Climate Model Parameters Research Articles

Observations of a Filamentous Intrusion and Vigorous Submesoscale Turbulence within a Cyclonic Mesoscale Eddy

Abstract Oceanic submesoscale flows are considered to be a crucial conduit for the downscale transfer of oceanic mesoscale kinetic energy and upper-ocean material exchange, both laterally and vertically, but defining observations revealing submesoscale dynamics and/or transport properties remain sparse. Here, we report on an elaborate observation of a warm and fresh filament intruding into a cyclonic mesoscale eddy. By integrating cruise measurements, satellite observations, particle-tracking simulations, and the trajectory of a surface drifter, we show that the filament originated from an anticyclonic eddy immediately to the west of the cyclonic eddy, and the evolution of the filament was mainly due to the geostrophic flows associated with the eddy pair. Our observations reveal the mass exchange of the eddy pair and suggest that submesoscale flows can degrade the coherence of mesoscale eddies, providing important implications for the transport properties of mesoscale eddies. Vigorous submesoscale turbulence was found within the eddy core region, due to filamentous intrusion and frontogenesis. Our findings have thus offered novel insights into the dynamics and transport properties of oceanic submesoscale flows, which should be taken into account in their simulation and parameterization in ocean and climate models. Significance Statement Mesoscale eddies, with a spatial scale from tens to hundreds of kilometers, are ubiquitous in the global ocean. Carrying the largest proportion of oceanic kinetic energy, mesoscale eddies play a key role in ocean dynamics and have important applications for marine biology. Although mesoscale eddies have been studied extensively over the past decades, there are two major issues that remain inconclusive: (i) How do mesoscale eddies dissipate? (ii) Can eddies coherently trap waters when moving over a long distance? Recent studies, mostly through computer simulations, suggest that oceanic submesoscale processes, with a typical scale of a few kilometers, are highly relevant to the above two issues. This study presents a rare observation of a filament intruding into a cyclonic eddy. Because of this filament intrusion, submesoscale activities are enhanced near the eddy core area, in contrast to previous observations that normally suggest weaker submesoscale activities in the eddy core area than at eddy peripheries. Such dedicated process-oriented observations provide unique opportunities for better understanding the dynamics and transport properties of mesoscale eddies and submesoscale processes.

Convective organization and 3D structure of tropical cloud systems deduced from synergistic A-Train observations and machine learning

Abstract. We are building a 3D description of upper tropospheric (UT) cloud systems in order to study the relation between convection and cirrus anvils. For this purpose we used cloud data from the Atmospheric InfraRed Sounder and the Infrared Atmospheric Sounding Interferometer and atmospheric and surface properties from the meteorological reanalyses ERA-Interim and machine learning techniques. The different artificial neural network models were trained on collocated radar–lidar data from the A-Train in order to add cloud top height, cloud vertical extent and cloud layering, as well as a rain intensity classification to describe the UT cloud systems. The latter has an accuracy of about 65 % to 70 % and allows us to build objects of strong precipitation, used to identify convective organization. This rain intensity classification is more efficient to detect large latent heating than cold cloud temperature. In combination with a cloud system analysis, we found that deeper convection leads to larger heavy rain areas and a larger detrainment, with a slightly smaller thick anvil emissivity. This kind of analysis can be used for a process-oriented evaluation of convective precipitation parameterizations in climate models. Furthermore, we have shown the usefulness of our data to investigate tropical convective organization metrics. A comparison of different tropical convective organization indices and proxies to define convective areas has revealed that all indices show a similar annual cycle in convective organization, in phase with convective core height and anvil detrainment. The geographical patterns and magnitudes in radiative heating rate interannual changes with respect to one specific convective organization index (Iorg) for the period 2008 to 2018 are similar to the ones related to the El Niño–Southern Oscillation. However, since the interannual anomalies of the convective organization indices are very small and noisy, it was impossible to find a coherent relationship with those of other tropical mean variables such as surface temperature, thin cirrus area or subsidence area.

