Peter Kuma Software and Science

Article | The Cold-Air Outbreaks in the Marine Boundary Layer Experiment model-observation intercomparison project (COMBLE-MIP), Part I: Model specification, observational constraints, and preliminary findings

Juliano et al. — Mon, 19 Jan 2026 00:00:00 +0000

Models are universally challenged to accurately predict the coupled microphysical, turbulent and radiative processes within widespread, long-lived marine cold-air outbreak (CAO) cloud fields, which leads to biases and uncertainties in atmospheric predictions over all time scales. Here we assemble a suite of ground-based and satellite measurements to initialize and constrain large-eddy simulations (LES) of cloud field evolution with distance downwind from the marginal ice zone during a strong, highly supercooled and convective CAO observed during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE). Detailed LES results are compared with large-scale models run in single-column model (SCM) mode, providing an observation-constrained framework for large-scale model evaluation and future improvements. All models reproduce rapid cloud formation off the ice edge, and a monotonic ascent of downwind cloud-top heights that is well correlated with time-integrated surface heat fluxes. LES generally reproduce domain-mean observational targets using a modest test domain (25 × 25 km²), and a larger domain (125 × 125 km²) enables better reproducing the observed growth of convective cell sizes. In realistic mixed-phase LES compared with liquid-only simulations, ice processes lead to thinner, broken cloud decks and substantially reduced cloud radiative effects on top-of-atmosphere longwave fluxes. By contrast, mixed-phase SCM simulations generally underpredict the impact of ice on radiative fluxes, primarily owing to insufficient reduction of cloud cover. Results indicate that cellular cloud structure is qualitatively captured by LES, and thus LES could provide guidance to improvement of large-scale model physics schemes. Follow-on work will extend these results to larger domains, apply objective analysis of mesoscale structure, and include prognostic aerosol properties for droplet and heterogeneous ice formation.

Poster | Ship-based lidar evaluation of Southern Ocean low clouds in the storm-resolving general circulation model ICON and the ERA5 and MERRA-2 reanalyses

Kuma et al. — Wed, 26 Nov 2025 00:00:00 +0000

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

Article | Ship-based lidar evaluation of Southern Ocean low clouds in the storm-resolving general circulation model ICON and the ERA5 and MERRA-2 reanalyses

Kuma et al. — Fri, 21 Nov 2025 00:00:00 +0000

Article | nextGEMS: entering the era of kilometer-scale Earth system modeling

Segura et al. — Thu, 23 Oct 2025 00:00:00 +0100

The Next Generation of Earth Modeling Systems (nextGEMS) project aimed to produce multidecadal climate simulations, for the first time, with resolved kilometer-scale (km-scale) processes in the ocean, land, and atmosphere. In only 3 years, nextGEMS achieved this milestone with the two km-scale Earth system models, ICOsahedral Non-hydrostatic model (ICON) and Integrated Forecasting System coupled to the Finite-volumE Sea ice-Ocean Model (IFS-FESOM). nextGEMS was based on three cornerstones: (1) developing km-scale Earth system models with small errors in the energy and water balance, (2) performing km-scale climate simulations with a throughput greater than 1 simulated year per day, and (3) facilitating new workflows for an efficient analysis of the large simulations with common data structures and output variables. These cornerstones shaped the timeline of nextGEMS, divided into four cycles. Each cycle marked the release of a new configuration of ICON and IFS-FESOM, which were evaluated at hackathons. The hackathon participants included experts from climate science, software engineering, and high-performance computing as well as users from the energy and agricultural sectors. The continuous efforts over the four cycles allowed us to produce 30-year simulations with ICON and IFS-FESOM, spanning the period 2020–2049 under the SSP3-7.0 scenario. The throughput was about 500 simulated days per day on the Levante supercomputer of the German Climate Computing Center (DKRZ). The simulations employed a horizontal grid of about 5 km resolution in the ocean and 10 km resolution in the atmosphere and land. Aside from this technical achievement, the simulations allowed us to gain new insights into the realism of ICON and IFS-FESOM. Beyond its time frame, nextGEMS builds the foundation of the Climate Change Adaptation Digital Twin developed in the Destination Earth initiative and paves the way for future European research on climate change.

Poster | Exploring sensitivity to ice nucleating particles and secondary ice production during COMBLE in idealised ICON large eddy simulations

Kuma and Possner — Wed, 02 Apr 2025 00:00:00 +0100

Poster presented at the CleanCloud General Assembly in Patras, Greece on 2–3 April 2025.

Article | Evaluating Cloud Properties at Scott Base: Comparing Ceilometer Observations With ERA5, JRA55, and MERRA2 Reanalyses Using an Instrument Simulator

McDonald et al. — Mon, 20 Jan 2025 00:00:00 +0000

This study compares CL51 ceilometer observations made at Scott Base, Antarctica, with statistics from the ERA5, JRA55, and MERRA2 reanalyses. To enhance the comparison we use a lidar instrument simulator to derive cloud statistics from the reanalyses which account for instrumental factors. The cloud occurrence in the three reanalyses is slightly overestimated above 3 km, but displays a larger underestimation below 3 km relative to observations. Unlike previous studies, we see no relationship between relative humidity and cloud occurrence biases, suggesting that the cloud biases do not result from the representation of moisture. We also show that the seasonal variation of cloud occurrence and cloud fraction, defined as the vertically integrated cloud occurrence, are small in both the observations and the reanalyses. We also examine the quality of the cloud representation for a set of weather states derived from ERA5 surface winds. The variability associated with grouping cloud occurrence based on weather state is much larger than the seasonal variation, highlighting weather state is a strong control of cloud occurrence. All the reanalyses continue to display underestimates below 3 km and overestimates above 3 km for each weather state. But the variability in ERA5 statistics matches the changes in the observations better than the other reanalyses. We also use a machine learning scheme to estimate the quantity of supercooled liquid water cloud from the ceilometer observations. Ceilometer low‐level supercooled liquid water cloud occurrences are considerably larger than values derived from the reanalyses, further highlighting the poor representation of low‐level clouds in the reanalyses.

