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Cloud microphysics is critical for weather and climate prediction. In this work, we document updates and corrections to the cloud microphysical scheme used in the Community Earth System Model (CESM) and other models. These updates include a new nomenclature for the scheme, now called Parameterization of Unified Microphysics Across Scales (PUMAS), and the ability to run the scheme on graphics processing units (GPUs). The main science changes include refactoring an ice number limiter and associated changes to ice nucleation, adding vapor deposition onto snow, and introducing an implicit numerical treatment for sedimentation. We also detail the improvements in computational performance that can be achieved with GPU acceleration. We then show the impact of these scheme changes on the (a) mean state climate, (b) cloud feedback response to warming, and (c) aerosol forcing. We find that corrections are needed to the immersion freezing parameterization and that ice nucleation has important impacts on climate. We also find that the revised scheme produces less cloud liquid and ice but that this can be adjusted by changing the loss process for cloud liquid (autoconversion). Furthermore, there are few discernible effects of the PUMAS changes on cloud feedbacks but some reductions in the magnitude of aerosol–cloud interactions (ACIs). Small cloud feedback changes appear to be related to the implicit sedimentation scheme, with a number of factors affecting ACIs.

In common weather and climate models rime-splintering is the only included secondary ice production process. In addition, assumed concentrations of cloud condensation nuclei or cloud droplet number as well as of ice-nucleating particle are higher in standard models than for Arctic environments. This can lead to a misrepresentation of phase distribution in and precipitation from Arctic mixed-phase clouds.During the Ny-Ålesund Aerosol Cloud Experiment (NASCENT) a holographic probe mounted on a tethered balloon took in-situ measurements of ice crystal and cloud droplet number and mass concentrations in Svalbard, Norway, during fall 2019 and spring 2020. In this study, we use the Weather Research and Forecasting (WRF) model to simulate selected case studies from this campaign in a high vertical and spatial resolution. We test the performance of different microphysical parametrizations and apply a new state-of-the-art secondary ice parametrization. We find that the degree of agreement with observations highly depends on the chosen microphysics settings and that for a case with a lot of observed secondary ice production modeled ice crystal concentrations stay below the measured values even after adding new secondary ice processes to the model.In addition to performing sensitivity studies regarding microphysical parametrizations and the direct comparison with observations from the balloon, we analyze the spatial variability of cloud phase and its dependence on different meteorological parameters., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Shortwave radiative feedbacks from Southern Ocean clouds are a major source of uncertainty in climate projections. Much of this uncertainty arises from changes in cloud scattering properties and lifetimes that are caused by changes in cloud thermodynamic phase. Here we use satellite observations to infer the scattering component of the cloud-phase feedback mechanism and determine its relative importance by comparing it with an estimate of the overall temperature-driven cloud feedback. The overall feedback is dominated by an optical thinning of low-level clouds. In contrast, the scattering component of cloud-phase feedback is an order of magnitude smaller and is primarily confined to free-tropospheric clouds. The small magnitude of this feedback component is a consequence of counteracting changes in albedo from cloud optical thickening and enhanced forward scattering by cloud particles. These results indicate that shortwave cloud feedback is likely positive over the Southern Ocean and that changes in cloud scattering properties arising from phase changes make a small contribution to the overall feedback. The feedback constraints shift the projected 66% confidence range for the global equilibrium temperature response to doubling atmospheric CO2 by about +0.1 K relative to a recent consensus estimate of cloud feedback. Significance Statement Understanding how clouds respond to global warming is a key challenge of climate science. One particularly uncertain aspect of the cloud response involves a conversion of ice particles to liquid droplets in extratropical clouds. Here we use satellite data to infer how cloud-phase conversions affect climate by changing cloud albedo. We find that ice-to-liquid conversions increase cloud optical thickness and shift the scattering angles of cloud particles toward the forward direction. These changes in optical properties have offsetting effects on cloud albedo. This finding provides new insight about how changes in cloud phase affect climate change.