Implicit learning of convective organization explains precipitation stochasticity

Accurate prediction of precipitation intensity is crucial for both human and natural systems, especially in a warming climate more prone to extreme precipitation. Yet, climate models fail to accurately predict precipitation intensity, particularly extremes. One missing piece of information in traditional climate model parameterizations is subgrid-scale cloud structure and organization, which affects precipitation intensity and stochasticity at coarse resolution. Here, using global storm-resolving simulations and machine learning, we show that, by implicitly learning subgrid organization, we can accurately predict precipitation variability and stochasticity with a low-dimensional set of latent variables. Using a neural network to parameterize coarse-grained precipitation, we find that the overall behavior of precipitation is reasonably predictable using large-scale quantities only; however, the neural network cannot predict the variability of precipitation (R2 ∼ 0.45) and underestimates precipitation extremes. The performance is significantly improved when the network is informed by our organization metric, correctly predicting precipitation extremes and spatial variability (R2 ∼ 0.9). The organization metric is implicitly learned by training the algorithm on a high-resolution precipitable water field, encoding the degree of subgrid organization. The organization metric shows large hysteresis, emphasizing the role of memory created by subgrid-scale structures. We demonstrate that this organization metric can be predicted as a simple memory process from information available at the previous time steps. These findings stress the role of organization and memory in accurate prediction of precipitation intensity and extremes and the necessity of parameterizing subgrid-scale convective organization in climate models to better project future changes of water cycle and extremes.

Evolution of the microstructure and reflectance of the surface scattering layer on melting, level Arctic sea ice

The microstructure of the uppermost portions of a melting Arctic sea ice cover has a disproportionately large influence on how incident sunlight is reflected and absorbed in the ice/ocean system. The surface scattering layer (SSL) effectively backscatters solar radiation and keeps the surface albedo of melting ice relatively high compared to ice with the SSL manually removed. Measurements of albedo provide information on how incoming shortwave radiation is partitioned by the SSL and have been pivotal to improving climate model parameterizations. However, the relationship between the physical and optical properties of the SSL is still poorly constrained. Until now, radiative transfer models have been the only way to infer the microstructure of the SSL. During the MOSAiC expedition of 2019–2020, we took samples and, for the first time, directly measured the microstructure of the SSL on bare sea ice using X-ray micro-computed tomography. We show that the SSL has a highly anisotropic, coarse, and porous structure, with a small optical diameter and density at the surface, increasing with depth. As the melting surface ablates, the SSL regenerates, maintaining some aspects of its microstructure throughout the melt season. We used the microstructure measurements with a radiative transfer model to improve our understanding of the relationship between physical properties and optical properties at 850 nm wavelength. When the microstructure is used as model input, we see a 10–15% overestimation of the reflectance at 850 nm. This comparison suggests that either a) spatial variability at the meter scale is important for the two in situ optical measurements and therefore a larger sample size is needed to represent the microstructure or b) future work should investigate either i) using a ray-tracing approach instead of explicitly solving the radiative transfer equation or ii) using a more appropriate radiative transfer model.

Projected changes in droughts and extreme droughts in Great Britain strongly influenced by the choice of drought index

Abstract. Droughts cause enormous ecological, economical and societal damage, and they are already undergoing changes due to anthropogenic climate change. The issue of defining and quantifying droughts has long been a substantial source of uncertainty in understanding observed and projected trends. Atmosphere-based drought indicators, such as the Standardised Precipitation Index (SPI) and the Standardised Precipitation Evapotranspiration Index (SPEI), are often used to quantify drought characteristics and their changes, sometimes as the sole metric representing drought. This study presents a detailed systematic analysis of SPI- and SPEI-based drought projections and their differences for Great Britain (GB), derived from the most recent set of regional climate projections for the United Kingdom (UK). We show that the choice of drought indicator has a decisive influence on the resulting projected changes in drought frequency, extent, duration and seasonality using scenarios that are 2 and 4 ∘C above pre-industrial levels. The projected increases in drought frequency and extent are far greater based on the SPEI than based on the SPI. Importantly, compared with droughts of all intensities, isolated extreme droughts are projected to increase far more with respect to frequency and extent and are also expected to show more pronounced changes in the distribution of their event durations. Further, projected intensification of the seasonal cycle is reflected in an increasing occurrence of years with (extremely) dry summers combined with wetter-than-average winters. Increasing summer droughts also form the main contribution to increases in annual droughts, especially using the SPEI. These results show that the choice of atmospheric drought index strongly influences the drought characteristics inferred from climate change projections, with a comparable impact to the uncertainty from the climate model parameters or the warming level; therefore, potential users of these indices should carefully consider the importance of potential evapotranspiration in their intended context. The stark differences between SPI- and SPEI-based projections highlight the need to better understand the interplay between increasing atmospheric evaporative demand, moisture availability and drought impacts under a changing climate. The region-dependent projected changes in drought characteristics by two warming levels have important implications for adaptation efforts in GB, and they further stress the need for rapid mitigation.

