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Understanding precipitation is essential for quantifying weather and climate-related risks. Changes in precipitation climatology are typically based on independent analysis of precipitation frequency and intensity. Here we show that where it rains more often, it also rains harder. When grouping global precipitation reanalysis data and observations from the past 40 years into regions of similar wet-day frequency, regardless of geographical separation, there is a strong correlation with wet-day intensity distributions. These wet-day-frequency regions are also more physically coherent than regions based on geographical location. We find the coherent relationship between wet-day frequency and intensity distributions is partially explained by wet-day-frequency regions having similar vertical velocity and convective available potential energy distributions, once polar regions are excluded. These represent dynamic and thermodynamic processes that indicate how conducive wet-day-frequency regions are to large-scale and convective precipitation. This suggests that the main drivers of precipitation are universal. We also show that extreme-precipitation metrics are dependent on wet-day frequency within our framework. Our results imply that wet-day frequency could be used to derive estimates of extreme-precipitation climate indices and corresponding uncertainties, these uncertainties being related to local processes.

Many of the implications of climate change for Aotearoa (New Zealand) remain unclear. To identify so-far unseen threats and opportunities related to climate change we applied a horizon-scanning process. First, we collated 171 threats and opportunities across our diverse fields of research. We then scored each item for novelty and potential impact and finally reduced the list to ten threats and ten opportunities through a prioritisation process. Within the 20 items presented in this paper, we uncover a range of climate-related costs and benefits. Unexpected opportunities evolve from economic reorganisation and changes to perspectives. The threats we highlight include the overall failure to interconnect siloed policy responses, as well as those relating to extreme events and feedbacks, as well pressures that undermine the coherence of society. A major theme of our work is that climate changes’ effects in Aotearoa are likely to transgress the boundaries of research disciplines, industry sectors and policy systems, emphasising the importance of developing transdisciplinary methods and approaches. We use this insight to connect potential responses to climate change with Aotearoa’s culture and geography.

Extra-tropical cyclones are key components of the atmospheric general circulation due to their ability to transport large quantities of heat, moisture, and momentum. Cyclones are an important contributor to extreme weather as their passage is associated with strong winds, and large precipitation accumulations. Here we connect a cyclone compositing scheme with regionally derived distributions of precipitation to present a framework for classifying spatially dependent extremes relative to the cyclone centre. Using this framework, cyclone composites for both average (50th percentile) and extreme (90th and 98th percentile) precipitation are derived from ERA5 reanalysis output. Composites are then partitioned into different stages of the cyclone lifecycle to assess the spatial and temporal evolution of precipitation extremes. We find that most extreme precipitation occurs within the comma-cloud structure close to the cyclone centre, with the extreme precipitation occurrence and intensity occurring in that region. Extreme precipitation is also identified to be largest during the period of deepening before the maximum cyclone intensity is reached. These regions of the cyclone correspond to places where large fractions of precipitation are above the extreme threshold. Strong spatial correlation are also seen between the average and extreme precipitation during the deepening phase for the precipitation mean, occurrence and fraction. This correlation weakens as the cyclone evolves and as the threshold used to determine extreme precipitation increases.

The biogeochemical behaviour of the Southern Ocean is complex and dynamic. The processes that affect this behaviour are highly dependent on physical, chemical, and biological constraints, which are poorly constrained in Earth System Models. We assess how emissions of dimethyl sulfide (DMS), a precursor of sulfate aerosol, change over the Southern Ocean when the chlorophyll-a distribution, which influences oceanic DMS production, is altered. Using a nudged configuration of the atmosphere-only United Kingdom Earth System Model, UKESM1-AMIP, we performed nine 10-year simulations using forcings representative of the period 2009–2018. Four different seawater DMS data sets are tested as input for these simulations. Three different DMS sea-to-air flux parameterizations are also explored. Our goal is to evaluate the changes in oceanic DMS, sea-to-air fluxes of DMS, and atmospheric DMS through these different simulations during austral summer. The mean spread across all the simulations with different oceanic DMS datasets, but the same sea-to-air flux parameterizations, is 112 % (3.3 to 6.9 TgS Yr−1). The mean spread in simulations using the same oceanic DMS dataset, but differing sea-to-air flux parameterisations is 50–60 % (2.9 to 4.7 TgS Yr−1). The choice of DMS emission parameterisation has a larger influence on atmospheric DMS than the choice of oceanic DMS source. We also find that linear relationships between wind and DMS flux generally compare better to observations than quadratic relationships. Simulations that implement a quadratic emission rate show on average 35 % higher DMS mixing ratios than the linear emission rates. Simulations using seawater DMS derived from satellite chlorophyll-a data in combination with a recently-developed flux parameterisation for DMS show the closest agreement with atmospheric DMS observations and are recommended to be included in future simulations. This work recommends for Earth System Models to include a sea-to-air parameterization that is appropriate for DMS, and for oceanic DMS datasets to include inter-annual variability based on observed marine biogenic activity. Such improvements will provide a more accurate process-based representation of oceanic and atmospheric DMS, and therefore sulfate aerosol, in the Southern Ocean region.

