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As a long-standing problem in climate models, large positive shortwave radiation biases exist at the surface over the Southern Ocean, impacting the accurate simulation of sea surface temperature, atmospheric circulation, and precipitation. Underestimations of low-level cloud fraction and liquid water content are suggested to predominantly contribute to these radiation biases. Most model evaluations for radiation focus on summer and rely on satellite products, which have their own limitations. In this work, we use surface-based observations at Macquarie Island to provide the first long-term, seasonal evaluation of both downwelling surface shortwave and longwave radiation in the Australian Community Climate and Earth System Simulator Atmosphere-only Model version 2 (ACCESS-AM2) over the Southern Ocean. The capacity of the Clouds and the Earth’s Radiant Energy System (CERES) product to simulate radiation is also investigated. We utilize the novel lidar simulator, the Automatic Lidar and Ceilometer Framework (ALCF), and all-sky cloud camera observations of cloud fraction to investigate how radiation biases are influenced by cloud properties. Overall, we find an overestimation of +9.5±33.5 W m−2 for downwelling surface shortwave radiation fluxes and an underestimation of -2.3±13.5 W m−2 for downwelling surface longwave radiation in ACCESS-AM2 in all-sky conditions, with more pronounced shortwave biases of +25.0±48.0 W m−2 occurring in summer. CERES presents an overestimation of +8.0±18.0 W m−2 for the shortwave and an underestimation of -12.1±12.2 W m−2 for the longwave in all-sky conditions. For the cloud radiative effect (CRE) biases, there is an overestimation of +4.8±28.0 W m−2 in ACCESS-AM2 and an underestimation of -7.9±20.9 W m−2 in CERES. An overestimation of downwelling surface shortwave radiation is associated with an underestimated cloud fraction and low-level cloud occurrence. We suggest that modeled cloud phase is also having an impact on the radiation biases. Our results show that the ACCESS-AM2 model and CERES product require further development to reduce these radiation biases not just in shortwave and in all-sky conditions, but also in longwave and in clear-sky conditions.

Uncertainty in cloud feedbacks in climate models is a major limitation in projections of future climate. Therefore, evaluation and improvement of cloud simulation are essential to ensure the accuracy of climate models. We analyse cloud biases and cloud change with respect to global mean near-surface temperature (GMST) in climate models relative to satellite observations and relate them to equilibrium climate sensitivity, transient climate response and cloud feedback. For this purpose, we develop a supervised deep convolutional artificial neural network for determination of cloud types from low-resolution (2.5∘×2.5∘) daily mean top-of-atmosphere shortwave and longwave radiation fields, corresponding to the World Meteorological Organization (WMO) cloud genera recorded by human observers in the Global Telecommunication System (GTS). We train this network on top-of-atmosphere radiation retrieved by the Clouds and the Earth’s Radiant Energy System (CERES) and GTS and apply it to the Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) model output and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5) and the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) reanalyses. We compare the cloud types between models and satellite observations. We link biases to climate sensitivity and identify a negative linear relationship between the root mean square error of cloud type occurrence derived from the neural network and model equilibrium climate sensitivity (ECS), transient climate response (TCR) and cloud feedback. This statistical relationship in the model ensemble favours models with higher ECS, TCR and cloud feedback. However, this relationship could be due to the relatively small size of the ensemble used or decoupling between present-day biases and future projected cloud change. Using the abrupt-4×CO2 CMIP5 and CMIP6 experiments, we show that models simulating decreasing stratiform and increasing cumuliform clouds tend to have higher ECS than models simulating increasing stratiform and decreasing cumuliform clouds, and this could also partially explain the association between the model cloud type occurrence error and model ECS.