Submesoscale inverse energy cascade enhances Southern Ocean eddy heat transport

Oceanic eddy-induced meridional heat transport (EHT) is an important process in the Southern Ocean heat budget, the variability of which significantly modulates global meridional overturning circulation (MOC) and Antarctic sea-ice extent. Although it is recognized that mesoscale eddies with scales of ~40–300 km greatly contribute to the EHT, the role of submesoscale eddies with scales of ~1–40 km remains unclear. Here, using two state-of-the-art high-resolution simulations (resolutions of 1/48° and 1/24°), we find that submesoscale eddies significantly enhance the total poleward EHT in the Southern Ocean with an enhancement percentage reaching 19–48% in the Antarctic Circumpolar Current band. By comparing the eddy energy budgets between the two simulations, we detect that the primary role of submesoscale eddies is to strengthen mesoscale eddies (and thus their heat transport capability) through inverse energy cascade rather than directly through submesoscale heat fluxes. Due to the submesoscale-mediated enhancement of mesoscale eddies in the 1/48° simulation, the clockwise upper cell and anti-clockwise lower cell of the residual-mean MOC in the Southern Ocean are weakened and strengthened, respectively. This finding identifies a potential route to improve the mesoscale parameterization in climate models for more accurate simulations of the MOC and sea ice variability in the Southern Ocean.

On the ocean's response to enhanced Greenland runoff in model experiments: relevance of mesoscale dynamics and atmospheric coupling

Abstract. Increasing Greenland Ice Sheet melting is anticipated to impact water mass transformation in the subpolar North Atlantic and ultimately the meridional overturning circulation. Complex ocean and climate models are widely applied to estimate magnitude and timing of related impacts under global warming. We discuss the role of the ocean mean state, subpolar water mass transformation, mesoscale eddies, and atmospheric coupling in shaping the response of the subpolar North Atlantic Ocean to enhanced Greenland runoff. In a suite of eight dedicated 60- to 100-year-long model experiments with and without atmospheric coupling, with eddy processes parameterized and explicitly simulated and with regular and significantly enlarged Greenland runoff, we find (1) a major impact by the interactive atmosphere in enabling a compensating temperature feedback, (2) a non-negligible influence by the ocean mean state biased towards greater stability in the coupled simulations, both of which make the Atlantic meridional overturning circulation less susceptible to the freshwater perturbation applied, and (3) a more even spreading and deeper mixing of the runoff tracer in the subpolar North Atlantic and enhanced inter-gyre exchange with the subtropics in the strongly eddying simulations. Overall, our experiments demonstrate the important role of mesoscale ocean dynamics and atmosphere feedback in projections of the climate system response to enhanced Greenland Ice Sheet melting and hence underline the necessity to advance scale-aware eddy parameterizations for next-generation climate models.

Многолетняя динамика снежного покрова в Приэльбрусье

Introduction. The snow accumulation regime is an important parameter in climate models, in assessing soil moisture reserves, in calculating the level of runoff, flood forecasting, and also for studying atmospheric pollution. In connection with global warming observed in recent decades, there are noticeable changes in climatic characteristics that affect the regime of snow accumulation in the mountains. Materials and research methods. To analyze and evaluate long-term changes in snow cover height, we used data from observations of precipitation and snow cover (from 1951 to 2022) at the Terskol meteorological station closest to the Elbrus resort, located at the altitude of 2100 m above the sea level. The materials of the study were long-term observations of the amount of precipitation in the cold season and the height of the snow cover at the Terskol meteorological station. Using the methods of statistical analysis, according to long-term data on snow depth for the cold period of the year, the mean and the standard deviation were calculated. Linear trends were obtained, the rate of change of the meteorological parameter (snow depth), anomalies were revealed relative to the average value for 1961 ÷ 1990 for each of the studied series. Research results and their discussion. The results of the analysis are the regularities of the temporal course of snow cover height at the Terskol meteorological station from 1951 to 2022. It was found that the value of the average snow depth in the full time series was 46 cm, the maximum value was 82 cm, the minimum – 16 cm, the average deviation was 15,65 cm. The time series of snow cover height is characterized by a negative trend expressed as a linear relationship. The values of anomalies of snow height values in different years are calculated. Conclusions. It is shown that the period from 1951 to 2022, the snow accumulation regime was characterized by an annual decrease in the average annual snow cover height by 0,1 cm per year. Snow height anomalies were obtained in different years.

