Strong pattern effect evident in Southern Ocean cloud feedback based on multiple lines of evidence

Global storm resolving models (GSRMs) represent the next generation of global climate models. One of them is a 5‐km Icosahedral Nonhydrostatic Weather and Climate Model (ICON). Its high resolution means that parameterizations of convection and clouds, including subgrid‐scale clouds, are omitted, relying on explicit simulation but necessarily utilizing microphysics and turbulence parameterizations. Standard‐resolution (10–100 km) models, which use convection and cloud parameterizations, have substantial cloud biases over the Southern Ocean (SO), adversely affecting radiation and sea surface temperature. The SO is dominated by low clouds, which cannot be observed accurately from space due to overlapping clouds, attenuation, and ground clutter. We evaluated SO clouds in ICON and the ERA5 and MERRA‐2 reanalyzes using approximately 2400 days of lidar observations and 2300 radiosonde profiles from 31 voyages and a Macquarie Island station during 2010–2021, compared to the model and reanalyzes using a ground‐based lidar simulator. We found that ICON and the reanalyzes underestimate the total cloud fraction by about 10% and 20%, respectively. ICON and ERA5 overestimate the cloud occurrence peak at about 500 m, associated with underestimated lower tropospheric stability and overestimated lifting condensation level. The reanalyzes strongly underestimate fog and very low‐level clouds, and MERRA‐2 underestimates cloud occurrence at almost all heights. Outgoing shortwave radiation is overestimated in MERRA‐2, implying a “too few, too bright” cloud problem. SO cloud and fog biases are a substantial issue in the analyzed model and reanalyzes and result in shortwave and longwave radiation biases.

Accurate representation of marine boundary layer (MBL) cloud and drizzle microphysics is essential for improving cloud–radiation feedback in models and satellite retrievals. This study derived empirical relationships to estimate cloud and drizzle microphysical properties—effective radius ( r e ) and liquid water content (LWC)—from reflectivity ( Z dB Z ) using aircraft measurements during the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) campaign. In situ cloud and drizzle droplet size distributions (DSDs) were measured by the cloud droplet probe (CDP; 2–40 μ m) and two-dimensional stereo (2D-S) probe (40–400 μ m) onboard aircraft and analyzed assuming lognormal and gamma size distributions, respectively. Empirical relationships r e = A exp(0.0384 × Z dB Z ) and LWC = B exp(0.115 × Z dB Z ) were derived, with coefficients A = 23.02 ± 2.4 and B = 2.70 ± 0.95 for clouds and A = 49.81 ± 3.5 and B = 0.15 ± 0.11 for drizzle. Sensitivity analyses show that A and B vary strongly with reflectivity but only weakly with droplet number or size distribution. The derived relationships were applied to the 94-GHz High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Cloud Radar (HCR) reflectivity measurements during SOCRATES. The retrieved r e and LWC values show excellent agreement with in situ estimates, exhibiting mean differences below 10%. Further implementation using Marine W-Band Atmospheric Radiation Measurement (ARM) Cloud Radar (M-WACR) reflectivity measurements during the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) campaign yielded results consistent with existing retrieval approaches (mean differences < 20%). These findings demonstrate that the derived empirical relationships provide a reliable and broadly applicable framework for retrieving MBL cloud and drizzle properties across different observational platforms and marine climatic regimes. Significance Statement Understanding cloud and drizzle microphysics is essential for improving climate model predictions, which are sensitive to regional conditions, observation methods, and instrument characteristics. Since cloud and drizzle droplets follow distinct size distribution patterns, accurate retrievals require case-specific analyses. In this study, we analyze droplet size distributions from in situ cloud and drizzle probes onboard research aircraft during the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) campaign over the Southern Ocean. Empirical relationships linking effective radius and liquid water content to radar reflectivity are derived, providing a useful reference for retrievals across different observational platforms and microphysical regimes. The results show that microphysical properties vary systematically with reflectivity, supporting the use of radar measurements to derive time-resolved vertical profiles of cloud and drizzle structure.