Cloud and aerosol lidars measuring backscatter and depolarization ratio are the most suitable lidars to detect cloud phase (liquid, ice, or mixed phase). However, such instruments are not widely deployed as part of operational networks. In this study, we propose a new algorithm to detect supercooled liquid water containing clouds (SLCC) based on ceilometers measuring only co-polarization backscatter. We utilize observations collected at Davis, Antarctica, where low-level, mixed-phase clouds, including supercooled liquid water (SLW) droplets and ice crystals, remain poorly understood due to the paucity of ground-based observations. A 3-month set of observations were collected during the austral summer of November 2018 to February 2019, with a variety of instruments including a depolarization lidar and a W-band cloud radar which were used to build a two-dimensional cloud phase mask distinguishing SLW and mixed-phase clouds. This cloud phase mask is used as the reference to develop a new algorithm based on the observations of a single polarization ceilometer operating in the vicinity for the same period. Deterministic and data-driven retrieval approaches were evaluated: an extreme gradient boosting (XGBoost) framework ingesting backscatter average characteristics was the most effective method at reproducing the classification obtained with the combined radar–lidar approach with an accuracy as high as 0.91. This study provides a new SLCC retrieval approach based on ceilometer data and highlights the considerable benefits of these instruments to provide intelligence on cloud phase in polar regions that usually suffer from a paucity of observations. Finally, the two algorithms were applied to a full year of ceilometer observations to retrieve cloud phase and frequency of occurrences of SLCC: SLCC was present 29 ± 6 % of the time for T19 and 24 ± 5 % of the time for G22-Davis over that annual cycle.

Due to its remote location and extreme weather conditions, atmospheric in situ measurements are rare in the Southern Ocean. As a result, aerosol–cloud interactions in this region are poorly understood and remain a major source of uncertainty in climate models. This, in turn, contributes substantially to persistent biases in climate model simulations such as the well-known positive shortwave radiation bias at the surface, as well as biases in numerical weather prediction models and reanalyses. It has been shown in previous studies that in situ and ground-based remote sensing measurements across the Southern Ocean are critical for complementing satellite data sets due to the importance of boundary layer and low-level cloud processes. These processes are poorly sampled by satellite-based measurements and are often obscured by multiple overlying cloud layers. Satellite measurements also do not constrain the aerosol–cloud processes very well with imprecise estimation of cloud condensation nuclei. In this work, we present a comprehensive set of ship-based aerosol and meteorological observations collected on the 6-week Southern Ocean Ross Sea Marine Ecosystem and Environment voyage (TAN1802) voyage of RV Tangaroa across the Southern Ocean, from Wellington, New Zealand, to the Ross Sea, Antarctica. The voyage was carried out from 8 February to 21 March 2018. Many distinct, but contemporaneous, data sets were collected throughout the voyage. The compiled data sets include measurements from a range of instruments, such as (i) meteorological conditions at the sea surface and profile measurements; (ii) the size and concentration of particles; (iii) trace gases dissolved in the ocean surface such as dimethyl sulfide and carbonyl sulfide; (iv) and remotely sensed observations of low clouds. Here, we describe the voyage, the instruments, and data processing, and provide a brief overview of some of the data products available. We encourage the scientific community to use these measurements for further analysis and model evaluation studies, in particular, for studies of Southern Ocean clouds, aerosol, and their interaction. The data sets presented in this study are publicly available at https://doi.org/10.5281/zenodo.4060237 (Kremser et al., 2020).

MAPM (Mapping Air Pollution eMissions) is a project whose goal is to develop a method to infer airborne particulate matter (PM) emissions maps from in situ PM concentration measurements. In support of MAPM, a winter field campaign was conducted in New Zealand in 2019 (June to September) to obtain the measurements required to test and validate the MAPM methodology. Two different types of instruments measuring PM were deployed: ES-642 remote dust monitors (17 instruments) and Outdoor Dust Information Nodes (ODINs; 50 instruments). The measurement campaign was bracketed by two intercomparisons where all instruments were co-located, with a permanently installed tapered element oscillating membrane (TEOM) instrument, to determine any instrument biases. Changes in biases between the pre- and post-campaign intercomparisons were used to determine instrument drift over the campaign period. Once deployed, each ES-642 was co-located with an ODIN. In addition to the PM measurements, meteorological variables (temperature, pressure, wind speed, and wind direction) were measured at three automatic weather station (AWS) sites established as part of the campaign, with additional data being sourced from 27 further AWSs operated by other agencies. Vertical profile measurements were made with 12 radiosondes during two 24 h periods and complimented measurements made with a mini micropulse lidar and ceilometer. Here we present the data collected during the campaign and discuss the correction of the measurements made by various PM instruments. We find that when compared to measurements made with a simple linear correction, a correction based on environmental conditions improves the quality of measurements retrieved from ODINs but results in over-fitting and increases the uncertainties when applied to the more sophisticated ES-642 instruments. We also compare PM2.5 and PM10 measured by ODINs which, in some cases, allows us to identify PM from natural and anthropogenic sources. The PM data collected during the campaign are publicly available from https://doi.org/10.5281/zenodo.4542559 (Dale et al., 2020b), and the data from other instruments are available from https://doi.org/10.5281/zenodo.4536640 (Dale et al., 2020a).