Precipitation efficiency (PE) relates cloud condensation to precipitation and intrinsically binds atmospheric circulation to the hydrological cycle. Due to PE’s inherent microphysical dependencies, definitions and estimates vary immensely. Consequently, PE’s sensitivity to greenhouse warming and implications for climate change are poorly understood. Here, we quantify PE’s role in climate change by defining a simple index ϵ as the ratio of surface precipitation to condensed water path. This macroscopic metric is reconcilable with microphysical PE measures and higher ϵ is associated with stronger mean Walker circulation. We further find that state-of-the-art climate models disagree on the sign and magnitude of future ϵ changes. This sign disagreement originates from models’ convective parameterizations. Critically, models with increasing ϵ under greenhouse warming, in line with cloud-resolving simulations, show greater slowdown of the large-scale Hadley and Walker circulations and a two-fold greater increase in extreme rainfall than models with decreasing ϵ.

Living downwind of a cement-producing or a metallurgical plant could mean you get more snow, fewer clouds and more sunshine compared to nearby areas. We use satellite observations to reveal the glaciation of supercooled stratiform liquid-phase clouds by anthropogenic aerosols acting as ice-nucleating particles. There are strong indications that glaciation is caused by aerosols emitted from oil refineries, coal-fired power plants, cement, metal smelting and processing, chemical plants, and other anthropogenic air pollution sources in Europe, Asia, North America and Australia. Heavily polluted areas derived by simulating aerosol dispersion from strong anthropogenic aerosol point sources overlap with the areas of glaciation, snowfall, and decreased cloud cover strikingly well. Moreover, the polluted areas with decreased cloud cover are plume-shaped, with a distinctive head pointing towards the pollution source, similar to aerosol-polluted cloud tracks in liquid-water clouds (Toll et al 2019 Nature https://doi.org/10.1038/s41586-019-1423-9).Glaciation-induced snowfall downwind of aerosol sources is observed using ground-based precipitation radars, and tracks of snow are also seen on the ground in satellite imagery. Aerosol-induced glaciation and snowfall lead to reduced cloud cover. At multiple aerosol sources, glaciation events are more frequent than polluted tracks in liquid-phase clouds. Thus, at least locally at some aerosol sources in the middle and high latitudes, the warming effect induced by aerosols acting as ice-nucleating particles likely exceeds the cooling effect induced by aerosols acting as cloud condensation nuclei. Further research is needed to quantify the global radiative forcing by anthropogenic ice nucleating particles.

The mechanisms that amplify orbitally-driven changes in insolation and drive the glacial cycles of the past 2.6 million years, the Pleistocene, are poorly understood. Previous studies indicate that cloud-phase feedbacks oppose ice sheet initiation when orbital configuration supports ice sheet growth. Cloud phase was observationally constrained in a recent study and provides evidence for a weaker negative cloud feedback in response to carbon dioxide doubling. We observationally constrain cloud phase in the Community Earth System Model and explore how changes in orbital configuration impact the climate response. Constraining cloud phase weakens the negative high latitude cloud phase feedback and unmasks positive water vapor and cloud feedbacks (amount and optical depth) that extend cooling to lower latitudes. Snowfall accumulation and ablation metrics also support ice sheet expansion as seen in proxy records. This indicates that well-known cloud and water vapor feedbacks are the mechanisms amplifying orbital climate forcing.