Understanding rainfall and its extremes is essential for quantifying weather and climate related risks, and the management of water resources. However, climate projections of rainfall have large uncertainties because of differing sensitivities to changes in dynamic, thermodynamic and microphysical factors. Emergent statistical constraints derived from observations and high resolution simulations can be used to reduce these uncertainties, but must be based on process understanding to be robust.Our recent work shows that clustering rainfall data into regions of similar wet day frequency, regardless of geographical separation, uncovers a strong correlation between wet day occurrence and daily rainfall accumulation distributions. This relationship is robust across a range of observational datasets with differing spatial resolutions.We hypothesise that this relationship shows that the presence or absence of precipitation generating weather systems (atmospheric rivers, cyclones, fronts and mesoscale convective storms) rather than their individual intensities is critical for daily rainfall totals. In this presentation, we will first examine whether the probability of specific dynamic, thermodynamic and microphysical states drives wet day occurrence. We will also use feature analysis and tracking schemes to identify how the distribution of each of these dynamic, thermodynamic and microphysical states varies for each precipitation generating weather system. This potentially allows a quantification of the importance of different precipitation generating systems for different wet day occurrences and how each contributes to daily accumulation distributions. This expands on previous work which has shown that atmospheric rivers, cyclones, fronts and mesoscale convective storms have varying relationships to rainfall and their extremes. In particular, it allows us to identify their relative importance and explains their relative efficacy for precipitation generation.

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 utilise 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 underestimation of cloud fraction. The associated biases in cloud occurrence are less clear and we suggest that modelled 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.

Sea ice drift data at high spatial resolution and surface wind model output are used to explore atmosphere-sea ice interactions in the Western Ross Sea including the three main polynyas areas; McMurdo Sound polynya (MSP), Terra Nova Bay polynya (TNBP), and the Ross Sea polynya (RSP). This study quantifies the relationship between the winds and sea ice drift and observes the average and annual anomalies across the region. Sea ice drift velocities are based on high-resolution (150 m) Advanced Synthetic Aperture Radar (ASAR) images from Envisat for winters between 2002 and 2012. Sea ice motion vectors were first correlated with the corresponding Antarctic Mesoscale Prediction System (AMPS) surface wind velocities, and the sensitivity of the spatial correlations and residuals were examined. Four drift parameters were selected (mean drift, the correlation between drift and wind, drift to wind scaling factor, and the directional drift constancy) to perform an unsupervised k-means classification to automatically distinguish six zones of distinctive sea ice characteristics solely based on ice drift and wind information. Results indicate a heterogeneous pattern of sea ice movement at a rate ranging from 0.41 to 2.24% of the wind speed in different areas. We also find that the directional constancy of sea ice drift is closely related to the wind fields. Sea ice drift and wind velocities display the highest correlation in free-drift areas (R = 0.70), followed by deformational drift zones (R = 0.54), and more random drift areas (R = 0.28). The classification illustrates the significance of localized wind-driven sea ice drift in this coastal area resulting in zones of convergence, shear, and free drift. The results also indicate that the most persistent patterns of sea ice motion are near the RSP and TNBP areas, both being driven by strong localized winds. Our findings identify that large-scale sea ice motion is predominantly wind-driven over much of the study area while ocean currents play only a minor role.

Over the remote Southern Ocean, cloud feedbacks contribute substantially to Earth system model (ESM) radiative biases. The evolution of low Southern Ocean clouds (cloud top heights < ~ 3 km) is strongly modulated by precipitation and/or evaporation, which act as the primary sink of cloud condensate. Constraining precipitation processes in ESMs requires robust observations suitable for process-level evaluations. A year-long subset (April 2016 – March 2017) of ground-based profiling instrumentation deployed during the Macquarie Island Cloud and Radiation Experiment (MICRE) field campaign (54.5° S, 158.9° E) combines a 95 GHz (W-band) Doppler cloud radar, two lidar ceilometers, and balloon-borne soundings to quantify the occurrence frequency of precipitation from liquid-phase cloud base. Liquid-based clouds at Macquarie Island precipitate ~ 70 % of the time, with deeper and colder clouds precipitating more frequently and at a higher intensity compared to thinner and warmer clouds. Supercooled cloud layers precipitate more readily than layers with cloud top temperatures > 0 °C, regardless of the geometric thickness of the layer, and also evaporate more frequently. We further demonstrate an approach to employ these observational constraints for evaluation of a 9-year GISS-ModelE3 ESM simulation. Model output is processed through the Earth Model Column Collaboratory (EMC2) radar and lidar instrument simulator with the same instrument specifications as those deployed during MICRE, therefore accounting for instrument sensitivities and ensuring a coherent comparison. Relative to MICRE observations, the ESM produces a smaller cloud occurrence frequency, smaller precipitation occurrence frequency, and greater sub-cloud evaporation. The lower precipitation occurrence frequency by the ESM relative to MICRE contrasts with numerous studies that suggest a ubiquitous bias by ESMs to precipitate too frequently over the SO when compared with satellite-based observations, likely owing to sensitivity limitations of space-borne instrumentation and different sampling methodologies for ground- versus space-based observations. Despite these deficiencies, the ESM reproduces the observed tendency for deeper and colder clouds to precipitate more frequently and at a higher intensity. The ESM also reproduces specific cloud regimes, including near-surface clouds that account for ~ 25 % of liquid-based clouds during MICRE and optically thin, non-precipitating clouds that account for ~ 27 % of clouds with bases higher than 250 m. We suggest that the demonstrated framework, which merges observations with appropriately constrained model output, is a valuable approach to evaluate processes responsible for cloud radiative feedbacks in ESMs.