Presentation | Ship-based lidar evaluation of Southern Ocean clouds in the storm-resolving general circulation model ICON and the ERA5 and MERRA-2 reanalyses

Kuma et al. — Tue, 26 Nov 2024 00:00:00 +0000

Global storm-resolving models (GSRMs) are the next avenue of climate modelling. Among them is the 5-km Icosahedral Nonhydrostatic Weather and Climate Model (ICON). The high resolution allows for parameterizations of convection and clouds to be avoided. Standard-resolution models have substantial cloud biases over the Southern Ocean (SO), affecting radiation and sea surface temperature. We evaluated SO clouds in ICON and the ERA5 and MERRA-2 reanalyses. The SO is dominated by low clouds, which cannot be observed accurately from space due to overlapping clouds, attenuation, and ground clutter. Instead, we analysed about 2400 days of lidar observations from 31 voyages and a station using a ground-based lidar simulator. ICON and the reanalyses underestimate the total cloud fraction by about 10 and 20%, respectively. ICON and ERA5 overestimate the cloud occurrence peak at about 500 m, potentially explained by their lifting condensation levels being too high. The reanalyses strongly underestimate near-surface clouds or fog. MERRA-2 tends to underestimate cloud occurrence at all heights. Less stable conditions are the most problematic for ICON and the reanalyses. In daily cloud cover, ICON and the reanalyses tend to be about 1 and 2 oktas clearer, respectively. Compared to radiosondes, potential temperature is accurate in the reanalyses, but ICON underestimates stability over the low-latitude SO and too humid in the boundary layer. MERRA-2 is too humid at all heights. SO cloud biases remain a substantial issue in the GSRM, but are an improvement over the lower-resolution reanalyses. Explicitly resolved convection and cloud processes were not enough to address the model cloud biases.

Poster | Ship-based lidar evaluation of Southern Ocean clouds in the storm-resolving general circulation model ICON and the ERA5 and MERRA-2 reanalyses

Kuma et al. — Wed, 13 Nov 2024 00:00:00 +0000

Presentation | Climate Modelling and Current Research Topics in Climate Science

Kuma — Tue, 29 Oct 2024 00:00:00 +0000

In recent decades, climate models have become an integral part of climate science. They allow us to not only predict the Earth's future climate but also to reconstruct the past and better understand the present climate. In this talk, you will learn how they work, what supercomputers they run on, and the broader context of climate change and human impact on the climate.

A recording of the presentation is available on [YouTube](https://www.youtube.com/watch?v=NRlbg-8y6Fw).

Report | NAISS Activity Report 2023/22-202

Kuma et al. — Thu, 30 May 2024 00:00:00 +0100

A project report for the National Academic Infrastructure for Supercomputing in Sweden.

Poster | Using ship observations to assess Southern Ocean clouds in a storm-resolving general circulation model ICON

Kuma and Bender — Wed, 15 May 2024 00:00:00 +0100

Currently, a new generation of km-scale resolution global climate models are in development as the forthcoming phase of climate modelling. One such model is a 5-km version of the Icosahedral Nonhydrostatic Weather and Climate Model (ICON) developed jointly by Deutscher Wetterdienst (DWD) and the Max-Planck-Institute for Meteorology (MPI-M). Because of the high resolution, most parametrisations, such as that of convection and clouds, can be avoided.

Previous studies have identified substantial large-scale biases in model clouds over the Southern Ocean, affecting sea surface temperature and the Earth's albedo overall. Our aim is to quantify how well the high-resolution ICON model is simulating clouds in this region, particularly in light of the fact that subgrid-scale clouds are not parametrised in this model. This region is mostly dominated by boundary layer clouds generated by shallow convection, and these are problematic to observe by spaceborne lidar and radars, which are affected by attenuation by overlapping and thick clouds and ground clutter, respectively. Therefore, we choose to use a large set of ship-based observations conducted with ceilometers and lidars on board of RV Polarstern and other voyages. Altogether, we analyse about 1500 days of data from 31 voyages and 1 sub-antarctic station covering diverse longitudes of the Southern Ocean. To achieve a like-for-like comparison with the model, we use a ground-based lidar simulator called the Automatic Lidar and Ceilometer Framework (ALCF). We contrast the results with the ECMWF Reanalysis 5 (ERA5) and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2).

We show that the model underestimates the total cloud fraction by about 10%, with overestimation of cloud below 2 km, and underestimation of cloud above 2 km. The reanalyses also underestimate the total cloud fraction by about 20%. ERA5 overestimates cloud below 1 km but underestimates near-surface cloud or fog. In addition to lidar data, we compare radiosonde profiles acquired on the RV Polarstern voyages with ICON. Notably, the model exhibits smaller natural variability than observations, and its lifting condensation level tends to be higher. This might explain why cloud occurrence is peaking higher in the model (at 500 m) than in observations (at the surface).

The results imply that Southern Ocean cloud biases are still a significant issue in a km-scale resolution model, even though an improvement over the lower-resolution reanalyses is notable. More effort is needed to improve model cloud simulations in this fast-changing and understudied region. The advancement from convection and cloud parametrisation to cloud-resolving models might not solve this bias without an additional effort.

Poster | Using ship observations to assess Southern Ocean clouds in a storm-resolving general circulation model ICON

Kuma and Bender — Wed, 29 Nov 2023 00:00:00 +0000

Article | Assessing the cloud radiative bias at Macquarie Island in the ACCESS-AM2 model

Pei et al. — Wed, 29 Nov 2023 00:00:00 +0000

As a long-standing problem in climate models, large positive shortwave radiation biases exist at the surface over the Southern Ocean, impacting the accurate simulation of sea surface temperature, atmospheric circulation, and precipitation. Underestimations of low-level cloud fraction and liquid water content are suggested to predominantly contribute to these radiation biases. Most model evaluations for radiation focus on summer and rely on satellite products, which have their own limitations. In this work, we use surface-based observations at Macquarie Island to provide the first long-term, seasonal evaluation of both downwelling surface shortwave and longwave radiation in the Australian Community Climate and Earth System Simulator Atmosphere-only Model version 2 (ACCESS-AM2) over the Southern Ocean. The capacity of the Clouds and the Earth’s Radiant Energy System (CERES) product to simulate radiation is also investigated. We utilize the novel lidar simulator, the Automatic Lidar and Ceilometer Framework (ALCF), and all-sky cloud camera observations of cloud fraction to investigate how radiation biases are influenced by cloud properties.