In a rapidly changing climate, changes in the frequency and intensity of future extreme weather events is expected to pose complex impact on futureweather-dependent energy systems. The contribution by Working Group I to the latest IPCC report (AR6) includes for the first time a dedicated chapteron weather extremes. It demonstrates that even relatively small incremental increases in global mean temperature (+0.5◦C) cause statistically significantchanges in extremes on the global scale and for large regions. In 2021, Europe experienced the worst wind drought in 60 years, followed by extreme heatwave episodes and a national daily maximum temperature record of 40.3◦C in the UK in 2022.The transition of the energy sector to renewable energy is key to reach the Paris Agreement goal of limiting global warming, but this also increases the energy sector’s exposure to future weather extremes in a changing climate. The impacts of climate variability and climate change on present and near future national energy systems are increasingly well documented within the literature. However, within this interdisciplinary field of research there has been little attention on impacts of extreme weather events on the energy system operations. Modellers usually use historic weather data and therefore do not consider ”new” extreme weather risks, posing problems on both the operational and infrastructural side. Omitting those risks can lead to designing systems that are operationally inadequate, i.e. prone to (long) power outages leading to major social, economic and health consequences.The present work aims to study the variability of renewable energy generation for weather years under different future climates over Europe. We will use different future climate scenarios from the Coupled Model Intercomparison Project 6th Phase (CMIP6), to assess future projections of wind energy capacity factors. The global datasets of climate data will be adapted for Europe where we will interpolate the wind speed based on model-level raw data and further create energy capacity factors. We expect this study to be a first step in exploring the future changes in and occurrences of extreme weather events, and how these will impact the generation of renewable energy over Europe. This will in turn contribute to the knowledge on how we can plan for climate resilient renewable energy systems robust to future extreme weather events over Europe.

Mineral dust aerosols impact Earth’s energy budget through interactions with radiation, clouds, atmospheric chemistry, the cryosphere and biogeochemistry. In this review, we summarize these interactions and assess the resulting impacts of dust, and of changes in dust, on global climate and climate change. We find that the total effect of these interactions on Earth’s global energy budget—the dust effective radiative effect—is -0.2 ± 0.5 Wm-2 (90% confidence interval). Compared to pre-industrial times, global dust mass loading is 55 ± 30% higher in the modern climate, leading to changes in the Earth’s energy budget. Indeed, this increase in dust has produced a global mean effective radiative forcing of -0.07 ± 0.18 Wm-2. Current climate models and climate assessments do not represent the historical increase in dust and thus omit the resulting radiative forcing, biasing climate change projections and assessments of climate sensitivity. Climate model simulations of future changes in dust diverge widely and are very uncertain. Further work is thus needed to constrain the radiative effects of dust on climate and to improve the representation of dust in climate models.

The Greenland Ice Sheet (GrIS) has been losing mass since the 1990s as a direct consequence of rising temperatures and has been projected to continue to lose mass at an accelerating pace throughout the 21st century, making it one of the largest contributors to future sea-level rise. The latest Climate Model Intercomparison Project 6th phase (CMIP6) models produce a greater Arctic amplification signal and therefore also a notably larger mass loss from the GrIS when compared to the older CMIP5 projections, despite similar forcing levels from greenhouse gas emissions. However, it is also argued that the strength of regional factors such as melt-albedo feedbacks and cloud-related feedbacks will partly impact future melt and sea-level rise contribution, but little is yet known about the role of these regional factors in differences in GrIS surface melt projections between CMIP6 to CMIP5. In this study, we use high-resolution (15 km) regional climate model simulations over the GrIS performed using the Modéle Atmosphériqe Régional (MAR) to physically downscale six CMIP5 RCP8.5 and five CMIP6 SSP5-8.5 extreme high-emission scenario simulations. Here, we show a greater annual mass loss from the GrIS at the end of the 21st century, but also for a given temperature increase over the GrIS, when comparing CMIP6 to CMIP5. We find a greater sensitivity of Greenland surface mass loss in CMIP6 centred around summer and autumn, yet the difference in mass loss is largest during autumn with a reduction of 14.1 ± 4.8 mmWE for a regional warming of +6.7 °C, 12.5 mmWE more mass loss than in CMIP5 RCP8.5 simulations for the same warming. Assessment of the surface energy budget and cloud-related feedbacks suggests a reduction in high clouds during summer and autumn – in addition to enhanced cloud optical depth during autumn – to be the main drivers of the additional energy reaching the surface, subsequently leading to enhanced surface melt and mass loss in CMIP6 compared to CMIP5. Our analysis highlights that Greenland is losing more mass in CMIP6 due to two factors; 1) a (known) greater sensitivity to greenhouse gas emissions and therefore warmer temperatures, 2) previously undocumented cloud-related surface energy budget changes that enhance the GrIS sensitivity to warming.