By the 2050s, more than 120 million people are predicted to settle in the Pearl River Delta (PRD), which covers large coastal cities such as Guangzhou, Shenzhen and Hong Kong. Cities in the PRD are vitally important to China in relation to their socio-economic contributions. From recent evidence, this strongly urbanized area is vulnerable to, and currently facing bigger incidences of, coastal and urban flooding. Flood risk is growing in low-lying coastal areas due to rapid urbanization and increasing flood hazards exacerbated by climate change. Frequent intensive rainstorms, sea-level rise, typhoons and surges threaten large populations and their economic assets, causing severe socio-economic and ecological impacts in the PRD cities. Current flood risk management (FRM) in the delta is still predominately focused on using traditional techno-fixes and infrastructure paradigms, lacking sufficient strategic planning and flood protection to develop adequate flood resilience. Recent urban floods, enhanced by storm surges and intensive rainstorms, have affected multiple PRD cities and drawn attention to flood risk as a major challenge in the PRD’s coastal cities. This review encourages development of long-term FRM practices with provincial and municipal authorities working together more closely to develop better-integrated regional FRM strategies for the PRD.

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.

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).

Understanding precipitation variability is challenging, particularly in regions such as New Zealand, where the local topography strongly controls precipitation. This study provides a comprehensive evaluation of mean and extreme precipitation features over Aotearoa, New Zealand, using six merged satellite-gauge, five reanalysis, and three in-situ products, using station data as a reference. We find that all products show similar features in depicting the mean precipitation pattern with a clear maximum over the Alpine regions and a strong west-east gradient across the South Island. However, there are differences in the magnitude of mean precipitation estimates amongst different products at various regions on a seasonal timescale. Investigating the frequency of wet days shows that GPCP has the lowest count of all the satellite-based products, likely due to its coarse grid size, while MSWEP depicts the highest frequencies over the wettest region. Similarly, for reanalysis, MERRA-TP likely overestimates the frequency of occurrence west of the Alps compared to MERRA-PCORR, although the former showed better similarity with other datasets in terms of mean precipitation pattern. Moreover, statistical tests such as the Pearson correlation coefficient of the spatiotemporal pattern revealed that amongst the satellite-based products, MSWEP and GPM-IMERGE outperform other products with values of 0.9 and 0.66 with a mean wet-bias of 0.37 and 0.89 mm/day over the entire country. At the same time, ERA-5 and BARRA-R perform better in the suite of reanalysis products with a mean correlation coefficient of 0.87 and 0.74 with a mean wet-bias of 0.43 and 0.76 mm/day, respectively. This presentation also incorporates a set of precipitation indices approved by the ETCCDI committee to facilitate an intercomparison of different products in capturing the extreme tail of the distribution. Substantial differences especially over the West Coast in the South Island were observed in the interannual variability of the indices among different products. A closer examination of the percentile-based indices such as R95P and R99P revealed a contrasting pattern between different products in geographical regions. For instance, all the satellite-based products consistently showed wet bias as compared to the reanalysis products that depicted dry bias in all seasons. The MSWEP and BARRA-R datasets had the smallest relative percentage difference compared with the station data for most of the indices, suggesting their potential use for capturing both the mean and extremes characteristics of precipitation quite well in this region.