Overall, we find an overestimation of W m^-2 for downwelling surface shortwave radiation fluxes and an underestimation of W m^-2 for downwelling surface longwave radiation in ACCESS-AM2 in all-sky conditions, with more pronounced shortwave biases of W m^-2 occurring in summer. CERES presents an overestimation of W m^-2 for the shortwave and an underestimation of W m^-2 for the longwave in all-sky conditions. For the cloud radiative effect (CRE) biases, there is an overestimation of W m^-2 in ACCESS-AM2 and an underestimation of W m^-2 in CERES. An overestimation of downwelling surface shortwave radiation is associated with an underestimated cloud fraction and low-level cloud occurrence. We suggest that modeled cloud phase is also having an impact on the radiation biases. Our results show that the ACCESS-AM2 model and CERES product require further development to reduce these radiation biases not just in shortwave and in all-sky conditions, but also in longwave and in clear-sky conditions.

Presentation | Climate Model Code Genealogy and Its Relation to Climate Feedbacks and Sensitivity

Kuma et al. — Thu, 21 Sep 2023 00:00:00 +0100

Contemporary general circulation models (GCMs) and Earth system models (ESMs) are developed by a large number of modeling groups globally. They use a wide range of representations of physical processes, allowing for structural (code) uncertainty to be partially quantified with multi-model ensembles (MMEs). Many models in the MMEs of the Coupled Model Intercomparison Project (CMIP) have a common development history due to sharing of code and schemes. This makes their projections statistically dependent and introduces biases in MME statistics. Previous research has focused on model output and code dependence, and model code genealogy of CMIP models has not been fully analyzed. We present a full reconstruction of CMIP3, CMIP5 and CMIP6 code genealogy of 167 atmospheric models, GCMs, and ESMs (of which 114 participated in CMIP) based on the available literature, with a focus on the atmospheric component and atmospheric physics. We identify 12 main model families. We propose family and code weighting methods designed to reduce the effect of model structural dependence in MMEs. We analyze weighted effective climate sensitivity (ECS), climate feedbacks, forcing, and global mean near-surface air temperature, and how they differ by model family. Models in the same family often have similar climate properties. We show that weighting can partially reconcile differences in ECS and cloud feedbacks between CMIP5 and CMIP6. The results can help in understanding structural dependence between CMIP models, and the proposed code and family weighting methods can be used in MME assessments to ameliorate model structural sampling biases.

Media | Family Trees Clarify Relationships Among Climate Models

Tue, 18 Jul 2023 00:00:00 +0000

Article | Climate model code genealogy and its relation to climate feedbacks and sensitivity

Kuma et al. — Wed, 12 Jul 2023 00:00:00 +0100

Contemporary general circulation models (GCMs) and Earth system models (ESMs) are developed by a large number of modeling groups globally. They use a wide range of representations of physical processes, allowing for structural (code) uncertainty to be partially quantified with multi-model ensembles (MMEs). Many models in the MMEs of the Coupled Model Intercomparison Project (CMIP) have a common development history due to sharing of code and schemes. This makes their projections statistically dependent and introduces biases in MME statistics. Previous research has focused on model output and code dependence, and model code genealogy of CMIP models has not been fully analyzed. We present a full reconstruction of CMIP3, CMIP5 and CMIP6 code genealogy of 167 atmospheric models, GCMs, and ESMs (of which 114 participated in CMIP) based on the available literature, with a focus on the atmospheric component and atmospheric physics. We identify 12 main model families. We propose family and ancestry weighting methods designed to reduce the effect of model structural dependence in MMEs. We analyze weighted effective climate sensitivity (ECS), climate feedbacks, forcing, and global mean near-surface air temperature, and how they differ by model family. Models in the same family often have similar climate properties. We show that weighting can partially reconcile differences in ECS and cloud feedbacks between CMIP5 and CMIP6. The results can help in understanding structural dependence between CMIP models, and the proposed ancestry and family weighting methods can be used in MME assessments to ameliorate model structural sampling biases.

Article | Machine learning of cloud types in satellite observations and climate models

Kuma et al. — Fri, 13 Jan 2023 00:00:00 +0000

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

Poster | Climate model code genealogy and its relation to sensitivity and feedbacks

Kuma et al. — Wed, 14 Sep 2022 00:00:00 +0100

Contemporary general circulation models and Earth system models are developed by a large group of modelling centres internationally. They use a broad range of implementations of climate dynamics and physical parametrisations, allowing for structural (code) uncertainty to be partially quantified with multi-model ensembles (MMEs). However, many models in the MMEs of the Climate Model Intercomparison Project (CMIP) have a common development history due to the widespread practice of sharing of code and parametrisations within and between modelling centres. This makes results from different models statistically dependent, potentially introducing biases in MME statistics. This situation became more pronounced in CMIP6 compared to CMIP5 due to the proliferation of model runs contributed by the same model, and due to the fact that several models predict much higher effective climate sensitivity (ECS) than multiple evidence assessments such as the Intergovernmental Panel on Climate Change Sixth Assessment Report, and this means that some MME statistics differ from multiple evidence estimates. Previous research investigating effects of model inter-dependence has focused on model output and code dependence, but model code genealogy of CMIP models has not been fully analysed. We present a full reconstruction of CMIP3, CMIP5 and CMIP6 model code genealogy based on available literature and online resources, with a focus on inheritance in the atmospheric component and atmospheric physical parametrisations. We developed a ‘fair’ model code weighting method based on the model code genealogy for the purpose of analysing the impact of such weighting on MME means. We assess the implications of such weighting on ECS, climate feedbacks, forcing and global mean near-surface air temperature, as well as simpler weighting methods based on model family, institute and country in CMIP5 and CMIP6. In some cases the impact is found to be substantial and can partially reconcile the differences in MME means between CMIP5 and CMIP6. We show that some model families have a propensity to be relatively warm or cold in the main CMIP5 and CMIP6 experiments. Our method is complementary to the existing methods based on model output clustering. The presented results can help in understanding of structural dependencies between CMIP models, and the proposed code and family weighting methods can be used in MME assessments to ameliorate model structure sampling biases.

Article | Detection of supercooled liquid water containing clouds with ceilometers: development and evaluation of deterministic and data-driven retrievals

Guyot et al. — Mon, 20 Jun 2022 00:00:00 +0100

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.

Presentation | Machine learning of cloud types in satellite observations and climate models

Kuma et al. — Fri, 13 May 2022 00:00:00 +0100

Presentation given at the 13th Annual SeRC Meeting, Bro, Sweden on 13 May 2022.

Presentation | Software for climate sciences

Kuma — Wed, 09 Feb 2022 00:00:00 +0000

Research seminar given at Stockholm University on 9 February 2022.

