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Abstract The impact of convective closure on the double‐ITCZ bias in the NCAR CESM2.2 is investigated in this study. The standard CESM2.2 simulates a remarkable double‐ITCZ bias in the central and eastern Pacific, especially in boreal winter and spring. Modifications to the closure in convection parameterization scheme greatly reduce the double‐ITCZ bias in all seasons, demonstrating that convection parameterization can substantially influence the double‐ITCZ bias in CESM2.2. Further analyses suggest that convection parameterization can modulate the tropical atmosphere‐ocean feedback processes, through which it influences the SST in the southern ITCZ region and hence the double‐ITCZ bias. The changes in the upper ocean temperature advection induced by modified convective closure plays important roles in reducing the warm SST bias and double‐ITCZ precipitation bias in the southern ITCZ region. The modified convective closure improves the low‐level cloud and shortwave cloud radiative forcing in the southeastern Pacific. However, surface heat flux plays only a limited role in reducing warm SST bias and double ITCZ bias because the impacts of shortwave radiation changes are largely canceled by changes in longwave radiation and latent heat flux. 

Abstract Humid heat waves pose significant risks to human health and the ecosystem. Intuitively, rainfall often alleviates extreme humid heat. However, here we show that light rain often accompanies extreme humid heat, exacerbating its frequency and intensity, especially over arid and semi-arid regions compared to no rain and moderate-to-heavy rain cases. This is because light rain does not dramatically reduce solar radiation but increases near-surface humidity through enhanced surface evaporation. The water replenishment from light rain as well as a shallower planetary boundary layer is crucial for consecutive extremes where there are commonly sporadic drizzle days amidst several rain-free days. These extremes last longer than rain-free extremes. Current global climate models (GCMs) overestimate light rain. After reducing this bias in a GCM, underestimations of humid heat waves in energy-limited regions and overestimations in water-limited regions are largely alleviated. These findings underscore the underappreciated impact of light rain on extreme humid heat. 

Very high tropical alpine ice cores provide a distinct paleoclimate record for climate changes in the middle and upper troposphere. However, the climatic interpretation of a key proxy, the stable water oxygen isotopic ratio in ice cores (δ¹⁸O_ice), remains an outstanding problem. Here, combining proxy records with climate models, modern satellite measurements, and radiative-convective equilibrium theory, we show that the tropical δ¹⁸O_iceis an indicator of the temperature of the middle and upper troposphere, with a glacial cooling of −7.35° ± 1.1°C (66% CI). Moreover, it severs as a “Goldilocks-type” indicator of global mean surface temperature change, providing the first estimate of glacial stage cooling that is independent of marine proxies as −5.9° ± 1.2°C. Combined with all estimations available gives the maximum likelihood estimate of glacial cooling as −5.85° ± 0.51°C. 

Abstract With the recent advances in data science, machine learning has been increasingly applied to convection and cloud parameterizations in global climate models (GCMs). This study extends the work of Han et al. (2020,https://doi.org/10.1029/2020MS002076) and uses an ensemble of 32‐layer deep convolutional residual neural networks, referred to as ResCu‐en, to emulate convection and cloud processes simulated by a superparameterized GCM, SPCAM. ResCu‐en predicts GCM grid‐scale temperature and moisture tendencies, and cloud liquid and ice water contents from moist physics processes. The surface rainfall is derived from the column‐integrated moisture tendency. The prediction uncertainty inherent in deep learning algorithms in emulating the moist physics is reduced by ensemble averaging. Results in 1‐year independent offline validation show that ResCu‐en has high prediction accuracy for all output variables, both in the current climate and in a warmer climate with +4K sea surface temperature. The analysis of different neural net configurations shows that the success to generalize in a warmer climate is attributed to convective memory and the 1‐dimensional convolution layers incorporated into ResCu‐en. We further implement a member of ResCu‐en into CAM5 with real world geography and run the neural‐network‐enabled CAM5 (NCAM) for 5 years without encountering any numerical integration instability. The simulation generally captures the global distribution of the mean precipitation, with a better simulation of precipitation intensity and diurnal cycle. However, there are large biases in temperature and moisture in high latitudes. These results highlight the importance of convective memory and demonstrate the potential for machine learning to enhance climate modeling. 

Abstract This study examines the free-tropospheric quasi-equilibrium at different global climate model (GCM) resolutions using the simulation of tropical convection by a cloud-resolving model during the Tropical Western Pacific International Cloud Experiment. The simulated dynamic and thermodynamic fields within the model domain are averaged over subdomains of different sizes equivalent to different GCM resolutions. These coarse-grained fields are then used to compute CAPE and its change with time, and their relationships with simulated convection. Results show that CAPE change with time is controlled predominantly by variations of thermodynamic properties in the planetary boundary layer for all subdomain sizes ranging from 64 to 4 km. Lag correlation analysis shows that CAPE generation by the free-tropospheric dynamical advection (dCAPE ls ) leads convective precipitation but is in phase with convective mass flux at 600 mb and 500 mb vertical velocity for all subdomain sizes. However, the correlation coefficients and regression slopes decrease as the subdomain size decreases for subdomain sizes smaller than 16 km. This is probably due to increased randomness of convection and more scale-dependence of the relationships when the subdomain size reaches the grey zone. By examining the sensitivity of the relationships of convection with dCAPE ls to temporal scales in different subdomain size, it shows that the quasi-equilibrium between dCAPE ls and convection holds well for timescales of 30 min or longer at all subdomain sizes. These results suggest that the free tropospheric quasi-equilibrium assumption may still be useable even for GCM resolutions in the grey zone. 