Extreme temperature events (ETEs) have evolved alongside the warming climate over most parts of the world. This study provides a statistical quantification of how human influences have increased the likelihood and frequencies of ETEs in New Zealand, depending on the synoptic weather type. We use the simulation under pre-industrial conditions (natural scenarios with no rise in greenhouse gases (GHGs)) and present-day conditions (anthropogenic scenarios) from the weather@home regional climate model. The ensembles of simulations under these two scenarios are used to identify how increases in GHG concentrations have impacted the frequency and intensity of ETEs based on their connection to different large-scale circulation patterns derived using Self Organizing Maps (SOMs). Over New Zealand, an average 2-3 fold rise in frequencies of extremes occurs irrespective of seasons due to elevated GHG concentrations with a mean temperature increase close to 1℃. For some synoptic situations, the frequency and intensity of ETEs are enhanced. In particular, for low-pressure centers to the northeast of New Zealand, the frequency of occurrence of daily temperature extremes has increased by a factor of 7 between anthropogenic and natural simulations for the winter season, though these synoptic patterns rarely occur. For low-pressure centers to the northwest of New Zealand, we observe extreme temperatures frequently in both anthropogenic and natural simulations which we attribute to warm air advection from the tropics. The frequency of occurrence of these synoptic patterns has also increased by a factor of 2 between the natural and anthropogenic simulations. For these synoptic states, the extremes are observed in the North Island and along the east coast of the country with the highest temperature along the Canterbury coast and Northland. However, the change between the natural and anthropogenic simulations is largest on the west coast along the Southern Alps.

Uncertainty in cloud feedback in climate models is a major limitation in projections of future climate. We analyse cloud biases and trends 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 deep convolutional artificial neural network for determination of cloud types from low-resolution daily mean top of atmosphere shortwave and longwave radiation images, corresponding to the World Meteorological Organization (WMO) cloud genera recorded by human observers in the Global Telecommunication System. We train this network on a satellite top of atmosphere radiation dataset, and apply it on the Climate Model Intercomparison Project phase 5 and 6 (CMIP5 and CMIP6) historical and abrupt-4xCO2 experiment model output and the ERA5 and Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) reanalyses. We compare these with satellite observations, link cloud type occurrence biases and trends to climate sensitivity, and compare our cloud types with an existing cloud regime classification based on the Moderate Resolution Imaging Spectroradiometer (MODIS) and International Satellite Cloud Climatology Project (ISCCP) satellite data. We show that there is a strong linear relationship between the root mean square error of cloud type occurrence and model equilibrium climate sensitivity, transient climate response and cloud feedback (Bayes factor 7×102, 4×102 and 13, respectively). This indicates that models with a better representation of the cloud types have a more positive cloud feedback and higher climate sensitivity. Along with other studies, our results point to a choice between two explanations: either high sensitivity models are plausible, contrary to combined assessments of climate sensitivity and cloud feedback in previous review studies, or the accuracy of representation of present-day clouds in models is negatively correlated with the accuracy of representation of future projected clouds.

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 optical thickness 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 7.0 and 7.1 of the HadGEM3 GCM and the MERRA-2 reanalysis. We find that MERRA-2 is biased in the opposite direction to GA (reflects too much SW radiation). In addition, MERRA-2 performs better in terms of absolute SW bias than nudged runs of GA7.0 and GA7.1 in the 60–70° S latitude band. GA7.1 reduces the SO SW radiation biases relative to GA7.0, but significant errors remain at up to 20 W m−2 between 60 and 70° S in the austral summer months. Using ship-based ceilometer observations, we find low cloud below 2 km to be predominant in the Ross Sea and the Indian Ocean sector of the SO. Utilising a novel surface lidar simulator developed for this study, derived from an existing COSP-ACTSIM spaceborne lidar simulator, we find that GA7.0 and MERRA-2 both underestimate low cloud occurrence relative to the ship observations by 18–25 % on average, though the cloud cover in MERRA-2 is closer to observations by about 7 %. Based on radiosonde observations, we also find the low cloud to be strongly linked to boundary-layer atmospheric stability and the sea surface temperature. GA7.0 and MERRA-2 agree well with observations in terms of boundary-layer stability, suggesting that subgrid-scale parametrisations do not generate enough cloud in response to the thermodynamic profile of the atmosphere and the surface temperature. Our analysis shows that MERRA-2 has a much greater proportion of cloud liquid water in the SO in January than GA7.0, a likely key contributor to the difference in SW radiation. We show that boundary-layer stability and relative humidity fields are very similar in GA7.0 and MERRA-2, and unlikely to be the cause of the different cloud representation, suggesting that subgrid-scale parametrisations are responsible for the difference between the models.

Cloud and aerosol lidars measuring backscatter and depolarization ratio are most suitable instruments 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 clouds based solely on ceilometers measuring only co-polarisation backscatter. We utilise 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–February 2019, with a variety of instruments including a depolarization lidar and a W-Band cloud radar which were used to build a 2-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 polarisation 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 SLW retrieval approach based solely 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.