Automatic lidars and ceilometers (ALCs) provide valuable information on cloud and aerosols but have not been systematically used in the evaluation of general circulation models (GCMs) and numerical weather prediction (NWP) models. Obstacles associated with the diversity of instruments, a lack of standardisation of data products and open processing tools mean that the value of large ALC networks worldwide is not being realised. We discuss a tool, called the Automatic Lidar and Ceilometer Framework (ALCF), that overcomes these problems and also includes a ground-based lidar simulator, which calculates the radiative transfer of laser radiation and allows one-to-one comparison with models. Our ground-based lidar simulator is based on the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP), which has been extensively used for spaceborne lidar intercomparisons. The ALCF implements all steps needed to transform and calibrate raw ALC data and create simulated attenuated volume backscattering coefficient profiles for one-to-one comparison and complete statistical analysis of clouds. The framework supports multiple common commercial ALCs (Vaisala CL31, CL51, Lufft CHM 15k and Droplet Measurement Technologies MiniMPL), reanalyses (JRA-55, ERA5 and MERRA-2) and models (the Unified Model and AMPS – the Antarctic Mesoscale Prediction System). To demonstrate its capabilities, we present case studies evaluating cloud in the supported reanalyses and models using CL31, CL51, CHM 15k and MiniMPL observations at three sites in New Zealand. We show that the reanalyses and models generally underestimate cloud fraction. If sufficiently high-temporal-resolution model output is available (better than 6-hourly), a direct comparison of individual clouds is also possible. We demonstrate that the ALCF can be used as a generic evaluation tool to examine cloud occurrence and cloud properties in reanalyses, NWP models, and GCMs, potentially utilising the large amounts of ALC data already available. This tool is likely to be particularly useful for the analysis and improvement of low-level cloud simulations which are not well monitored from space. This has previously been identified as a critical deficiency in contemporary models, limiting the accuracy of weather forecasts and future climate projections. While the current focus of the framework is on clouds, support for aerosol in the lidar simulator is planned in the future.

Southern Ocean (SO) shortwave (SW) radiation biases are a common problem in contemporary general circulation models (GCMs), with most models exhibiting a tendency to absorb too much incoming SW radiation. These biases have been attributed to deficiencies in the representation of clouds during the austral summer months, either due to cloud cover or cloud albedo being too low. The problem has been the focus of many studies, most of which utilised satellite datasets for model evaluation. We use multi-year ship-based observations and the CERES spaceborne radiation budget measurements to contrast cloud representation and SW radiation in the atmospheric component Global Atmosphere (GA) version 7.1 of the HadGEM3 GCM and the MERRA-2 reanalysis. We find that the prevailing bias is negative in GA7.1 and positive in MERRA-2. GA7.1 performs better than MERRA-2 in terms of absolute SW bias. Significant errors of up to 21 W m−2 (GA7.1) and 39 W m−2 (MERRA-2) are present in both models in the austral summer. Using ship-based ceilometer observations, we find low cloud below 2 km to be predominant in the Ross Sea and the Indian Ocean sectors of the SO. Utilising a novel surface lidar simulator developed for this study, derived from an existing Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) – active remote sensing simulator (ACTSIM) spaceborne lidar simulator, we find that GA7.1 and MERRA-2 both underestimate low cloud and fog occurrence relative to the ship observations on average by 4 %–9 % (GA7.1) and 18 % (MERRA-2). Based on radiosonde observations, we also find the low cloud to be strongly linked to boundary layer atmospheric stability and the sea surface temperature. GA7.1 and MERRA-2 do not represent the observed relationship between boundary layer stability and clouds well. We find that MERRA-2 has a much greater proportion of cloud liquid water in the SO in austral summer than GA7.1, a likely key contributor to the difference in the SW radiation bias. Our results suggest that subgrid-scale processes (cloud and boundary layer parameterisations) are responsible for the bias and that in GA7.1 a major part of the SW radiation bias can be explained by cloud cover underestimation, relative to underestimation of cloud albedo.