Influences of Mesoscale Ocean Eddies on Flow Vertical Structure in a Resolution‐Based Model Hierarchy

AbstractThe understanding and representation of energetic transfers associated with ocean mesoscale eddies is fundamental to the development of parameterizations for climate models. We investigate the influence of eddies on flow vertical structure as a function of underlying dynamical regime and grid resolution. We employ the GFDL‐MOM6 in an idealized configuration and systematically consider four horizontal resolutions: 1/4°, 1/8°, 1/16°, and 1/32°. We analyze the distributions of potential and kinetic energy, decomposed into barotropic and baroclinic, and eddy and mean parts. As resolution increases and baroclinically unstable modes are better captured, kinetic energy increases and potential energy decreases. The dominant trend in vertical structure is an increasing fraction of kinetic energy going into the barotropic mode, particularly its eddy component, as eddies are increasingly resolved. We attribute the increased baroclinicity at low resolutions to inaccurate representation of vertical energy fluxes, leading to suppressed barotropization and energy trapping in high vertical modes. We also explore how the underlying dynamical regime influences energetic pathways. In cases where large‐scale flow is dominantly barotropic, resolving the deformation radius is less crucial to accurately capturing the flow's vertical structure. We find the barotropic kinetic energy fraction to be a useful metric in assessing vertical structure. In the highest‐resolution case, the barotropic kinetic energy fraction correlates with the scale separation between the deformation scale and the energy‐containing scale, that is, the extent of the eddy‐driven inverse cascade. This work suggests that mesoscale eddy parameterizations should incorporate the energetic effects of eddies on vertical structure in a scale‐aware, physically informed manner.

Investigation on cloud vertical structures based on Ka-band cloud radar observations at Wuhan in Central China

Based on vertically pointing observations of Ka-band frequency-modulated continuous millimeter wave cloud radar (MMCR) in 2020, we investigate cloud vertical structures at Wuhan in central China. Case comparison indicates that the cloud top locations in the MMCR observation are largely consistent with those in satellite data. The precipitation has the occurrence frequency of 9.5% over the year, with the maximum value of 16.3% in July and the submaximal value of 14.7% in January, showing the significant influences of monsoons on precipitation at Wuhan. As the humid, cloudy and rainy climate, the probability of precipitation and non-precipitation clouds increases gradually from 53.9% in January to a large value of 71.5% in July. The non-precipitation clouds have the probability of 44.5% in the year, with two prominent peaks at the reflectivities of −35 to −30 dBZ around 12 km in summer and − 37– −32 dBZ around 10 km in spring. The cloud top can reach the heights of 14.49, 16.62, 13.38 and 11.73 km from spring to winter, respectively. The monthly mean cloud layer thickness is the largest (2.18 km) in May and September, and the smallest (1.36 km) in November. The seasonally averaged base and top heights and thickness of clouds have small diurnal variations. The high cloud is predominant in the warm months from May to September, and in the other cool months, the middle cloud has the most fraction. The quantitative investigation of clouds is helpful in improving the cloud parameterization in climate models.

Observation of secondary ice production in clouds at low temperatures

Abstract. Ice particles play an important role in precipitation formation and radiation balance. Therefore, an accurate description of ice initiation in the atmosphere is of great importance for weather prediction models and climate simulations. Despite the abundance of ice crystals in the atmosphere, the mechanisms for their formation remain not well understood. There are two major sets of mechanisms of ice initiation in the atmosphere: primary nucleation and secondary ice production. Secondary ice production occurs in the presence of preexisting ice, which results in an enhancement of the concentration of ice particles. Until recently, secondary ice production was mainly attributed to the rime-splintering mechanism, known as the Hallett–Mossop process, which is active in a relatively narrow temperature range from −3 to −8 ∘C. The existence of the Hallett–Mossop process was well supported by in situ observations. The present study provides an explicit in situ observation of secondary ice production at temperatures as low as −27 ∘C, which is well outside the range of the Hallett–Mossop process. This observation expands our knowledge of the temperature range of initiation of secondary ice in clouds. The obtained results are intended to stimulate laboratory and theoretical studies to develop physically based parameterizations for weather prediction and climate models.

