical distribution of cloud cover and top‐of‐atmosphere radiative fluxes. Model differences in cirrus populations, likely driven by subgrid processes such as ice microphysics, dominate over regional differences between convectively active tropical land and ocean locations.

In clouds containing both liquid and ice with temperatures between −3°C and −8°C, liquid droplets collide with large ice crystals, freeze, and shatter, producing a plethora of small ice splinters. This process, known as Hallett‐Mossop rime splintering, and other forms of secondary ice production, can cause clouds to reflect less sunlight and to have shorter lifetimes. We show its impact on Southern Ocean shallow cumuli using a novel suite of five global storm‐resolving simulations, which partition the Earth's atmosphere into 2–4 km wide columns. We evaluate simulated clouds and radiation over the Southern Ocean with aircraft observations from the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES), and satellite observations from Clouds and the Earth's Radiant Energy System (CERES) and Himawari. Simulations with large concentrations of ice crystals in boundary layer clouds, which agree better with SOCRATES observations, have reduced mixed‐phase cumulus cloud cover and weaker shortwave cloud radiative effects (CREs) that are less biased compared with CERES. Using a pair of simulations differing only in their treatment of Hallett‐Mossop rime splintering, we show that including this process increases ice crystal concentrations in cumulus clouds and weakens shortwave CREs over the Southern Ocean by 10 W m⁻². We also demonstrate the key role that global storm‐resolving models can play in detangling the effects of clouds on Earth's climate across scales, making it possible to trace the impact of changes in individual cumulus cloud anvils (10 km²) on the radiative budget of the massive Southern Ocean basin (10⁷ km²).

Climate model simulations project different regimes of summertime temperature distribution changes under a quadrupling of CO₂for dry land, moist land, and oceanic surfaces. The entire temperature distribution shifts over dry land surfaces, while moist land surfaces feature an elongated upper tail of the distribution, with extremes increasing more than the corresponding means by ∼20% of the global mean warming. Oceanic surfaces show weaker warming relative to land surfaces, with no significant elongation of the upper tail. Dry land surfaces show little change in turbulent sensible (SH) or latent (LH) fluxes, with new balance reached with compensating adjustments among downwelling and upwelling radiative fluxes. By contrast, moist land surfaces show enhanced partitioning of turbulent flux toward SH, while oceanic surfaces show enhanced partitioning toward LH. Amplified warming of extreme temperatures over moist land surfaces is attributed to suppressed evapotranspiration and larger Bowen ratios.

Tropical average shortwave cloud radiative effect (SWCRE) anomalies observed by CERES/EBAF v4 are explained by observed average sea surface temperature () and the difference between the warmest 30% where deep convection occurs and). Observed tropospheric temperatures show variations in boundary layer capping strength over time consistent with the evolution of SST^#. The CERES/EBAF v4 data confirm that associated cloud fraction changes over the colder waters dominate SWCRE. This observational evidence for the “pattern effect” noted in General Circulation Model simulations suggests that SST^#captures much of this effect. The observed sensitivities (dSWCRE/dW·m⁻²·K⁻¹, dSWCRE/dSST^#≈−4.8W·m⁻²·K⁻¹) largely reflect El Niño–Southern Oscillation. As El Niño develops,increases and SST^#decreases (both increasing SWCRE). Only after the El Niño peak, SST^#increases and SWCRE decreases. SST^#is also relevant for the tropical temperature trend profile controversy and the discrepancy between observed and modeled equatorial Pacific SST trends. Causality and implications for future climates are discussed.

General Circulation Model (GCM) simulations with prescribed observed sea surface temperature (SST) over the historical period show systematic global shortwave cloud radiative effect (SWCRE) variations uncorrelated with global surface temperature (known as “pattern effect”). Here, we show that a single parameter that quantifies the difference in SSTs between regions of tropical deep convection and the tropical or global average (Δ^conv) captures the time‐varying “pattern effect” in the simulations using the PCMDI/AMIPII SST recommended for CMIP6. In particular, a large positive trend in the 1980s–1990s in Δ^convexplains the change of sign to a strongly negative SWCRE feedback since the late 1970s. In these decades, the regions of deep convection warm about +50% more than the tropical average. Such an amplification is rarely observed in forced coupled atmosphere‐ocean GCM simulations, where the amplified warming is typically about +10%. During the post 2000 global warming hiatus Δ^convshows little change, and the more recent period of resumed global warming is too short to robustly detect trends. In the prescribed SST simulations, Δ^convis forced by the SST difference between warmer and colder regions. An index thereof (SST^#) evaluated for six SST reconstructions shows similar trends for the satellite era, but the difference between the pre‐ and the satellite era is substantially larger in the PCMDI/AMIPII SSTs than in the other reconstructions. Quantification of the cloud feedback depends critically on small changes in the shape of the SST probability density distribution. These sensitivities underscore how essential highly accurate, persistent, and stable global climate records are to determine the cloud feedback.

The linearity of global‐mean outgoing longwave radiation (OLR) with surface temperature is a basic assumption in climate dynamics. This linearity manifests in global climate models, which robustly produce a global‐mean longwave clear‐sky (LWCS) feedback of 1.9 W/m²/K, consistent with idealized single‐column models (Koll & Cronin, 2018,https//:doi.org/10.1073/pnas.1809868115). However, there is considerable spatial variability in the LWCS feedback, including negative values over tropical oceans (known as the “super‐greenhouse effect”) which are compensated for by larger values in the subtropics/extratropics. Therefore, it is unclear how the idealized single‐column results are relevant for the global‐mean LWCS feedback in comprehensive climate models. Here we show with a simple analytical theory and model output that the compensation of this spatial variability to produce a robust global‐mean feedback can be explained by two facts: (1) When conditioned upon free‐tropospheric column relative humidity (RH), the LWCS feedback is independent of RH, and (2) the global histogram of free‐tropospheric column RH is largely invariant under warming.