With low concentrations of tropospheric aerosol, the Southern Ocean offers a “natural laboratory” for studies of aerosol–cloud interactions. Aerosols over the Southern Ocean are produced from biogenic activity in the ocean, which generates sulfate aerosol via dimethylsulfide (DMS) oxidation, and from strong winds and waves that lead to bubble bursting and sea spray emission. Here, we evaluate the representation of Southern Ocean aerosols in the Hadley Centre Global Environmental Model version 3, Global Atmosphere 7.1 (HadGEM3-GA7.1) chemistry–climate model. Compared with aerosol optical depth (AOD) observations from two satellite instruments (the Moderate Resolution Imaging Spectroradiometer, MODIS-Aqua c6.1, and the Multi-angle Imaging Spectroradiometer, MISR), the model simulates too-high AOD during winter and too-low AOD during summer. By switching off DMS emission in the model, we show that sea spray aerosol is the dominant contributor to AOD during winter. In turn, the simulated sea spray aerosol flux depends on near-surface wind speed. By examining MODIS AOD as a function of wind speed from the ERA-Interim reanalysis and comparing it with the model, we show that the sea spray aerosol source function in HadGEM3-GA7.1 overestimates the wind speed dependency. We test a recently developed sea spray aerosol source function derived from measurements made on a Southern Ocean research voyage in 2018. In this source function, the wind speed dependency of the sea spray aerosol flux is less than in the formulation currently implemented in HadGEM3-GA7.1. The new source function leads to good agreement between simulated and observed wintertime AODs over the Southern Ocean; however, it reveals partially compensating errors in DMS-derived AOD. While previous work has tested assumptions regarding the seawater climatology or sea–air flux of DMS, we test the sensitivity of simulated AOD, cloud condensation nuclei and cloud droplet number concentration to three atmospheric sulfate chemistry schemes. The first scheme adds DMS oxidation by halogens and the other two test a recently developed sulfate chemistry scheme for the marine troposphere; one tests gas-phase chemistry only, while the second adds extra aqueous-phase sulfate reactions. We show how simulated sulfur dioxide and sulfuric acid profiles over the Southern Ocean change as a result and how the number concentration and particle size of the soluble Aitken, accumulation and coarse aerosol modes are affected. The new DMS chemistry scheme leads to a 20 % increase in the number concentration of cloud condensation nuclei and cloud droplets, which improves agreement with observations. Our results highlight the importance of atmospheric chemistry for simulating aerosols and clouds accurately over the Southern Ocean.

Analysis of Coupled Model Intercomparison Project Phase 6 (CMIP6) models has focused on their higher equilibrium climate sensitivity (ECS) relative to CMIP5 simulations, with higher ECS values attr...

We demonstrate that the relationship between the abundance of particulate surface area observed at sea-level and measurements of backscattered light by a ceilometer can be used to classify the mixi...

The 2B-CLDCLASS-LIDAR R05 (2BCL5) and the raDAR/liDAR (DARDAR) satellite retrievals of cloud occurrence are compared as a function of altitude and latitude. The largest disparities are observed at ...

We studied sulfate aerosols over the Southern Ocean using the atmosphere-only climate model HadGEM3-GA7.1. The model contains biases in the aerosol seasonal variability over the Southern Ocean (40°S to 60°S), which cascade to uncertainties in aerosol-cloud interactions. Aerosols over the Southern Ocean are primarily natural in origin, such as sea spray aerosol and sulfate aerosol formed by phytoplankton-produced dimethyl sulfide (DMS).The current sulfate chemistry scheme implemented in the model simplifies the oxidation pathways for DMS, which has been identified as a major source of the seasonal bias present. The simulations performed here incorporate a comprehensive sulfate scheme in both the gas and aqueous-phase. An intermediate complexity biogeochemical dynamic model, MEDUSA, simulated a global climatology of seawater DMS, which is compared with a seawater DMS observational dataset from 2011. We compared the seasonality of sulfate aerosols over the Southern Ocean, and the global distribution using the two seawater DMS climatologies. Simulated aerosols over the Southern Ocean were evaluated against satellite and in-situ observations. The results show the impact of seawater DMS on sulfate aerosols and their influence on cloud formation.

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.

Weather and climate models are challenged by uncertainties and biases in simulating Southern Ocean (SO) radiative fluxes that trace to a poor understanding of cloud, aerosol, precipitation, and radiative processes, and their interactions. Projects between 2016 and 2018 used in situ probes, radar, lidar, and other instruments to make comprehensive measurements of thermodynamics, surface radiation, cloud, precipitation, aerosol, cloud condensation nuclei (CCN), and ice nucleating particles over the SO cold waters, and in ubiquitous liquid and mixed-phase clouds common to this pristine environment. Data including soundings were collected from the NSF–NCAR G-V aircraft flying north–south gradients south of Tasmania, at Macquarie Island, and on the R/V Investigator and RSV Aurora Australis. Synergistically these data characterize boundary layer and free troposphere environmental properties, and represent the most comprehensive data of this type available south of the oceanic polar front, in the cold sector of SO cyclones, and across seasons. Results show largely pristine environments with numerous small and few large aerosols above cloud, suggesting new particle formation and limited long-range transport from continents, high variability in CCN and cloud droplet concentrations, and ubiquitous supercooled water in thin, multilayered clouds, often with small-scale generating cells near cloud top. These observations demonstrate how cloud properties depend on aerosols while highlighting the importance of dynamics and turbulence that likely drive heterogeneity of cloud phase. Satellite retrievals confirmed low clouds were responsible for radiation biases. The combination of models and observations is examining how aerosols and meteorology couple to control SO water and energy budgets.