Mesoscale Convective Systems Simulated by a High‐Resolution Global Nonhydrostatic Model Over the United States and China

AbstractMesoscale convective systems (MCSs) contribute a large fraction of warm‐season precipitation and generate hazardous weather with substantial socioeconomic impacts. Uncertainties in convection parameterizations in climate models limit our understanding of MCS characteristics and reliability of future projection. We examine MCSs simulated by the global 14 km Nonhydrostatic ICosahedral Atmospheric Model (NICAM) without cumulus parameterization against satellite observation from Global Precipitation Measurement during 2001–2008. We focus on MCSs over the central United States and eastern China where MCSs are prevalent from March to August. A process‐oriented tracking method incorporating both cloud and precipitation criteria is used to identify and track MCSs. About 140/100 MCSs initiate in the central United States/eastern China per warm season and most of them initiate in the east of high mountains and in coastal regions. The frequency distribution of MCS lifetime is well captured by NICAM. However, the simulated MCSs have stronger precipitation, smaller precipitation area, and larger cold cloud system than that observed in both regions, which may be caused by weak entrainment as it is not well resolved at 14 km resolution. The simulated MCS number is also underestimated in summer. By examining the climatological and MCS large‐scale environments, the significant underestimation of MCS number in summer over the central United States may be attributed to the large climatological dry bias in the atmosphere. For China, mean moisture in summer is well simulated but deficiency in capturing the dynamic condition related to the coastal topography for triggering convection may have contributed to underestimation of MCS even in a sufficiently moist environment.

Evaluating Coupled Climate Model Parameterizations via Skill at Reproducing the Monsoon Intraseasonal Oscillation

Abstract Empirically generated indices are used to evaluate the skill of a global climate model in representing the monsoon intraseasonal oscillation (MISO). This work adapts the method of Suhas et al., an extended empirical orthogonal function (EEOF) analysis of daily rainfall data with the first orthogonal function indicating MISO strength and phase. This method is applied to observed rainfall and Community Earth System Model (CESM1.2) simulation results. Variants of the CESM1.2 including upper ocean parameterizations for Langmuir turbulence and submesoscale mixed layer eddy restratification are used together with the EEOF analysis to explore sensitivity of the MISO to global upper ocean process representations. The skill with which the model variants recreate the MISO strength and persistence is evaluated versus the observed MISO. While all model versions reproduce the northward rainfall propagation traditionally associated with the MISO, a version including both Langmuir turbulence and submesoscale restratification parameterizations provides the most accurate simulations of the time scale of MISO events.

A Library of Large‐Eddy Simulations Forced by Global Climate Models

AbstractAdvances in high‐performance computing have enabled large‐eddy simulations (LES) of turbulence, convection, and clouds. However, their potential to improve parameterizations in global climate models (GCMs) is only beginning to be harnessed, with relatively few canonical LES available so far. The purpose of this paper is to begin creating a public LES library that expands the training data available for calibrating and evaluating GCM parameterizations. To do so, we use an experimental setup in which LES are driven by large‐scale forcings from GCMs, which in principle can be used at any location, any time of year, and in any climate state. We use this setup to create a library of LES of clouds across the tropics and subtropics, in the present and in a warmer climate, with a focus on the transition from stratocumulus to shallow cumulus over the East Pacific. The LES results are relatively insensitive to the choice of host GCM driving the LES. Driven with large‐scale forcing under global warming, the LES simulate a positive but weak shortwave cloud feedback, adding to the accumulating evidence that low clouds amplify global warming.