Lecture | Global Climate System: Clouds and aerosols in the climate system

Kuma — Tue, 24 Jan 2023 00:00:00 +0000

Lecture given at Stockholm University on 24 January 2023.

Presentation | Clouds in climate models and atmospheric observations

Kuma — Tue, 14 Dec 2021 00:00:00 +0000

Research seminar given at Stockholm University on 14 December 2021.

Poster | Using deep learning cloud classification in cloud feedback and climate sensitivity determination

Kuma and Bender — Wed, 27 Oct 2021 00:00:00 +0100

We develop a deep convolutional neural network for determination of cloud types in low-resolution daily mean top-of-atmosphere shortwave and longwave radiation images, corresponding to the classical cloud types recorded by human observers in the Global Telecommunication System. We train this network on the CERES top of atmosphere radiation dataset, and apply this network on the CMIP6 abrupt-4xCO2 model output to determine long-term change in cloud type occurrence in these models with increasing CO2 concentration. We contrast these results with corresponding cloud type change in historical satellite measurements. The proposed neural network approach is broadly applicable for model, reanalysis and satellite imagery evaluation because it does not require high resolution and corresponds to the cloud types commonly recorded at weather stations worldwide.

Presentation | Machine learning of cloud types for evaluation of climate models and constraining climate sensitivity

Kuma and Bender — Mon, 25 Oct 2021 00:00:00 +0100

Presentation | Using deep learning cloud classification for cloud feedback and climate sensitivity determination

Kuma and Bender — Thu, 02 Sep 2021 00:00:00 +0100

Presentation given at a FORCeS WP5 & WP6 Science Meeting, 2 September 2021.

Media | How Airborne Microplastics Affect Climate Change

Wed, 20 Oct 2021 00:00:00 +0000

Media | Microplastics May Be Cooling—and Heating—Earth’s Climate

Wed, 20 Oct 2021 00:00:00 +0000

Media | Microplastics May Be Cooling—and Heating—Earth’s Climate

Sat, 23 Oct 2021 00:00:00 +0000

Media | Microplastics in the air have a small cooling effect on our climate

Wed, 20 Oct 2021 00:00:00 +0000

Media | Microplastics are in the air we breathe and in Earth’s atmosphere, and they affect the climate

Wed, 20 Oct 2021 00:00:00 +0000

Article | Direct radiative effects of airborne microplastics

Revell et al. — Wed, 20 Oct 2021 00:00:00 +0100

Microplastics are now recognized as widespread contaminants in the atmosphere, where, due to their small size and low density, they can be transported with winds around the Earth^{1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25}. Atmospheric aerosols, such as mineral dust and other types of airborne particulate matter, influence Earth’s climate by absorbing and scattering radiation (direct radiative effects) and their impacts are commonly quantified with the effective radiative forcing (ERF) metric²⁶. However, the radiative effects of airborne microplastics and associated implications for global climate are unknown. Here we present calculations of the optical properties and direct radiative effects of airborne microplastics (excluding aerosol–cloud interactions). The ERF of airborne microplastics is computed to be 0.044 ± 0.399 milliwatts per square metre in the present-day atmosphere assuming a uniform surface concentration of 1 microplastic particle per cubic metre and a vertical distribution up to 10 kilometres altitude. However, there are large uncertainties in the geographical and vertical distribution of microplastics. Assuming that they are confined to the boundary layer, shortwave effects dominate and the microplastic ERF is approximately −0.746 ± 0.553 milliwatts per square metre. Compared with the total ERF due to aerosol–radiation interactions²⁷ (−0.71 to −0.14 watts per square metre), the microplastic ERF is small. However, plastic production has increased rapidly over the past 70 years²⁸; without serious attempts to overhaul plastic production and waste-management practices, the abundance and ERF of airborne microplastics will continue to increase.

Article | Southern Ocean Cloud and Aerosol data: a compilation of measurements from the 2018 Southern Ocean Ross Sea Marine Ecosystems and Environment voyage

Kremser et al. — Fri, 02 Jul 2021 00:00:00 +0100

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

Article | The winter 2019 air pollution (PM2.5) measurement campaign in Christchurch, New Zealand

Dale et al. — Tue, 18 May 2021 00:00:00 +0100

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).

Poster | Climate sensitivity and the Southern Ocean: the effect of the “too few, too bright” model cloud problem

Kuma and Bender — Wed, 21 Apr 2021 00:00:00 +0100

Equilibrium and transient climate sensitivity (ECS and TCS) are some of the most fundamental properties characterising the future climate. Progress in estimating climate sensitivity over the last three decades has been hampered by a large climate model spread of ECS and TCS estimates, and more recently by a large increase in ECS predicted by several models in the latest generation of the Climate Model Intercomparison Project 6 (CMIP6). Clouds have been identified as the major source of this uncertainty and the recent increase in estimated ECS. A “too few, too bright” model cloud problem has been found in several regions of the globe, including tropical latitudes and the Southern Ocean. Southern Ocean has also been a major focus of changes in model microphysics in an effort to simulate more realistic supercooled liquid clouds. Here, we focus on the too few, too bright problem in the Southern Ocean in CMIP6 models and its possible relation to climate sensitivity. We explore the possibility of applying new emergent constraints on climate sensitivity based on metrics of the too few, too bright problem. We use satellite and and ship-based observational datasets such as lidar and radiometer observations for constraining climate sensitivity and evaluation of clouds in this region across generations of CMIP models.

Article | Classification of the Below-Cloud Mixing State Over the Southern Ocean Using In-Situ and Remotely-Sensed Measurements

Hartery et al. — Fri, 19 Mar 2021 00:00:00 +0000

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 mixing state of the atmospheric layer beneath the lowest observed cloud, where the relationship is defined by the Spearman Rank correlation. The accuracy of this correlation-based method was compared to two methods of detecting boundary layer decoupling based on radiosonde measurements. An optimized version of the new methodology correctly determined the mixing state of the below-cloud layer for 76 ± 4% of the radiosondes available for comparison. Further, it was more accurate than an alternative ground-based metric used to determine the below-cloud mixing state. For the majority of the time series in which the correlation analysis could be applied, the below-cloud boundary layer was well-mixed (54%), or else fog was present (27%), which indicated that aerosol particles observed at sea-level often have a direct pathway into low-cloud (81%). In the remaining analysis period, the near-surface atmospheric layer was stable and the atmospheric layer near the ocean surface was decoupled from the overlying cloud (19%). Forecasts from the Antarctic Mesoscale Prediction System also support our findings, showing that conditions that mix aerosol particles from the ocean surface to the lowest observed cloud occur 84% of the time over the open Southern Ocean. As a result, aerosol particles measured near sea-level are often tightly coupled to low-cloud formation over the Southern Ocean, highlighting the utility of shipborne aerosol observations in the region.