Mineral dust aerosols impact the energy budget of Earth through interactions with radiation, clouds, atmospheric chemistry, the cryosphere and biogeochemistry. In this Review, we summarize these interactions and assess the resulting impacts of dust, and of changes in dust, on global climate and climate change. The total effect of dust interactions on the global energy budget of Earth — the dust effective radiative effect — is −0.2 ± 0.5 W m−2 (90% confidence interval), suggesting that dust net cools the climate. Global dust mass loading has increased 55 ± 30% since pre-industrial times, driven largely by increases in dust from Asia and North Africa, leading to changes in the energy budget of Earth. Indeed, this increase in dust has produced a global mean effective radiative forcing of −0.07 ± 0.18 W m−2, somewhat counteracting greenhouse warming. Current climate models and climate assessments do not represent the historical increase in dust and thus omit the resulting radiative forcing, biasing climate change projections and assessments of climate sensitivity. Climate model simulations of future changes in dust diverge widely and are very uncertain. Further work is thus needed to constrain the radiative effects of dust on climate and to improve the representation of dust in climate models.

Mixed-phase clouds greatly affect projections of future climate, with recent evaluations highlighting the influence of the ice nucleation process in these clouds. Here we explore how this process affects climate sensitivity using simulations of the Community Earth System Model version 2 (CESM2). Ice nucleation is found to influence simulated cloud feedbacks not just over extratropical low clouds but over most regions and levels of the troposphere. However, the otherwise major influence of ice nucleation on total cloud feedback is negated when holding global mean cloud phase to observed levels. In satellite-constrained model experiments, dissimilar ice nucleation realizations all result in a strongly positive total cloud feedback, as in the default model. Global-scale cloud phase is hence confirmed to be the dominant link between ice nucleation and climate sensitivity. Conversely, whether ice nucleation is treated as aerosol-sensitive is found to be of limited importance. A microphysics update from CESM1 to CESM2 had substantially weakened ice nucleation in mixed-phase clouds, in part due to a model issue. Our findings suggest that this contributed to increased climate sensitivity primarily by reducing a global-scale cloud phase bias. Despite the issue, CESM2’s ice nucleation appears to form more realistic mixed-phase clouds than either a corrected implementation or CESM1’s ice nucleation scheme.

Ice crystals are an important component of clouds due to their strong impact on cloud radiative properties and precipitation formation. The shape of an ice crystal impacts its radiative effect, riming efficiency, and fall speed. The ice crystal shapes are dependent on (i) the environment (temperature and humidity) that they grow in and (ii) the microphysical processes (i.e. riming, aggregation) that they have experienced. These connections offer a great opportunity to trace back the previous in-cloud conditions and the microphysical processes in clouds. Thus, ice crystal shape classification is crucial to better understand radiation properties and precipitation formation of clouds. Scientists have explored and developed various algorithms to automatically classify ice crystal shapes in the past decades. Among those, the machine learning algorithm Convolutional Neural Network (CNN), shows a good performance due to its ability to catch the main features that describe ice crystal habits and recognize patterns between images. However, the existing classification methods all show an overlap between physical process categories (i.e. rimed) and basic habit categories (i.e. column) of ice crystals due to the existence of compound ice (i.e. column-rimed), especially in situations conducive to light riming. A CNN was trained using over 10’000 images of pristine and complex ice crystals recorded by a holographic imager during the NASCENT campaign (Pasquier et al., BAMS, in revision) in Fall 2019, in Ny-Ålesund, Svalbard. To avoid the overlap between physical process and basic habit categories of ice crystals, each ice label contains two properties; one of 7 basic habits property (i.e. column) and up to 3 physical process properties (aggregate, rimed, aged). The trained model gives us both the basic habit and the physical processes information, which helps us to better understand the microphysical processes in clouds.