We demonstrate that the temporal correlation between the abundance of particulate surface area at sea-level and measurements of ceilometer backscatter can be used to quantify boundary layer mixing....

The Ross Sea region, including three main polynya areas in McMurdo Sound, Terra Nova Bay, and in front of the Ross Ice Shelf, has experienced a significant increase in sea ice extent in the first four decades of satellite observations. Here, we use Co-Registration of Optically Sensed Images and Correlation (COSI-Corr) to estimate 894 high-resolution sea ice motion fields of the Western Ross Sea in order to explore ice-atmosphere interactions based on sequential high-resolution Advanced Synthetic Aperture Radar (ASAR) images from the Envisat satellite acquired between 2002–2012. Validation of output motion vectors with manually drawn vectors for 24 image pairs show Pearson correlation coefficients of 0.92 ± 0.09 with a mean deviation in direction of −3.17 ± 6.48 degrees. The high-resolution vectors were also validated against the Environment and Climate Change Canada sea ice motion tracking algorithm, resulting in correlation coefficients of 0.84 ± 0.20 and the mean deviation in the direction of −0.04 ± 17.39 degrees. A total of 480 one-day separated velocity vector fields have been compared to an available NSIDC low-resolution sea ice motion vector product, showing much lower correlations and high directional differences. The high-resolution product is able to better identify short-term and spatial variations, whereas the low-resolution product underestimates the actual sea ice velocities by 47% in this important near-coastal region. The large-scale pattern of sea ice drift over the full time period is similar in both products. Improved image coverage is still desired to capture drift variations shorter than 24 h.

As greenhouse gases continue to accumulate in Earth’s atmosphere, the nature of extreme weather events (EWEs) has been changing and is expected to change in the future. EWEs have contributions from anthropogenic climate change as well as from natural variability, which complicates attribution statements. EWERAM is a project that has been funded through the New Zealand Ministry of Business, Innovation and Employment Smart Ideas programme to develop the capability to provide, within days of an EWE having occurred over New Zealand, and while public interest is still high, scientifically defensible statements about the role of climate change in both the severity and frequency of that event. This is expected to raise public awareness and understanding of the effects of climate change on EWEs.A team of researchers from five institutions across New Zealand are participating in EWERAM. EWE attribution is a multi-faceted problem and different approaches are required to address different research aims. Although robustly assessing the contribution of changes in the thermodynamic state to an observed event can be more tractable than including changes in the dynamics of weather systems, for New Zealand, changes in dynamics have had a large impact on the frequency and location of EWEs. As such, we have initiated several lines of research to deliver metrics on EWE attribution, tailored to meet the needs of various stakeholders, that encompass the effects of both dynamical and thermodynamical changes in the atmosphere. This presentation will give an overview of EWERAM and present the methodologies and tools used in the project.

With low concentrations of tropospheric aerosol, the Southern Ocean offers a "natural laboratory" for studies of aerosol–cloud interactions. Aerosols over the Southern Ocean are produced from biogenic activity in the ocean, which generates sulfate aerosol via dimethylsulfide (DMS) oxidation, and from strong winds and waves that lead to bubble bursting and sea spray emission. Here, we evaluate the representation of Southern Ocean aerosols in the Hadley Centre Global Environmental Model version 3, Global Atmosphere 7.1 (HadGEM3-GA7.1) chemistry–climate model. Compared with aerosol optical depth (AOD) observations from two satellite instruments (the Moderate Resolution Imaging Spectroradiometer, MODIS-Aqua c6.1, and the Multi-angle Imaging Spectroradiometer, MISR), the model simulates too-high AOD during winter and too-low AOD during summer. By switching off DMS emission in the model, we show that sea spray aerosol is the dominant contributor to AOD during winter. In turn, the simulated sea spray aerosol flux depends on near-surface wind speed. By examining MODIS AOD as a function of wind speed from the ERA-Interim reanalysis and comparing it with the model, we show that the sea spray aerosol source function in HadGEM3-GA7.1 overestimates the wind speed dependency. We test a recently developed sea spray aerosol source function derived from measurements made on a Southern Ocean research voyage in 2018. In this source function, the wind speed dependency of the sea spray aerosol flux is less than in the formulation currently implemented in HadGEM3-GA7.1. The new source function leads to good agreement between simulated and observed wintertime AODs over the Southern Ocean; however, it reveals partially compensating errors in DMS-derived AOD. While previous work has tested assumptions regarding the seawater climatology or sea–air flux of DMS, we test the sensitivity of simulated AOD, cloud condensation nuclei and cloud droplet number concentration to three atmospheric sulfate chemistry schemes. The first scheme adds DMS oxidation by halogens and the other two test a recently developed sulfate chemistry scheme for the marine troposphere; one tests gas-phase chemistry only, while the second adds extra aqueous-phase sulfate reactions. We show how simulated sulfur dioxide and sulfuric acid profiles over the Southern Ocean change as a result and how the number concentration and particle size of the soluble Aitken, accumulation and coarse aerosol modes are affected. The new DMS chemistry scheme leads to a 20% increase in the number concentration of cloud condensation nuclei and cloud droplets, which improves agreement with observations. Our results highlight the importance of atmospheric chemistry for simulating aerosols and clouds accurately over the Southern Ocean.