Article | Ground-based lidar processing and simulator framework for comparing models and observations (ALCF 1.0)

Kuma et al. — Wed, 06 Jan 2021 00:00:00 +0000

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.

Media | Clouds over the Southern Ocean hold the key to better climate change predictions, study says

Sat, 08 Aug 2020 00:00:00 +0000

Dataset | Southern Ocean Cloud and Aerosol data set: a compilation of measurements from the 2018 Southern Ocean Ross Sea Marine Ecosystems and Environment voyage

Kremser et al. — Fri, 30 Oct 2020 00:00:00 +0000

Presentation | Doctoral thesis presentation: Comparing remotely sensed observations of clouds and aerosols in the Southern Ocean with climate model simulations

Kuma et al. — Wed, 07 Oct 2020 00:00:00 +0100

Doctoral thesis presentation given at the University of Canterbury, Christchurch, New Zealand on 7 October 2020.

Article | Evaluation of Southern Ocean cloud in the HadGEM3 general circulation model and MERRA-2 reanalysis using ship-based observations

Kuma et al. — Fri, 05 Jun 2020 00:00:00 +0100

Thesis | Comparing remotely sensed observations of clouds and aerosols in the Southern Ocean with climate model simulations

Kuma — Thu, 19 Nov 2020 00:00:00 +0000

Southern Ocean (SO) shortwave (SW) radiation biases are a common problem in contemporary general circulation models (GCMs), with most models exhibiting a tendency to absorb too much incoming SW radiation. These biases have been attributed to deficiencies in the representation of clouds during the austral summer months, either due to cloud cover or cloud albedo being too low. They affect simulation of New Zealand (NZ) and global climate in GCMs due to excessive heating of the sea surface and the effect on large-scale circulation. Therefore, improvement of GCMs is necessary for accurate prediction of future NZ and global climate. We performed ship-based lidar, radar, radiosonde and weather observations on two SO voyages and processed data from multiple past SO voyages. We used the observations and satellite measurements for evaluation of the Hadley Centre Global Environmental Model version 3 (HadGEM3) and contrasting with the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) to better understand the source of the problem. Due to the nature of lidar observations (the laser signal is quickly attenuated by clouds) they cannot be used for 1:1 comparison with a model without using a lidar simulator, which performs atmospheric radiative transfer calculations of the laser signal. We modify an existing satellite lidar simulator present in the Cloud Feedback Model Intercomparison Project (CFMIP) Observational Simulator Package (COSP) for use with the ground-based lidars used in our observations by modifying the geometry of the radiative transfer calculations, Mie and Rayleigh scattering of the laser signal. We document and make the modified lidar simulator available to the scientific community as part of a newly-developed lidar processing tool called the Automatic Lidar and Ceilometer Framework (ALCF), which enables unbiased comparison between lidar observations and models by performing calibration of lidar backscatter, noise removal and consistent cloud detection. We apply the lidar simulator on HadGEM3 model fields. Significant SW radiation errors in the SO of up to 21 Wm⁻² are shown to be present in the model. Using the lidar observations, we find that the model underestimates overall cloud cover by about 9% and strongly underestimates boundary layer low-level stratocumulus (Sc) cloud below 1 km and fog. By using radiosonde observations, we find that the observed cloud was strongly linked to the boundary layer stability and sea surface temperature, while this relationship is weaker in the model. We identify that these errors are not due to misrepresentation of large-scale circulation, which is prescribed in our model based on global satellite observations by nudging. We conclude that the problem is likely in the subgrid-scale parametrisation schemes of the boundary layer, convection and large-scale could. In order to address the deficiencies identified we perform experimental simulations of HadGEM3 with modifications of the parametrisation schemes. We find that a three-layer cloud profiles were common in the Ross Sea region, consisting of cumulus (Cu) below Sc, and corresponding to local thermodynamic levels: lifting condensation level, dry and moist neutral buoyancy levels of parcels lifted from the surface. We find that not enough moisture is transported to the top of the boundary layer to form Sc clouds. By increasing surface moisture flux and convective mass flux in the model we improve the Sc cloud simulation, but we show that a lack of vertical moisture transport across the lifting condensation level from the surface layer to the zone of convective mass flux is a likely limiting factor. We show that the modifications had a positive impact on the Southern Ocean and global radiation balance of up to 5 Wm⁻² in zonal average over this limited time period. We suggest that further research should focus on the weak vertical coupling between the boundary layer turbulence and boundary layer convection parametrisation in the model, and that the lidar simulator framework is used as a cloud evaluation tool in further studies due to its benefits over more statistical approaches, which tend to hide compensating biases.

Article | The state of the atmosphere in the 2016 southern Kerguelen Axis campaign region