Marine stratocumuli are the most dominant cloud type by area coverage in the Southern Ocean (SO). They can be divided into different self-organized cellular morphological regimes known as open and closed mesoscale-cellular convec- tive (MCC) clouds. Open and closed cells are the two most frequent types of organizational regimes in the SO. Using the liDAR- raDAR (DARDAR) version 2 retrievals, we quantify 59 % of all MCC clouds in this region as mixed-phase clouds (MPCs) during a 4-year time period from 2007 to 2010. The net radiative effect of SO MCC clouds is governed by changes in cloud albedo. Both, cloud morphology and phase, have previously been shown to impact cloud albedo individually, but their interac- tions and their combined impact on cloud albedo remain unclear. Here, we investigate the relationships between cloud phase, organizational patterns, and their differences regarding their cloud radiative properties in the SO. The mixed-phase fraction, which is defined as the number of MPCs divided by the sum of MPC and supercooled liquid cloud (SLC) pixels, of all MCC clouds at a given cloud-top temperature (CTT) varies considerably between austral summer and winter. We further find that seasonal changes in cloud phase at a given CTT across all latitudes are largely independent of cloud morphology and are thus seemingly constrained by other external factors. Overall, our results show a stronger dependence of cloud phase on cloud-top height (CTH) than CTT for clouds below 2.5 km in altitude. Preconditioning through ice-phase processes in MPCs has been observed to accelerate individual closed to open cell transitions in extratropical stratocumuli. The hypothesis of preconditioning has been further substantiated in large-eddy simulations of open and closed MPCs. In this study, we do not find preconditioning to primarily impact climatological SO cloud mor- phology statistics. Meanwhile, in-cloud albedo analysis reveals stronger changes in open and closed cell albedo in SLCs than MPCs. In particular few optically thick (cloud optical thickness > 10) open cell stratocumuli are characterized as ice-free SLCs. Theses differences in in-cloud albedo are found to alter the cloud radiative effect in the SO by 12 W m−2 to 39 W m−2 depending on season and cloud phase.

Marine stratocumuli are the most dominant cloud type by area coverage in the Southern Ocean (SO). They can be divided into different self-organized cellular morphological regimes known as open and closed mesoscale-cellular convec- tive (MCC) clouds. Open and closed cells are the two most frequent types of organizational regimes in the SO. Using the liDAR- raDAR (DARDAR) version 2 retrievals, we quantify 59 % of all MCC clouds in this region as mixed-phase clouds (MPCs) during a 4-year time period from 2007 to 2010. The net radiative effect of SO MCC clouds is governed by changes in cloud albedo. Both, cloud morphology and phase, have previously been shown to impact cloud albedo individually, but their interac- tions and their combined impact on cloud albedo remain unclear. Here, we investigate the relationships between cloud phase, organizational patterns, and their differences regarding their cloud radiative properties in the SO. The mixed-phase fraction, which is defined as the number of MPCs divided by the sum of MPC and supercooled liquid cloud (SLC) pixels, of all MCC clouds at a given cloud-top temperature (CTT) varies considerably between austral summer and winter. We further find that seasonal changes in cloud phase at a given CTT across all latitudes are largely independent of cloud morphology and are thus seemingly constrained by other external factors. Overall, our results show a stronger dependence of cloud phase on cloud-top height (CTH) than CTT for clouds below 2.5 km in altitude. Preconditioning through ice-phase processes in MPCs has been observed to accelerate individual closed to open cell transitions in extratropical stratocumuli. The hypothesis of preconditioning has been further substantiated in large-eddy simulations of open and closed MPCs. In this study, we do not find preconditioning to primarily impact climatological SO cloud mor- phology statistics. Meanwhile, in-cloud albedo analysis reveals stronger changes in open and closed cell albedo in SLCs than MPCs. In particular few optically thick (cloud optical thickness > 10) open cell stratocumuli are characterized as ice-free SLCs. Theses differences in in-cloud albedo are found to alter the cloud radiative effect in the SO by 12 W m−2 to 39 W m−2 depending on season and cloud phase.