Parameter Uncertainty Quantification in an Idealized GCM With a Seasonal Cycle

AbstractClimate models are generally calibrated manually by comparing selected climate statistics, such as the global top‐of‐atmosphere energy balance, to observations. The manual tuning only targets a limited subset of observational data and parameters. Bayesian calibration can estimate climate model parameters and their uncertainty using a larger fraction of the available data and automatically exploring the parameter space more broadly. In Bayesian learning, it is natural to exploit the seasonal cycle, which has large amplitude compared with anthropogenic climate change in many climate statistics. In this study, we develop methods for the calibration and uncertainty quantification (UQ) of model parameters exploiting the seasonal cycle, and we demonstrate a proof‐of‐concept with an idealized general circulation model (GCM). UQ is performed using the calibrate‐emulate‐sample approach, which combines stochastic optimization and machine learning emulation to speed up Bayesian learning. The methods are demonstrated in a perfect‐model setting through the calibration and UQ of a convective parameterization in an idealized GCM with a seasonal cycle. Calibration and UQ based on seasonally averaged climate statistics, compared to annually averaged, reduces the calibration error by up to an order of magnitude and narrows the spread of the non‐Gaussian posterior distributions by factors between two and five, depending on the variables used for UQ. The reduction in the spread of the parameter posterior distribution leads to a reduction in the uncertainty of climate model predictions.

Geometrical and Microphysical Properties of Clouds Formed in the Presence of Dust above the Eastern Mediterranean

In this work, collocated lidar–radar observations are used to retrieve the vertical profiles of cloud properties above the Eastern Mediterranean. Measurements were performed in the framework of the PRE-TECT experiment during April 2017 at the Greek atmospheric observatory of Finokalia, Crete. Cloud geometrical and microphysical properties at different altitudes were derived using the Cloudnet target classification algorithm. We found that the variable atmospheric conditions that prevailed above the region during April 2017 resulted in complex cloud structures. Mid-level clouds were observed in 38% of the cases, high or convective clouds in 58% of the cases, and low-level clouds in 2% of the cases. From the observations of cloudy profiles, pure ice phase occurred in 94% of the cases, mixed-phase clouds were observed in 27% of the cases, and liquid clouds were observed in 8.7% of the cases, while Drizzle or rain occurred in 12% of the cases. The significant presence of Mixed-Phase Clouds was observed in all the clouds formed at the top of a dust layer, with three times higher abundance than the mean conditions (26% abundance at −15 °C). The low-level clouds were formed in the presence of sea salt and continental particles with ice abundance below 30%. The derived statistics on clouds’ high-resolution vertical distributions and thermodynamic phase can be combined with Cloudnet cloud products and lidar-retrieved aerosol properties to study aerosol-cloud interactions in this understudied region and evaluate microphysics parameterizations in numerical weather prediction and global climate models.

PBL Height From AIRS, GPS RO, and MERRA‐2 Products in NASA GES DISC and Their 10‐Year Seasonal Mean Intercomparison

AbstractWithin the planetary boundary layer (PBL), surface forcing response, drag, turbulence, and vertical mixing are important processes and play a more critical role here than in the overlying “free atmosphere.” The PBL height (PBLH) is an important parameter in climate models, weather forecasts, and air quality prediction. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provides data processing, archiving, and distribution services for numerous Earth science products. PBLH is a parameter in three products served by GES DISC, which are from the atmospheric infrared sounder (AIRS), the global positioning system (GPS) radio occultation (RO) experiment, and the NASA reanalysis product Modern‐Era Retrospective analysis for Research and Applications‐2 (MERRA‐2). These products have different spatial and temporal resolutions and coverages, and their PBLH definitions are also different. To better serve the PBL research community, we have summarized the specifications of these products. A 10‐year seasonal mean intercomparison is also conducted to provide further guidance to users. The intercomparison results show that MERRA‐2 has a much shallower PBL than AIRS and GPS RO. An experimental study indicates that the different PBLH definition in MERRA‐2 caused smaller values of PBLH. The improvement of the water vapor retrieval in AIRS version 7 over version 6 results in the version 7 PBLH agreeing better with GPS RO and MERRA‐2 than version 6, especially near the equator and low latitudes.