This study investigates the impacts of strong wind events on the sea ice concentration within polynya regions, with a focus on the Ross Sea Polynya (RSP). In particular, this work quantifies the sensitivity of sea ice concentrations to surface winds and whether there are threshold wind speeds required for regions of the polynya to open up with subsequent impacts on air-sea heat fluxes. To analyse these processes, we examine version 3.1 of the Bootstrap sea ice concentration (SIC) satellite data set derived from SSM/I brightness temperatures and how they are connected to the surface winds from the ERA5 reanalysis over the period 1979 to 2018. While we examine these relationships around the entire Antarctic continent, we focus on the RSP and low-level jets in the Ross Sea. In particular, we examine how strong wind events which impact SIC in the RSP are linked to Ross Ice Shelf Air Stream events (strong low-level jets in the region). The hypothesis that the increase in Ross Ice Shelf Air Stream events, associated with a strengthening of the Amundsen Sea Low, has contributed to trends in sea ice production in this region is examined.

We compare the consistency between eight reanalyses: CERA20C, ERA5, ERA‐Interim, ERA20C, NCEP‐DOE, MERRA2, JRA55, and 20CRV2c. This comparison uses daily surface winds near Antarctica to classify synoptic patterns using the self‐organizing map technique. The relative frequency of occurrence (RFO) of these patterns are very similar during the satellite era in each reanalysis. The three most common patterns are the same in each reanalysis and changes between the reanalyses only display a 12% relative variation. Examination of the RFOs over time highlights that the CERA20C and 20CRV2c reanalyses display large changes previous to 1957. These changes are likely connected to model relaxation toward their climatology because of a lack of observational constraints. Primarily, we introduce the entropy coefficient (U) which quantifies the consistency between reanalyses in their representation of synoptic patterns. Examination of U shows current reanalyses (ERA5, ERA‐Interim, JRA55, and MERRA2) are highly consistent in the satellite era likely due to good observational coverage. However, centennial reanalyses (CERA20C, ERA20C, and 20CRV2c) show two upward step changes in consistency as measured by U at around 1957 and to a lesser extent 1979. Low values of U before 1957 suggest that centennial reanalyses are of limited use before this date, but may be useful after 1957 in this region. We also show that the entropy coefficient displays an inverse relationship with ensemble spread metrics of individual reanalyses. We conclude that the entropy coefficient provides a powerful quantification of the influence of changes in observation density on reanalysis quality in data sparse regions.

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.

Asymmetry in the Southern Hemisphere stratospheric ozone hole is important due to both direct radiative heating and its effect on dynamics. It is also a strong indicator of the underlying quality of the stratospheric dynamics of a climate model. We investigate the simulation of the zonal asymmetry in ozone in the NIWA-UKCA atmosphere–ocean chemistry–climate model using elliptical diagnostics, a methodology used for the first time in this subject area. During spring, the region most depleted in ozone is displaced from the pole toward South America based on ERA-Interim and the model output. The model correctly simulates the direction of this displacement but significantly underestimates its magnitude. The model shows that as ozone becomes increasingly depleted over the late 20th century this asymmetry in the ozone distribution moves west, before moving east as polar ozone recovers over the course of the 21st century. Comparison with model runs in which ozone-depleting substances are held fixed at pre-ozone-hole levels shows that this shift is primarily a function of the magnitude of ozone depletion, although increases in greenhouse gases also have some effect.

We examine the effects of non-linear interactions between surface albedo, water vapor and cloud cover (referred to as climate variables) on amplified warming of the polar regions, using a new energy balance model. Our simulations show that the sum of the contributions to surface temperature changes due to any variable considered in isolation is smaller than the temperature changes from coupled feedback simulations. This non-linearity is strongest when all three climate variables are allowed to interact. Surface albedo appears to be the strongest driver of this non-linear behavior, followed by water vapor and clouds. This is because increases in longwave radiation absorbed by the surface, related to increases in water vapor and clouds, and increases in surface absorbed shortwave radiation caused by a decrease in surface albedo, amplify each other. Furthermore, our results corroborate previous findings that while increases in cloud cover and water vapor, along with the greenhouse effect itself, warm the polar regions, water vapor also significantly warms equatorial regions, which reduces polar amplification. Changes in surface albedo drive large changes in absorption of incoming shortwave radiation, thereby enhancing surface warming. Unlike high latitudes, surface albedo change at low latitudes are more constrained. Interactions between surface albedo, water vapor and clouds drive larger increases in temperatures in the polar regions compared to low latitudes. This is in spite of the fact that, due to a forcing, cloud cover increases at high latitudes and decreases in low latitudes, and that water vapor significantly enhances warming at low latitudes.