Large-scale models struggle to accurately represent maritime low-level boundary layer clouds, leading to uncertainties in projecting a future climate. This study uses a regime-based approach to assess global climate model representation of warm-phase microphysical processes over the Southern Ocean (SO) and northeast Pacific, regions frequently characterized by the stratocumulus-to-cumulus transition (SCT) regime. In situ aircraft observations collected during the Southern Ocean Clouds, Radiation and Aerosol Transport Experimental Study (SOCRATES) and the Cloud Systems Evolution in the Trades (CSET) campaigns were used to evaluate simulated low-level cloud microphysical properties. Using environmental variables and airborne remote sensing, we investigate methods for compositing aircraft observations into stratocumulus, open-cell, and undetermined cloud sampling regimes. This approach aims to mitigate issues with scaling aircraft observations to model grid resolutions and discrepancies with collocating aircraft observations with simulated clouds. We assessed simulated warm-phase cloud processes in the Community Earth System Model (CESM) using new model diagnostics tools to improve our representation of the SCT regime, and results from both CESM2 and CESM3 will be presented.

The global ocean has taken up an estimated 89% of excess heat and 26% of annual CO2 emissions resulting from anthropogenic activity in recent decades. Despite the crucial role of the ocean in the climate system, there remains significant uncertainty around ocean heat and carbon uptake. Processes of ocean carbon and heat uptake and redistribution are among the leading processes limiting scientists’ ability to predict the rate and magnitude of global climate change. Furthermore, despite being responsible for as much as 40–50% of global annual oceanic CO2 uptake, the Southern Ocean remains the most controversial ocean basin, with large differences in both the magnitude and variability of CO2 fluxes across various estimation methods. The disagreement regarding the magnitude and variability of Southern Ocean CO2 fluxes largely arises due to the sparsity and uneven distribution of in-situ observations of carbon in the ocean, a problem which also constrains estimates of ocean heat uptake. However, machine learning has been extensively demonstrated in recent years to be an effective tool for overcoming such data limitations through gap-filling methods.We leverage existing expertise in machine learning methods from generating the SOM-FFN and MOBO-DIC ocean carbon data products and apply this expertise to the development of a novel machine learning generated ocean heat uptake data product for the Southern Ocean. We integrate new Earth Observation data from Copernicus satellites with in-situ observation data from the Southern Ocean as input for our new data products. Here, we present a beta-version of our new machine-learning based products of carbon and heat uptake in the Southern Ocean and a first analysis of the variability and trends of the carbon and heat uptake of these data products. This work presents our first steps towards data products which will allow us to identify transport pathways of carbon and heat within the ocean interior. The novel machine learning-based products will also be used to support the advancement of Earth System Models and climate change predictions.

<p>Together with imminent climate action, building a sustainable future for the humanity requires striving for healthier environments. Atmospheric aerosol particles (also referred to as particulate matter, PM) play a key role in defining the air that the future generations will breathe but also the climates they live in, PM being an important short-lived climate forcer but also a key component of air quality and global environmental health hazard. The contribution of aerosol particles has been a key uncertainty in estimates of the Earth’s radiative forcing since the establishment of the Intergovernmental Panel for Climate Change (IPCC) and still remains as the single largest quoted source of uncertainty in the anthropogenic climate forcing during the industrial period. In the latest assessment by the IPCC, the radiative forcing by aerosol particles has been estimated to be -0.45 W m-2 (between -0.95 and 0.05 W m-2) for aerosol-radiation interactions (RFari) and -0.45 W m-2 (between -1.25 and 0 W m-2) for aerosol-cloud interactions (RFaci). Recent reviews indicate no significant reduction in the uncertainty. The large range of possible aerosol forcing values has serious consequences for climate projections and therefore developing strategies for reaching Paris agreement targets. It is currently not possible to say if a reduction in aerosol emissions due to air pollution mitigation and a phase-out of aerosol emissions will result in a noticeable increase in global mean temperature  or in a negligible climate effect. We will discuss the components and reasons of this uncertainty, focusing on those that are important for aerosol-cloud interactions. We will identify critical bottlenecks in 1) scientific understanding of fundamental aerosol and cloud microphysical processes; 2) method development for improving the understanding of aerosol, cloud, and aerosol-cloud processes as well as their representation in Earth System Models (ESMs); and 3) knowledge transfer within and between the relevant research communities. We will argue for key actions to overcome these bottlenecks, giving examples of good practices for breaking new ground in this long-standing problem that continues to intrigue the atmospheric and climate science communities. Besides enhancing the scientific understanding of the Earth system within the realm of natural science based on multiple lines of evidence, and developing novel (e.g. machine learning –based) methodologies for analyzing existing observational data and model output, we call for additional perspectives from social science and humanities on the communication and knowledge transfer practices within atmospheric and climate research. Making the relevant knowledge transfer pathways and processes transparent is urgently needed to enable systematic determination of the actions required to maximally utilize the existing knowledge, and to ensure effective implementation of new results that may help to narrow down the uncertainty associated with aerosols in climate projections. Improved understanding of the role of aerosols in the climate system will result in enhanced credibility of ESMs and hence also tighter constraints for policies aiming for simultaneous climate neutrality and zero pollution targets.</p>