Klekociuk et al. — Wed, 01 Apr 2020 00:00:00 +0100

The near-surface environment of the Southern Ocean is subject to particular biases in weather and climate simulations, particularly during the summer season, and relatively few analyses of cloud and radiation properties have been reported for the region. Here we provide an analysis of ship-based measurements of downwelling radiation, cloud fraction and cloud base height from the RSV Aurora Australis during the Kerguelen Axis marine science campaign which was conducted in the Southern Ocean south-east of the Kerguelen Plateau between January and March 2016. Our study period focussed on a 22-day interval during the first two months of the campaign. We compared estimates of cloud fraction obtained with a cloud imager and ceilometer, and found good agreement between the two measurement types, particularly when the camera images were analysed in a narrow overhead field to account for differences in the measurement techniques. We used the Interim European Centre for Medium-Range Weather Forecasts Reanalysis (ERA-Interim) and the Antarctic Mesoscale Prediction System Polar Weather and Research Forecasting model (Polar WRF) to provide comparison data for our measurements. We found that both comparison data sets generally underestimated cloud cover (observed cloud fraction ~0.96 compared with 0.87 for ERA-Interim and 0.63 for Polar WRF). As a consequence, the comparison data showed biases in both the surface shortwave irradiance (+59 W m⁻² for ERA-Interim and +154 W m⁻² for Polar WRF) and the longwave irradiance (−23 W m⁻² for ERA-Interim and −46 W m⁻² for Polar WRF). The observed mean net surface cloud radiative effect (CRE) of −228 W m⁻² was significantly more negative than found in previous observations in the Southern Ocean region, and compares with a net surface CRE of − 138 W m⁻² for ERA-Interim which also showed relatively strong cloud forcing. The observed net surface CRE bias for ERA-Interim of + 90 W m⁻² appears primarily the result of the reanalysis underestimating the cloud fraction, which at least partly relates to a lack of low clouds. Polar WRF was also found to have a deficit of low clouds. We characterised the relationship between the ratio of irradiances by Photosynthetically Active Radiation (PAR) and shortwave radiation and cloud transmittance. As a consequence of cloud, light levels were estimated as being below the level for light-limited photosynthesis during 31% of the available time the sun was above the horizon (69% of each day on average), compared with the expected clear-sky value of 10%. Over the campaign period, the Indian Ocean sector of the Southern Ocean was influenced by the positive phase of the Southern Annular Mode (SAM). Notably, the surface SAM index in January and March was the most positive observed since 1957. This situation generally led to near-surface climatological differences over the southern part of the campaign region over much of the period, which included significant negative anomalies in mean sea level pressure and air temperature, and positive anomalies in zonal wind. Overall, the cloudiness of our study region appeared to be above average for the time of year, but we could not identify a clear cause for this in the prevailing climatic conditions. While the level of shortwave radiation was likely below average for the time of year, this deficit is not likely to have significantly impacted on photosynthesis in the mixed layer of the ocean.

Poster | Ground-based lidar processing and simulator framework for comparing models and observations

Kuma et al. — Thu, 30 Jan 2020 00:00:00 +0000

Atmospheric lidar measurements are a well-established tool for remote sensing of clouds. For over a decade, spaceborne lidar measurements produced by the CALIOP instrument on the CALIPSO satellite and CATS on the International Space Station have proven invaluable for model cloud evaluation in general circulation and numerical weather forecasting models. They have revealed the vertical structure of clouds, particularly in combination with radar instruments, which is impossible to obtain with passive remote sensing instruments. However, the measurements are limited by rapid attenuation of the lidar signal in thick clouds. Ground-based lidar measurements are becoming more common due to greater availability of instruments such as ceilometers installed on a wide scale globally. They can provide much needed lidar measurements of clouds ”from below”, but processing of lidar data and model evaluation using this data is not well-developed compared to satellite measurements. We present an open source tool called the Automatic Lidar and Ceilometer Framework (ALCF) which implements common lidar processing steps such as resampling, noise removal, cloud detection, calculation of statistics, as well as model—observation intercomparison by bundling the COSP/ACTSIM lidar simulator and allowing it to produce ”curtain” lidar pseudo-measurements from model output of various models (MERRA-2, AMPS, CMIP5) corresponding to ground-based and shipborne instruments (Vaisala CL31, CL51, Lufft CHM 15k, Sigma Space MiniMPL). These pseudo-measurements can be compared in an ”apples to apples” comparison with observations. We hope this tool will enable ground-based lidars to be used more commonly for model evaluation and improvement efforts.

Article | Constraining the Surface Flux of Sea Spray Particles from the Southern Ocean

Hartery et al. — Fri, 24 Jan 2020 00:00:00 +0000

Modeling the shortwave radiation balance over the Southern Ocean region remains a challenge for Earth system models. To investigate whether this is related to the representation of aerosol-cloud interactions, we compared measurements of the total number concentration of sea spray-generated particles within the Southern Ocean region to model predictions thereof. Measurements were conducted from a container laboratory aboard the R/V Tangaroa throughout an austral summer voyage to the Ross Sea. We used source-receptor modeling to calculate the sensitivity of our measurements to upwind surface fluxes. From this approach, we could constrain empirical parameterizations of sea spray surface flux based on surface wind speed and sea surface temperature. A newly tuned parameterization for the flux of sea spray particles based on the near-surface wind speed is presented. Comparisons to existing model parameterizations revealed that present model parameterizations led to overestimations of sea spray concentrations. In contrast to previous studies, we found that including sea surface temperature as an explanatory variable did not substantially improve model-measurement agreement. To test whether or not the parameterization may be applicable globally, we conducted a regression analysis using a database of in situ whitecap measurements. We found that the key fitting parameter within this regression agreed well with the parameterization of sea spray flux. Finally, we compared calculations from the best model of surface flux to boundary layer measurements collected onboard an aircraft throughout the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES), finding good agreement overall.

Poster | Automatic Lidar and Ceilometer Framework (ALCF)

Kuma et al. — Mon, 30 Sep 2019 00:00:00 +0100

Poster | Evaluation of Southern Ocean cloud in the HadGEM3 general circulation model and MERRA-2 reanalysis using ship-based observations

Kuma et al. — Mon, 06 May 2019 00:00:00 +0100

Southern Ocean shortwave radiation biases of up to 40 Wm^-2 in summer are common in general circulation models, with misrepresentation of cloud identified as the major cause. We evaluate the atmospheric component GA7.0 and GA7.1 of the HadGEM3 general circulation model and the MERRA-2 reanalysis, and find that GA7.0 and GA7.1 underestimate the reflected top of atmosphere shortwave radiation, while MERRA-2 overestimates this quantity. Using a dataset of ship ceilometer and radiosonde observations we evaluate cloud cover and link it to the thermodynamic profile. We find low cloud below 2 km and fog predominant and cloud cover exceeding 90% in most regions. We show that this cloud is strongly linked to boundary layer stability and sea surface temperature. Using a ground-based lidar simulator we produce virtual ceilometer measurements along the voyage tracks for a 1:1 comparison with the ceilometer measurements. We find that GA7.0 and MERRA-2 underestimate cloud cover by 18–25%, especially cloud below 1 km and fog. While the boundary layer stability is well represented in GA7.0 and MERRA-2, the link between the boundary layer stability and cloud found in observations is not present in the models, pointing to deficiencies in the subgrid scale parametrisation of cloud.