The equilibrium climate sensitivity of Earth is defined as the global mean surface air temperature increase that follows a doubling of atmospheric carbon dioxide. For decades, global climate models have predicted it as between approximately 2 and 4.5 °C. However, a large subset of models participating in the 6th Coupled Model Intercomparison Project predict values exceeding 5 °C. The difference has been attributed to the radiative effects of clouds, which are better captured in these models, but the underlying physical mechanism and thus how realistic such high climate sensitivities are remain unclear. Here we analyse Community Earth System Model simulations and find that, as the climate warms, the progressive reduction of ice content in clouds relative to liquid leads to increased reflectivity and a negative feedback that restrains climate warming, in particular over the Southern Ocean. However, once the clouds are predominantly liquid, this negative feedback vanishes. Thereafter, other positive cloud feedback mechanisms dominate, leading to a transition to a high-sensitivity climate state. Although the exact timing and magnitude of the transition may be model dependent, our findings suggest that the state dependence of the cloud-phase feedbacks is a crucial factor in the evolution of Earth’s climate sensitivity with warming. Simulations suggest a shift to a high sensitivity of Earth’s climate to increasing CO2 can be attributed to the decline in the ice content in clouds as the climate warms.

More frequent high pressure conditions associated with atmospheric blocking episodes over Greenland in recent decades have been suggested to enhance melt through large-scale subsidence and cloud dissipation, which allows more solar radiation to reach the ice sheet surface. Here we investigate mechanisms linking high pressure circulation anomalies to Greenland cloud changes and resulting cloud radiative effects, with a focus on the previously neglected role of topography. Using reanalysis and satellite data in addition to a regional climate model, we show that anticyclonic circulation anomalies over Greenland during recent extreme blocking summers produce cloud changes dependent on orographic lift and descent. The resulting increased cloud cover over northern Greenland promotes surface longwave warming, while reduced cloud cover in southern and marginal Greenland favors surface shortwave warming. Comparison with an idealized model simulation with flattened topography reveals that orographic effects were necessary to produce area-averaged decreasing cloud cover since the mid-1990s and the extreme melt observed in the summer of 2012. This demonstrates a key role for Greenland topography in mediating the cloud and melt response to large-scale circulation variability. These results suggest that future melt will depend on the pattern of circulation anomalies as well as the shape of the Greenland Ice Sheet. Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-19-0527.s1. © 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Recent studies have revealed biases in sea surface temperature trends in CMIP6 between about 1970 and 2015, and other studies have suggested a possible lack of aerosol emissions over Asia in the same period. Motivated by these findings, we investigate the effect of doubling anthropogenic aerosol emissions over Asia from 1950 onward on sea surface temperatures in NorESM2-LM. While the perturbation of historical aerosol emissions does not yield a robust improvement in modeled sea surface temperature trends, we discover other changes which are worthy of further investigation: We find that the ocean heat transport decreases significantly in the Northern Hemisphere extratropics, which is counter-intuitive given the increasing temperature gradient between the tropics and the polar regions due to the enhanced aerosol emissions. When doubling SO2 emissions, we find increases in Southern Hemisphere sea surface temperatures in contrast to cooling in the Northern Hemisphere. Furthermore, we compare the fully-coupled simulations to atmosphere-only simulations where historical sea surface temperatures are prescribed and the same perturbation of aerosol emissions over Asia is imposed. The atmosphere-only simulations show much weaker changes in cloud cover compared to the fully-coupled simulations. Hence, sea surface temperature changes – possibly caused by changes in the oceanic circulation – must play an important role in setting the atmospheric response.