Clouds are a major source of uncertainty in climate model projections over the Southern Ocean and Antarctica1. The inaccurate representation of clouds in climate models results in biases in the net radiative balance which has knock-on effects on the ability of models to represent sea surface temperatures, ocean heat uptake, sea ice cover, and ultimately large-scale circulation in the Southern Hemisphere2,3,4,5,6. Evidence suggests that this is due to the poor representation of mixed phase clouds in models—the dominant cloud type in this region. As part of the Southern Ocean Clouds project, we have conducted two flying campaigns out of Rothera research station which is located on the Antarctic peninsula, in order to investigate the composition of clouds over the Southern Ocean. Over the course of two field seasons (one in the 2022-23, and one in the 2024-25 Antarctic season) we performed more then 40 flights consisting of over 140 flying hours. During these flights we measured ice crystal, water droplet and aerosol number concentrations and sizes. We also collected Ice Nucleating Particles on filters in addition to performing measurements of meteorological parameters, turbulence, and radiative balance.  Here we will present an overview of the flying campaign of the 2024-25 season, and compare the observations conducted this year to those which were made during our previous campaign in the 2022-23 season. Although we saw higher droplet number concentrations for the 2024-25 campaign than during the 2022-23 campaign, both revealed the presence of higher droplet number concentrations at higher altitudes (> 2000 m asl) indicating a potential long range source for these.  1 Bodas-Salcedo, A., et al., 2014: Origins of the Solar Radiation Biases over the Southern Ocean in CFMIP2 Models. J. Climate, 27, 41–56, https://doi.org/10.1175/JCLI-D-13-00169.1.  2 Lauer, A., et al., 2018: Process-level improvements in CMIP5 models and their impact on tropical variability, the Southern Ocean and monsoons. Earth Syst. Dynam., 9, 33–67, https://doi.org/10.5194/esd-9-33-2018.  3 Frölicher, T. L., et al., 2015: Dominance of the Southern Ocean in Anthropogenic Carbon and Heat Uptake in CMIP5 Models. J. Climate, 28, 862–886, https://doi.org/10.1175/JCLI-D-14-00117.1.  4 Ferrari and Ferreira 2011: what processes drive the ocean heat transport? Ocean. Model., 38, 171-186, https://doi.org/10.1016/j.ocemod.2011.02.013.  5: Ceppi P., et al., 2012: Southern Hemisphere Jet latitude biases in CMIP5 models linked to shortwave cloud forcing. Geophys. Res. Lett, 39, 19: https://doi.org/10.1029/2012GL053115.  6 Y. Hwang, D.M.W. Frierson, 2013: Link between the double-Intertropical Convergence Zone problem and cloud biases over the Southern Ocean, Proc. Natl. Acad. Sci. U.S.A., 110 (13) 4935-4940, https://doi.org/10.1073/pnas.1213302110 