Presentation | Evaluation of HadGEM3 Southern Ocean cloud using observations and reanalyses

Kuma et al. — Wed, 05 Dec 2018 00:00:00 +0000

Current general circulation models are affected by biases in simulated clouds in the Southern Ocean (SO) which are despite some recent progress are still too large, resulting in biases in shortwave and longwave radiative transfer. These biases are also present in the HadGEM3 model, a UK Met Office model which has been a focus of development in the Deep South National Science Challenge with the aim to more accurately project future climate in New Zealand. The amount of cloud has more ice phase than expected. Satellite observations have been used extensively to study this problem, but the predominantly low level cloud in the SO cannot be reliably observed from space due to the prevalence of overlapping cloud and other limitations such as ground clutter in active instruments. We use observational data from a number of SO voyages to assess clouds in HadGEM3, and contrast it with the MERRA-2 reanalysis, which provides very different cloud phase and cloud occurrence results in the SO. We use ceilometer observations collected on voyages to assess cloud vertical distribution, mini micropulse lidar observations to assess cloud phase and radiosonde observations to assess tropospheric stability and humidity profiles. The ceilometer observations cannot be compared directly with the model due to attenuation of the lidar beam in thick cloud, and we use a ceilometer simulator developed for this project for a like-for-like comparison. We also apply this evaluation to a number of experimental HadGEM3 runs produced with different choices of cloud scheme parameters in order to fix the SO cloud bias.

Article | An analysis of the cloud environment over the Ross Sea and Ross Ice Shelf using CloudSat/CALIPSO satellite observations: The importance of synoptic forcing

Jolly et al. — Tue, 10 Jul 2018 00:00:00 +0100

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.

Poster | Shipborne and ground-based observations of clouds in the Southern Ocean

Kuma et al. — Tue, 19 Jun 2018 00:00:00 +0100

The Southern Ocean is characterised by sparse ground-based and in-situ atmospheric measurements. While satellite measurements provide continuous spatial and temporal coverage, they are generally not capable of observing low-level clouds and the cloud base, which are critical for accurately modelling radiative transfer. Results from general circulation models show significant biases in outgoing shortwave radiation in this region, believed to be related to deficiencies in representation of clouds, aerosols or their interaction. As part of the Cloud and Aerosol project of the New Zealand Deep South Challenge (DSC) we collected and analysed cloud measurements from multiple shipborne and ground-based deployments of several meteorological instruments: ceilometer, lidar, micro rain radar, radio soundings, aerosol sensors, sky cameras and UAV-borne sensors. With this combination of instruments we hope to advance understanding of cloud processes in this region, quantify model error compared to observations and contribute to the modelling effort of the DSC. Currently we have collected observations from a ground-based deployment on Macquarie Island and multiple voyages: Aurora Australis, R/V Tangaroa, HMNZS Wellington and R/V Nathaniel B. Palmer. By incorporating data from these and planned future deployments we intend to produce a Southern Ocean dataset of atmospheric measurements available for general use. This presentation will provide an overview of our progress and results.

Presentation | Doctoral Confirmation Presentation: Assessment of Southern Ocean Clouds and Aerosol in General Circulation Models

Kuma et al. — Mon, 09 Apr 2018 00:00:00 +0100

Doctoral confirmation presentation given at the University of Canterbury, Christchurch, New Zealand on 9 April 2018.

Media | New Zealand’s Next Top Model

Mon, 01 Jan 2018 00:00:00 +0000

Presentation | Assessment of Southern Ocean clouds and aerosols in the New Zealand Earth System Model using shipborne and ground-based observations

Kuma et al. — Mon, 13 Nov 2017 00:00:00 +0000

We present a multi-year dataset of shipborne and ground-based ceilometer, radar and aerosol observations in the Southern Ocean, which allows for cloud to be seen “from below”. In particular, we discuss the use the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) to compare the ceilometer measurements with NZESM simulations. The COSP simulator currently does not support ground-based lidars with a matching wavelength, but the ACTSIM lidar simulator in COSP requires only a few modifications to support the analysis of ceilometer data. Using an instrument simulator such as COSP allows us to account for the limited view of the ceilometer and signal attenuation in the atmosphere.

We apply the newly developed ceilometer simulator to the NZESM atmospheric state output in the regions of the Southern Ocean where shipborne or ground-based observations are available, and compare the resulting backscatter distribution and algorithmically derived products such as cloud base between the model and observations.

Poster | Assessment of Southern Ocean clouds and aerosols in the New Zealand Earth System Model using shipborne and ground-based observations

Kuma et al. — Mon, 04 Sep 2017 00:00:00 +0100

One of the primary objectives of the New Zealand Earth System Model (NZESM) is to reduce shortwave radiation biases over the Southern Ocean, which are related to deficiencies in representation of clouds and aerosols in this region. This is a subject of active research with multiple hypotheses being tested including cloud microphysics, cloud-aerosol interaction, horizontal homogeneity and differences in frequency of cloud regimes related to different weather systems being examined. Comparison with observations is necessary for the identification and resolution of the deficiencies. Unfortunately, observations in the Southern Ocean are scarce, with satellites providing the most extensive spatial and temporal coverage, especially instruments such as MODIS and ISCCP and active instruments such as radar and lidar on the CloudSat and CALIPSO satellites. However, these instruments lack the capability to observe low-level cloud when there is a higher-level overlapping cloud. We present a new multi-year dataset of shipborne and ground-based ceilometer, radar and aerosol observations in the Southern Ocean, which allows for cloud to be seen ‘from below’ and assess the cloud-aerosol interaction. We also discuss the use the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) to compare the ceilometer measurements with NZESM simulations. The COSP simulator currently does not support ground-based lidars with a matching wavelength, but we have identified that the ACTSIM lidar simulator in COSP requires only a few modifications to support the analysis of ceilometer data. Using an instrument simulator such as COSP allows us to account for the limited view of the ceilometer and signal attenuation in the atmosphere. We therefore discuss future efforts and extending the capability of the COSP simulator.

Poster | Shipborne and ground-based observations of clouds in the Subantarctic and the Southern Ocean

Kuma et al. — Mon, 26 Jun 2017 00:00:00 +0100

The Subantarctic and the Southern Ocean are regions where there are sparse ground-based and in-situ cloud measurements. While satellite measurements provide continuous spatial and temporal coverage, they are generally not capable of observing low-level clouds and the cloud base, which are critical for understanding the radiative energy budget. Results from general circulation models show bias in the shortwave radiation in this region, related to representation of clouds. As part of the Cloud and Aerosol project of the New Zealand Deep South National Science Challenge (DSC) we aim to collect and analyse cloud measurements from shipborne and ground-based deployments of several meteorological instruments: a near-infrared ceilometer, a micro rain radar and radio soundings. With this combination of multiple instruments we hope to advance the understanding of cloud processes in this region, quantify model errors compared to observations and contribute to the modelling effort of the DSC. Currently we have collected observations from a ground-based deployment on the Macquarie Island and multiple ship deployments: Aurora Australis, RV Tangaroa, HMNZS Wellington and R/V Nathaniel B. Palmer. By incorporating data from these and planned future deployments we intend to compile and maintain a Southern Ocean cloud dataset which will be available for general use. This presentation will provide an overview of our current progress and preliminary results.