Cloud feedbacks are a major contributor to the spread of climate sensitivity in global climate models (GCMs) [1]. Among the most poorly understood cloud feedbacks is the one associated with the cloud phase, which is expected to be modified with climate change [2]. Cloud phase bias, in addition, has significant implications for the simulation of radiative properties and glacier and ice sheet mass balances in climate models. In this context, this work aims to expand our knowledge on how the representation of the cloud phase affects snow formation in GCMs. Better understanding this aspect is necessary to develop climate models further and improve future climate predictions. This study aims to improve the understanding of the link between the representation of cloud phase and surface snowfall in historical simulations, comparing them to a combination of satellite remote sensing and reanalysis data. We use the cloud and snowfall products from CloudSat satellite and the European Centre for Medium-Range Weather Forecast Re-Analysis 5 (ERA5), producing a global surface snowfall rate climatology for each dataset. We compare the outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models to the snowfall rate climatology produced by CloudSat and ERA5. Comparing historical simulations from climate models with CloudSat will relate the already identified cloud phase biases with snowfall biases in specific regions for the past decade. Statistical analysis is carried out to determine cloud phase and precipitation (liquid and solid) biases and their potential connection to each other in the climate models. The results show that, globally, CMIP6 models can reproduce some of the characteristics of liquid water content and snowfall, as seen in ERA5. In addition, a comparison between ERA5 and the CMIP6 models shows that CMIP6 models underestimate the ice water path. Then, we look at the regional differences of ice water path, liquid water path, and surface snowfall to better understand how the individual CMIP6 models perform in different regions—showing an overestimation of ice water path in the southern and northern hemisphere extratropics. At the same time, surface snowfall is within the ERA5 standard deviation. When comparing the ERA5 ice water path to surface snowfall, we identify a positive relationship between the two, independent of the latitudes. On the other hand, the CMIP6 ensemble mean or CMIP6 models individually do not indicate a positive relationship. The next step of this ongoing research is to investigate the relationship between cloud phase (ice, liquid water path) and surface snowfall rate based on CloudSat retrievals. [1] Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., et al. (2020). Causes of higher climate sensitivity in CMIP6 models. Geophysical Research Letters, 47, e2019GL085782. https://doi-org.ezproxy.uio.no/10.1029/2019GL085782 [2] Bjordal, J., Storelvmo, T., Alterskjær, K. et al. Equilibrium climate sensitivity above 5 °C plausible due to state-dependent cloud feedback. Nat. Geosci. 13, 718–721 (2020). https://doi-org.ezproxy.uio.no/10.1038/s41561-020-00649-1

Earth’s transient climate response (TCR) quantifies the global mean surface air temperature change due to a doubling of atmospheric CO2, at the time of doubling. TCR is highly correlated with near-term climate projections, and thus of utmost relevance for climate policy, but remains poorly constrained. Within state-of-the-art Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP6), the TCR range (1.1 -2.9oC is much too wide to offer useful guidance to policymakers on remaining carbon budgets aligneded with the Paris agreement goals. To address this issue, we here present an observation-based TCR estimate of 1.9-2.7oC (95% confidence interval). We show that this method correctly diagnoses TCR from 22 CMIP6 ESMs if the same variables are taken from the ESMs as are available from observations. This increases confidence in the new estimate and range, which are higher and narrower, respectively, than those of the CMIP6 ensemble.