Southern Ocean primary productivity plays a key role in global ocean biogeochemistry and climate. At the Southern Ocean sea ice edge in coastal McMurdo Sound, we observed simultaneous cobalamin and iron limitation of surface water phytoplankton communities in late Austral summer. Cobalamin is produced only by bacteria and archaea, suggesting phytoplankton-bacterial interactions must play a role in this limitation. To characterize these interactions and investigate the molecular basis of multiple nutrient limitation, we examined transitions in global gene expression over short time scales, induced by shifts in micronutrient availability. Diatoms, the dominant primary producers, exhibited transcriptional patterns indicative of co-occurring iron and cobalamin deprivation. The major contributor to cobalamin biosynthesis gene expression was a gammaproteobacterial population, Oceanospirillaceae ASP10-02a. This group also contributed significantly to metagenomic cobalamin biosynthesis gene abundance throughout Southern Ocean surface waters. Oceanospirillaceae ASP10-02a displayed elevated expression of organic matter acquisition and cell surface attachment-related genes, consistent with a mutualistic relationship in which they are dependent on phytoplankton growth to fuel cobalamin production. Separate bacterial groups, including Methylophaga, appeared to rely on phytoplankton for carbon and energy sources, but displayed gene expression patterns consistent with iron and cobalamin deprivation. This suggests they also compete with phytoplankton and are important cobalamin consumers. Expression patterns of siderophore- related genes offer evidence for bacterial influences on iron availability as well. The nature and degree of this episodic colimitation appear to be mediated by a series of phytoplankton-bacterial interactions in both positive and negative feedback loops.

Abstract. The Southern Ocean has a profound impact on the Earth's climate system. Its strong winds, intense currents, and fierce waves are critical components of the air–sea interface and contribute to absorbing, storing, and releasing heat, moisture, gases, and momentum. Owing to its remoteness and harsh environment, this region is significantly undersampled, hampering the validation of prediction models and large-scale observations from satellite sensors. Here, an unprecedented data set of simultaneous observations of winds, surface currents, and ocean waves is presented, to address the scarcity of in situ observations in the region – https://doi.org/10.26179/5ed0a30aaf764 (Alberello et al., 2020c) and https://doi.org/10.26179/5e9d038c396f2 (Derkani et al., 2020). Records were acquired underway during the Antarctic Circumnavigation Expedition (ACE), which went around the Southern Ocean from December 2016 to March 2017 (Austral summer). Observations were obtained with the wave and surface current monitoring system WaMoS-II, which scanned the ocean surface around the vessel using marine radars. Measurements were assessed for quality control and compared against available satellite observations. The data set is the most extensive and comprehensive collection of observations of surface processes for the Southern Ocean and is intended to underpin improvements of wave prediction models around Antarctica and research of air–sea interaction processes, including gas exchange and dynamics of sea spray aerosol particles. The data set has further potentials to support theoretical and numerical research on lower atmosphere, air–sea interface, and upper-ocean processes.

The Southern Ocean is a major component within the global ocean and climate system and potentially the location where the most rapid climate change is most likely to happen, particularly in the high-latitude polar regions. In these regions, even small temperature changes can potentially lead to major environmental perturbations. Climate change is likely to be regional and may be expressed in various ways, including alterations to climate and weather patterns across a variety of time-scales that include changes to the long interdecadal background signals such as the development of the El Niño-Southern Oscillation (ENSO). Oscillating climate signals such as ENSO potentially provide a unique opportunity to explore how biological communities respond to change. This approach is based on the premise that biological responses to shorter-term sub-decadal climate variability signals are potentially the best predictor of biological responses over longer time-scales. Around the Southern Ocean, marine predator populations show periodicity in breeding performance and productivity, with relationships with the environment driven by physical forcing from the ENSO region in the Pacific. Wherever examined, these relationships are congruent with mid-trophic-level processes that are also correlated with environmental variability. The short-term changes to ecosystem structure and function observed during ENSO events herald potential long-term changes that may ensue following regional climate change. For example, in the South Atlantic, failure of Antarctic krill recruitment will inevitably foreshadow recruitment failures in a range of higher trophic-level marine predators. Where predator species are not able to accommodate by switching to other prey species, population-level changes will follow. The Southern Ocean, though oceanographically interconnected, is not a single ecosystem and different areas are dominated by different food webs. Where species occupy different positions in different regional food webs, there is the potential to make predictions about future change scenarios.