Location information from long-duration super-pressure balloons flying in the Southern Hemisphere lower stratosphere during 2014 as part of X Project Loon are used to assess the quality of a number of different reanalyses including National Centers for Environmental Prediction Climate Forecast System version 2 (NCEP-CFSv2), European Centre for Medium-Range Weather Forecasts (ERA-Interim), NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA), and the recently released MERRA version 2. Balloon GPS location information is used to derive wind speeds which are then compared with values from the reanalyses interpolated to the balloon times and locations. All reanalysis data sets accurately describe the winds, with biases in zonal winds of less than 0.37 m s−1 and meridional biases of less than 0.08 m s−1. The standard deviation on the differences between Loon and reanalyses zonal winds is latitude-dependent, ranging between 2.5 and 3.5 m s−1, increasing equatorward. Comparisons between Loon trajectories and those calculated by applying a trajectory model to reanalysis wind fields show that MERRA-2 wind fields result in the most accurate simulated trajectories with a mean 5-day balloon–reanalysis trajectory separation of 621 km and median separation of 324 km showing significant improvements over MERRA version 1 and slightly outperforming ERA-Interim. The latitudinal structure of the trajectory statistics for all reanalyses displays marginally lower mean separations between 15 and 35° S than between 35 and 55° S, despite standard deviations in the wind differences increasing toward the equator. This is shown to be related to the distance travelled by the balloon playing a role in the separation statistics.

Despite warming trends in global temperatures, sea ice extent in the Southern Hemisphere has shown an increasing trend over recent decades. Wind-driven sea ice export from coastal polynyas is an important source of sea ice production. Areas of major polynyas in the Ross Sea, the region with largest increase in sea ice extent, have been suggested to produce a vast amount of the sea ice in the region. We investigate the impacts of strong wind events on the Ross Sea Polynyas and its sea ice concentration and possible consequences on sea ice production. We utilise Bootstrap sea ice concentration (SIC) measurements derived from satellite based, Special Sensor Microwave Imager (SSM/I) brightness temperatures. We compared these with surface winds and temperatures from automatic weather stations (AWS) of the University of Wisconsin-Madison Antarctic Meteorology Program. Our analysis focusses on the austral winter period defined as 1st April to 1st November in this study. Daily data were used to classified into characteristic regimes based on the percentiles of wind speed. For each regime, a composite of SIC anomaly was formed for the Ross Sea region. We found that persistent weak winds near the edge of the Ross Ice Shelf are generally associated with positive SIC anomalies in the Ross Sea Polynya (RSP). Conversely we found negative SIC anomalies in this area during persistent strong winds. By analysing sea ice motion vectors derived from SSM/I and SSMIS brightness temperatures, we find significant sea ice motion anomalies throughout the Ross Sea during strong wind events. These anomalies persist for several days after the strong wind event. Strong, negative correlations are found between SIC and AWS wind speed within the RSP indicating that strong winds cause significant advection of sea ice in the region. We were able to recreate these correlations using co-located ERA-Interim wind speeds. However when only days of a certain percentile based wind speed classification were used, the cross correlation functions produced by ERA-Interim wind speeds differed significantly from those produced using AWS wind speeds. The rapid decrease in SIC during a strong wind event is followed by a more gradual recovery in SIC. This increase occurs on a more gradual time scale than the average persistence of a strong wind event and the resulting sea ice motion anomalies, highlighting the production of new sea ice through thermodynamic processes. In the vicinity of Ross Island, ERA-Interim underestimates wind speeds by a factor of 1.7, which results in a significant misrepresentation of the impact of winds on polynya processes.

This case study implements long-term projections of domestic water demand for a UK water company, Thames Water. Projections of per household consumption (PHC) and households were combined to yield future demand. Regression models predicted PHC using the determinants of occupancy, property type, ethnicity and rateable value, drawing on 2006–2015 domestic water-use data as a baseline. A model was developed for diffusing savings in per capita consumption (PCC), drawn from published studies of interventions. PCC declines were converted to PHC reductions using baseline ratios. Interventions were grouped into Business as Usual, Light Green (limited intervention), and Dark Green (extreme intervention) scenarios. Projected households were generated by property type, occupancy, and ethnicity for Thames Water’s resource zones for 2011 to 2101 and multiplied by projected PHCs to yield water-demand projections. By 2101, the 2011 water demand of 1,225 million liters a day grew 90% under Business as Usual, 69% under Light Green, and 46% under Dark Green.