Article | Single interval longwave radiation scheme based on the net exchanged rate decomposition with bracketing

Geleyn et al. — Mon, 17 Apr 2017 00:00:00 +0100

The main obstacle to efficient calculation of longwave radiative transfer is the existence of multiple radiative sources, each with its own emission spectrum. The work presented here overcomes this problem by combining the full spectrum broadband approach with the net exchanged rate decomposition. The idea is worked out to suit the needs of numerical weather prediction, where the most costly contribution representing the sum of internal exchanges is interpolated between cheap minimum and maximum estimates, while exchange with the surface and dominant cooling to space contributions are calculated accurately. The broad-band approach must address the additional problems related to spectral integration and many ideas developed previously for the solar spectrum are reused. Specific issues appear, the dependence of broadband gaseous transmissions on the temperature of the emitting body being the most important one. The thermal spectrum also brings some simplifications—aerosols, clouds and the Earth's surface can safely be treated as grey bodies. The optical saturation of gaseous absorption remains the main complication and non-random spectral overlaps between gases become much more significant than in the solar spectrum. The broadband character of the proposed scheme enables the use of an unreduced spatial resolution with an intermittent update of gaseous transmissions and interpolation weights, thus ensuring a full response of longwave radiation to rapidly varying cloudiness and temperature fields. This is in contrast to the mainstream strategy, where very accurate and expensive radiative transfer calculations are performed infrequently, often with reduced spatial resolution. The approach proposed here provides a much better balance between errors coming from the radiation scheme itself and from the intermittency strategy. The key achievement, ensuring a good scalability of the scheme, is a computational cost essentially linear in the number of layers, with straightforward inclusion of scattering as an additional bonus.

Article | Single interval shortwave radiation scheme with parameterized optical saturation and spectral overlaps

Mašek et al. — Mon, 17 Aug 2015 00:00:00 +0100

Spectral integration is the most time consuming part of solar radiative transfer codes used in numerical weather prediction. Routinely used approaches usually incline to one of two extremes – expensive and very accurate correlated k-distribution method made affordable by doing radiative transfer calculations with reduced temporal and/or spatial resolution, or cheaper but less accurate broadband approach affordable at every grid-point and time-step. Both approaches have their pros and cons, but hybrid solutions do not seem very promising. The presented work improves accuracy of full spectrum broadband approach by parameterizing secondary saturation of gaseous absorption, optical saturation of Rayleigh scattering and of cloud absorption as well as non-random gas-cloud spectral overlap. In order to isolate the problem of spectral integration from other approximations, one builds a narrowband reference using the same delta-two stream framework as the broadband scheme. Using this reference reveals the surprising fact that saturation effect of cloud absorption for one single layer and for the whole solar spectrum can be parameterized in a rather compact way, with one simple formula for liquid clouds and one for ice clouds. One then introduces the concept of effective cloud optical depth, which extends the applicability of parameterized cloud optical saturation to multi-layer cases, accommodating also effects of gas-cloud spectral overlap in the near-infrared. A scheme with all the above parameterizations indeed pushes accuracy limits of broadband approach to the level where a single shortwave interval can be used. This opens the possibility to reduce costs by using selective intermittency, where slowly evolving gaseous transmissions are updated on the timescale of hours, while quickly varying cloud optical properties are recomputed at every model time-step. In a companion article it will be demonstrated that the above core strategy is applicable also to thermal radiative transfer, with perhaps even better cost effectiveness there.

Thesis | Broadband approach as a framework for implementation of radiative transfer scheme with selective intermittency: Cost versus accuracy study

Kuma — Tue, 09 Jun 2015 00:00:00 +0100

The computational complexity of radiation schemes in NWP models precludes full radiative transfer calculations in every time step and every grid point of the model. Traditionally, models resort to calling a radiation scheme on a reduced temporal or spatial resolution, optionally scaling the resulting fluxes for the change in temperature profile and the solar zenith angle. As a result, the variability of cloud cover is neglected, leading to a considerable error. In the shortwave spectrum, relatively slowly changing gaseous optical properties are one of the most expensive parts to calculate. We propose a modification to the shortwave part of the ACRANEB2 broadband radiation scheme to interpolate gaseous optical thickness of layers with respect to the solar zenith angle within a chosen intermittency period, while still accounting for evolving cloudiness by recalculating its optical properties and the resulting fluxes via the adding method in every model time step. In this work we use a single column model to study the dependence of shortwave gaseous optical thickness on the solar zenith angle, we show that this dependence can be approximated with good accuracy, implement this approximation in the ACRANEB2 radiation scheme and assess the impact on accuracy of heating rates and model run time in 24-h simulations of the limited-area NWP model ALADIN. We show that the modification results in time saving of up to 4 % of total model run time and incurs error on shortwave heating rates up to ±0.4 K/day at noon (90 % confidence interval) and 0.06 K/day (MAE) over the whole domain and time period, and that both performance and accuracy scale with the length of the intermittency period. This relationship is summarised in a cost vs. accuracy comparison, giving potential users a guide on choosing the optimal intermittency period in their circumstances. The proposed modification became part of the ACRANEB2 radiation scheme implemented in package ALARO-1 version A, and since January 2015 it is operational in the model ALADIN/CHMI.

Thesis | Visualising Data from CloudSat and CALIPSO Satellites

Kuma — Thu, 01 Jul 2010 00:00:00 +0100

CloudSat and CALIPSO are NASA, resp. joint NASA and CNES polar-orbiting Earth observation satellites. CloudSat carries a millimetre-wave radar for observation of clouds. CALIPSO carries a visible and infrared polarisation-sensitive lidar for observation of aerosols and ice-phase clouds. Data from these satellites are distributed in the form of HDF4 and HDF-EOS2 product files. We introduce a software tool ccplot capable of visualising several data sets from the CloudSat 2B-GEOPROF, CALIPSO Lidar L1B Profiles, CALIPSO Lidar L2 Cloud Layer and Aqua MODIS L1B products. ccplot is a scriptable, unix command-line tool. We released ccplot on the Internet under the open-source-compatible BSD license.