The Intergovernmental Panel on Climate Change (IPCC) assesses the sensitivity of the climate system to increases in greenhouse gas concentrations using multiple lines of evidence, covering paleoclimate data, historical observations, and numerical Earth system model (ESM) simulations. Within IPCC’s latest Assessment Report (AR6), there is, for the first time, a non-negligible difference between the most likely rate of warming estimated in the report and the average warming rate simulated by the ESMs that participated in the Coupled Model Intercomparison Project (CMIP6). This discrepancy occurs because a large number of CMIP6 models have projected future warming rates that are higher than previously reported but quite unlikely according to historical observations. The consequence is that using a random selection of CMIP6 simulations is likely to overestimate historical and future warming (compared to what is assessed in the IPCC report), potentially leading to avoidable inconsistencies when compared to observations or greater projected changes compared to what could be inferred from CMIP5.As this constitutes a wide-spread obstacle and limitation to using CMIP6 simulations ‘out of the box’, we propose here a simple model weighting method with the objective to address this problem. Our approach can be used to 1) evaluate the extent to which any given set of CMIP6 simulations is consistent with IPCC-assessed warming rates and 2) calculate the appropriate model weights so that potential inconsistencies are reduced as much as possible. The calculation of the weights is solely based on the user’s selection of a CMIP6 subset and does not require any data manipulation. The weights can then be easily implemented in existing analyses to calculate weighted (i.e. instead of just arithmetic) multi-model means, weighted quantiles, etc. We demonstrate the interest and flexibility of the method with some examples, including global to regional assessments of historical and projected changes in temperature and precipitation. We illustrate the extent to which applying model weights can reconcile otherwise divergent scientific results and provide assessments that are more robust across CMIP generations.

Although the Southern Ocean is thought to account for a significant portion of the contemporary oceanic uptake of carbon dioxide (CO2), flux estimates in this region are based on sparse observations that are strongly biased toward summer. Here we present new estimates of Southern Ocean air‐sea CO2 fluxes calculated with measurements from biogeochemical profiling floats deployed by the Southern Ocean Carbon and Climate Observations and Modeling project during 2014–2017. Compared to ship‐based CO2 flux estimates, the float‐based fluxes find significantly stronger outgassing in the zone around Antarctica where carbon‐rich deep waters upwell to the surface ocean. Although interannual variability contributes, this difference principally stems from the lack of autumn and winter ship‐based observations in this high‐latitude region. These results suggest that our current understanding of the distribution of oceanic CO2 sources and sinks may need revision and underscore the need for sustained year‐round biogeochemical observations in the Southern Ocean.

The recent publication of the summary for policy makers by Working Group I of the Intergovernmental Panel on Climate Change (IPCC) [1] has injected a renewed sense of urgency to address climate change. It is therefore timely to review the notion of preventing 'dangerous anthropogenic interference with the climate system' as put forward in the United Nations Framework Convention on Climate Change (UNFCCC). The article by Danny Harvey in this issue [2] offers a fresh perspective by rephrasing the concept of 'dangerous interference' as a problem of risk assessment. As Harvey points out, identification of 'dangerous interference' does not require us to know with certainty that future climate change will be dangerous—an impossible task given that our knowledge about future climate change includes uncertainty. Rather, it requires the assertion that interference would lead to a significant probability of dangerous climate change beyond some risk tolerance, and therefore would pose an unacceptable risk.

In the Southern Ocean, strong eastward ocean jets interact with large topographic features, generating eddies that feed back onto the mean flow. Deep-reaching eddies interact with topography, where turbulent dissipation and generation of internal lee waves play an important role in the ocean’s energy budget. However, eddy effects in the deep ocean are difficult to observe and poorly characterized. This study investigates the energy contained in eddies at depth, when an ocean jet encounters topography. This study uses a two-layer ocean model in which an imposed unstable jet encounters a topographic obstacle (either a seamount or a meridional ridge) in a configuration relevant to an Antarctic Circumpolar Current frontal jet. The authors find that the presence of topography increases the eddy kinetic energy (EKE) at depth but that the dominant processes generating this deep EKE depend on the shape and height of the obstacle as well as on the baroclinicity of the jet before it encounters topography. In cases with high topography, horizontal shear instability is the dominant source of deep EKE, while a flat bottom or a strongly sheared inflow leads to deep EKE being generated primarily through baroclinic instability. These results suggest that the deep EKE is set by an interplay between the inflowing jet properties and topography and imply that the response of deep EKE to changes in the Southern Ocean circulation is likely to vary across locations depending on the topography characteristics.