We use the 2B-GEOPROF-LIDAR R04 (2BGL4) and R05 (2BGL5) products and the 2B-CLDCLASS-LIDAR R04 (2BCL4) product, all generated by combining CloudSat radar and CALIPSO lidar satellite measurements with auxiliary data, to examine the vertical distribution of cloud occurrence around the Ross Ice Shelf (RIS) and Ross Sea region. We find that the 2BGL4 product, used in previous studies in this region, displays a discontinuity at 8.2 km which is not observable in the other products. This artefact appears to correspond to a change in the horizontal and vertical resolution of the CALIPSO dataset used above this level. We then use the 2BCL4 product to examine the vertical distribution of cloud occurrence, phase, and type over the RIS and Ross Sea. In particular we examine how synoptic conditions in the region, derived using a previously developed synoptic classification, impact the cloud environment and the contrasting response in the two regions. We observe large differences between the cloud occurrence as a function of altitude for synoptic regimes relative to those for seasonal variations. A stronger variation in the occurrence of clear skies and multi-layer cloud and in all cloud type occurrences over both the Ross Sea and RIS is associated more with synoptic type than seasonal composites. In addition, anomalies from the mean joint histogram of cloud top height against thickness display significant differences over the Ross Sea and RIS sectors as a function of synoptic regime, but are near identical over these two regions when a seasonal analysis is completed. However, the frequency of particular phases of cloud, notably mixed phase and water, is much more strongly modulated by seasonal than synoptic regime compositing, which suggests that temperature is still the most important control on cloud phase in the region.

Background & aimsMetabolism supports cell proliferation and growth. Surprisingly, the tumor suppressor miR-22 is induced by metabolic stimulators like bile acids. Thus, this study examines whether miR-22 could be a metabolic silencer.MethodsThe relationship between miR-22 and the expression of fibroblast growth factor 21 (FGF21) and its receptor FGFR1 was studied in cells and fatty livers obtained from patients and mouse models. We evaluated the effect of an miR-22 inhibitor alone and in combination with obeticholic acid (OCA) for the treatment of steatosis.ResultsThe levels of miR-22 were inversely correlated with those of FGF21, FGFR1, and PGC1α in human and mouse fatty livers, suggesting that hepatic miR-22 acts as a metabolic silencer. Indeed, miR-22 reduced FGFR1 by direct targeting and decreased FGF21 by reducing the recruitment of PPARα and PGC1α to their binding motifs. In contrast, an miR-22 inhibitor increases hepatic FGF21 and FGFR1, leading to AMPK and ERK1/2 activation, which was effective in treating alcoholic steatosis in mouse models. The farnesoid x receptor-agonist OCA induced FGF21 and FGFR1, as well as their inhibitor miR-22. An miR-22 inhibitor and OCA were effective in treating diet-induced steatosis, both alone and in combination. The combined treatment was the most effective at improving insulin sensitivity, releasing glucagon-like peptide 1, and reducing hepatic triglyceride in obese mice.ConclusionThe simultaneous induction of miR-22, FGF21 and FGFR1 by metabolic stimulators may maintain FGF21 homeostasis and restrict ERK1/2 activation. Reducing miR-22 enhances hepatic FGF21 and activates AMPK, which could be a novel approach to treat steatosis and insulin resistance.Lay summaryThis study examines the metabolic role of a tumor suppressor, miR-22, that can be induced by metabolic stimulators such as bile acids. Our novel data revealed that the metabolic silencing effect of miR-22 occurs as a result of reductions in metabolic stimulators, which likely contribute to the development of fatty liver. Consistent with this finding, an miR-22 inhibitor effectively reversed both alcohol- and diet-induced fatty liver; miR-22 inhibition is a promising therapeutic option which could be used in combination with obeticholic acid.