Tropical teleconnection impacts on Antarctic climate changes

Over the modern satellite era, substantial climatic changes have been observed in the Antarctic, including atmospheric and oceanic warming, ice sheet thinning and a general Antarctic-wide expansion of sea ice, followed by a more recent rapid loss. Although these changes, featuring strong zonal asymmetry, are partially influenced by increasing greenhouse gas emissions and stratospheric ozone depletion, tropical–polar teleconnections are believed to have a role through Rossby wave dynamics. In this Review, we synthesize understanding of tropical teleconnections to the Southern Hemisphere extratropics arising from the El Niño–Southern Oscillation, Interdecadal Pacific Oscillation and Atlantic Multidecadal Oscillation, focusing on the mechanisms and long-term climatic impacts. These teleconnections have contributed to observed Antarctic and Southern Ocean changes, including regional rapid surface warming, pre-2015 sea ice expansion and its sudden reduction thereafter, changes in ocean heat content and accelerated thinning of most of the Antarctic ice sheet. However, due to limited observations and inherent model biases, uncertainties remain in understanding and assessing the importance of these teleconnections versus those arising from greenhouse gases, ozone recovery and internal variability. Sustained pan-Antarctic efforts towards long-term observations, and more realistic dynamics and parameterizations in high-resolution climate models, offer opportunities to reduce these uncertainties.

Diel cycle impacts on the chemical and light absorption properties of organic carbon aerosol from wildfires in the western United States

Abstract. Organic aerosol (OA) emissions from biomass burning have been the subject of intense research in recent years, involving a combination of field campaigns and laboratory studies. These efforts have aimed at improving our limited understanding of the diverse processes and pathways involved in the atmospheric processing and evolution of OA properties, culminating in their accurate parameterizations in climate and chemical transport models. To bring closure between laboratory and field studies, wildfire plumes in the western United States were sampled and characterized for their chemical and optical properties during the ground-based segment of the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Using a custom-developed multiwavelength integrated photoacoustic-nephelometer spectrometer in conjunction with a suite of instruments, including an oxidation flow reactor equipped to generate hydroxyl (OH⚫) or nitrate (NO3⚫) radicals to mimic daytime or nighttime oxidative aging processes, we investigated the effects of multiple equivalent hours of OH⚫ or NO3⚫ exposure on the chemical composition and mass absorption cross-sections (MAC(λ)) at 488 and 561 nm of OA emitted from wildfires in Arizona and Oregon. We found that OH⚫ exposure induced a slight initial increase in absorption corresponding to short timescales; however, at longer timescales, the wavelength-dependent MAC(λ) decreased by a factor of 0.72 ± 0.08, consistent with previous laboratory studies and reports of photobleaching. On the other hand, NO3⚫ exposure increased MAC(λ) by a factor of up to 1.69 ± 0.38. We also noted some sensitivity of aerosol aging to different fire conditions between Arizona and Oregon. The MAC(λ) enhancement following NO3⚫ exposure was found to correlate with an enhancement in CHO1N and CHOgt1N ion families measured by an Aerodyne aerosol mass spectrometer.

Sensitivity of Northern Hemisphere climate to ice–ocean interface heat flux parameterizations

Abstract. We investigate the impact of three different parameterizations of ice–ocean heat exchange on modeled sea ice thickness, sea ice concentration, and water masses. These three parameterizations are (1) an ice bath assumption with the ocean temperature fixed at the freezing temperature; (2) a two-equation turbulent heat flux parameterization with ice–ocean heat exchange depending linearly on the temperature difference between the underlying ocean and the ice–ocean interface, whose temperature is kept at the freezing point of the seawater; and (3) a three-equation turbulent heat flux approach in which the ice–ocean heat flux depends on the temperature difference between the underlying ocean and the ice–ocean interface, whose temperature is calculated based on the local salinity set by the ice ablation rate. Based on model simulations with the stand-alone sea ice model CICE, the ice–ocean model MPIOM, and the climate model COSMOS, we find that compared to the most complex parameterization (3), the approaches (1) and (2) result in thinner Arctic sea ice, cooler water beneath high-concentration ice and warmer water towards the ice edge, and a lower salinity in the Arctic Ocean mixed layer. In particular, parameterization (1) results in the smallest sea ice thickness among the three parameterizations, as in this parameterization all potential heat in the underlying ocean is used for the melting of the sea ice above. For the same reason, the upper ocean layer of the central Arctic is cooler when using parameterization (1) compared to (2) and (3). Finally, in the fully coupled climate model COSMOS, parameterizations (1) and (2) result in a fairly similar oceanic or atmospheric circulation. In contrast, the most realistic parameterization (3) leads to an enhanced Atlantic meridional overturning circulation (AMOC), a more positive North Atlantic Oscillation (NAO) mode and a weakened Aleutian Low.