Trends in surface air temperature (SAT) are a common metric for global warming. Using observations and observationally driven models, we show that a more comprehensive metric for global warming and weather extremes is the trend in surface equivalent potential temperature (Thetae_sfc) since it also accounts for the increase in atmospheric humidity and latent energy. From 1980 to 2019, while SAT increased by 0.79 ° C , Thetae_sfc increased by 1.48 ° C globally and as much as 4 ° C in the tropics. The increase in water vapor is responsible for the factor of 2 difference between SAT and Thetae_sfc trends. Thetae_sfc increased more uniformly (than SAT) between the midlatitudes of the southern hemisphere and the northern hemisphere, revealing the global nature of the heating added by greenhouse gases (GHGs). Trends in heat extremes and extreme precipitation are correlated strongly with the global/tropical trends in Thetae_sfc. The tropical amplification of Thetae_sfc is as large as the arctic amplification of SAT, accounting for the observed global positive trends in deep convection and a 20% increase in heat extremes. With unchecked GHG emissions, while SAT warming can reach 4.8 ° C by 2100, the global mean Thetae_sfc can increase by as much as 12 ° C , with corresponding increases of 12 ° C (median) to 24 ° C (5% of grid points) in land surface temperature extremes, a 14- to 30-fold increase in frequency of heat extremes, a 40% increase in the energy available for tropical deep convection, and an up to 60% increase in extreme precipitation. 

Convective parameterization is the long-lasting bottleneck of global climate modelling and one of the most difficult problems in atmospheric sciences. Uncertainty in convective parameterization is the leading cause of the widespread climate sensitivity in IPCC global warming projections. This paper reviews the observations and parameterizations of atmospheric convection with emphasis on the cloud structure, bulk effects, and closure assumption. The representative state-of-the-art convection schemes are presented, including the ECMWF convection scheme, the Grell scheme used in NCEP model and WRF model, the Zhang-MacFarlane scheme used in NCAR and DOE models, and parameterizations of shallow moist convection. The observed convection has self-suppression mechanisms caused by entrainment in convective updrafts, surface cold pool generated by unsaturated convective downdrafts, and warm and dry lower troposphere created by mesoscale downdrafts. The post-convection environment is often characterized by “diamond sounding” suggesting an over-stabilization rather than barely returning to neutral state. Then the pre-convection environment is characterized by slow moistening of lower troposphere triggered by surface moisture convergence and other mechanisms. The over-stabilization and slow moistening make the convection events episodic and decouple the middle/upper troposphere from the boundary layer, making the state-type quasi-equilibrium hypothesis invalid. Right now, unsaturated convective downdrafts and especially mesoscale downdrafts are missing in most convection schemes, while some schemes are using undiluted convective updrafts, all of which favour easily turned-on convection linked to double-ITCZ (inter-tropical convergence zone), overly weak MJO (Madden-Julian Oscillation) and precocious diurnal precipitation maximum. We propose a new strategy for convection scheme development using reanalysis-driven model experiments such as the assimilation runs in weather prediction centres and the decadal prediction runs in climate modelling centres, aided by satellite simulators evaluating key characteristics such as the lifecycle of convective cloud-top distribution and stratiform precipitation fraction. 

ABSTRACT The radiance of sky brightness differs principally with wavelength passband. Atmospheric scattering of sunlight causes the radiation in the near-infrared band. The Antarctic is a singular area of the planet, marked by an unparalleled climate and geographical conditions, including the coldest temperatures and driest climate on Earth, which leads it to be the best candidate site for observing in infrared bands. At present, there are still no measurements of night-sky brightness at DOME A. We have developed the Near-Infrared Sky Brightness Monitor (NISBM) in the J, H, and Ks bands for measurements at DOME A. The instruments were installed at DOME A in 2019 and early results of NIR sky brightness from 2019 January–April have been obtained. The variation of sky background brightness with solar elevation and scanning angle is analysed. The zenith sky flux intensity for the early night at DOME A in the J band is in the 600–1100 μJy arcsec−2 range, that in the H band is between 1100 and 2600 μJy arcsec−2, and that in the Ks band is in the range ∼200–900 μJy arcsec−2. This result shows that the sky brightness in J and H bands is close to that of Ali in China and Mauna Kea in the USA. The sky brightness in the Ks band is much better than that in Ali, China and Mauna Kea, USA. This shows that, from our early results, DOME A is a good site for astronomical observation in the Ks band. 

Abstract The striking resemblance of high multiplicity proton-proton (pp) collisions at the LHC to heavy ion collisions challenges our conventional wisdom on the formation of the quark-gluon plasma (QGP). A consistent explanation of the collectivity phenomena in pp will help us to understand the mechanism that leads to the QGP-like signals in small systems. In this study, we introduce a transport model approach connecting the initial conditions provided by PYTHIA8 with subsequent AMPT rescatterings to study the collective behavior in high energy pp collisions. The multiplicity dependence of light hadron productions from this model is in reasonable agreement with the pp$$\sqrt{s}=13$$ $\sqrt{s}=13$TeV experimental data. It is found in the comparisons that both the partonic and hadronic final state interactions are important for the generation of the radial flow feature of the pp transverse momentum spectra. The study also shows that the long range two particle azimuthal correlation in high multiplicity pp events is sensitive to the proton sub-nucleon spatial fluctuations.