Cloud feedbacks are a major contributor to the spread of climate sensitivity in global climate models (GCMs) [1]. Among the most poorly understood cloud feedbacks is the one associated with the cloud phase, which is expected to be modified with climate change [2]. Cloud phase bias, in addition, has significant implications for the simulation of radiative properties and glacier and ice sheet mass balances in climate models. In this context, this work aims to expand our knowledge on how the representation of the cloud phase affects snow formation in GCMs. Better understanding this aspect is necessary to develop climate models further and improve future climate predictions. This study will compare surface snowfall, ice, and liquid water content from the Coupled Model Intercomparison Project Phase 6 (CMIP 6) climate models (accessed through Pangeo) to the European Centre for Medium-Range Weather Forecast Re-Analysis 5 (ERA5) data from 1985 to 2014. We conduct statistical analysis at the annual and seasonal timescales to determine the biases in cloud phase and precipitation (liquid and solid) in the CMIP6 models and their potential connection between them. For the analysis, we use the Jupyter notebook on the CMIP6 analysis (https://github.com/franzihe/eosc-nordic-climate-demonstrator/blob/master/work/), which guides the user step by step. The use of the Pangeo.io intake package makes it possible to browse the CMIP6 online catalog for the required variables, models, and experiments and stores it in xarray dask datasets. Vertical variables in sigma pressure levels had to be interpolated to standard pressure levels as provided in ERA5. We also interpolated the horizontal and vertical variables to the exact horizontal grid resolution before calculating the climatology. A global comparison between the reanalysis (ERA5) and the CMIP6 models shows that models tend to underestimate the ice water path compared to the reanalysis even if most of them can reproduce some of the characteristics of liquid water content and snowfall. To better understand the link between biases in cloud phase and surface snowfall rate, we try to find a relationship between ice water path and surface snowfall in GCMs. Linear regressions within extratropical areas show a positive relationship between ice water content and surface snowfall in the reanalysis data, while CMIP6 models do not have these characteristics. [1] Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., et al. (2020). Causes of higher climate sensitivity in CMIP6 models. Geophysical Research Letters, 47, e2019GL085782. https://doi-org.ezproxy.uio.no/10.1029/2019GL085782 [2] Bjordal, J., Storelvmo, T., Alterskjær, K. et al. Equilibrium climate sensitivity above 5 °C plausible due to state-dependent cloud feedback. Nat. Geosci. 13, 718–721 (2020). https://doi-org.ezproxy.uio.no/10.1038/s41561-020-00649-1 Github: https://github.com/franzihe

Mineral dust aerosols play an important role in the Earth’s radiative budget. Dust aerosols interact with radiation in both the longwave and shortwave spectrum through direct radiative effects by absorption and scattering, and indirect effects through influencing cloud microphysical properties. Understanding dust-climate interactions are becoming increasingly important as effective air quality measures are reducing anthropogenic aerosol emissions and desertification in arid and semi-arid regions of the world are projected to increase in the face of climate change. In this work, we aim to better understand dust-climate interactions in the CMIP6 models by diagnosing the dust direct and indirect effects in 9 CMIP6 models participating in the piClim-2xdust experiment under AerChemMIP. The piClim-2xdust experiment doubles the dust emission in the model while keeping the other aerosols at pre-industrial levels. This means that any changes to the clouds and clear sky top of the atmosphere energy balance can be attributed to dust-cloud or dust-radiation interactions. We assess the robustness of the dust radiation and cloud response in the CMIP6 models and discuss the impact of differences in the representation of dust aerosols between the models.

The role of clouds for the surface radiation budget is particularly complex in the rapidly changing Arctic. However, despite their importance, long-term observations of Arctic clouds are relatively sparse. Here we present observations of cold clouds based on 7 years (2011–2017) of ground-based lidar observations at the Arctic Lidar Observatory for Middle Atmosphere Research (ALOMAR) in Andenes in the Norwegian Arctic. In two case studies, we assess (1) the agreement between a collocated cirrus cloud observation from the ground-based lidar and the spaceborne lidar onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite, and (2) the ground-based lidar’s capability of determining cloud phase in mixed-phase clouds from depolarization measurements. We then compute multi-year statistics of cold clouds from both platforms with respect to their occurence, cloud top and base height, cloud top temperature and thermodynamic phase for the period 2011–2017. We find that satellite and ground-based observations agree well for the coincident cirrus measurement, and that the vertical phase distribution within a liquid-topped mixed-phase cloud could be identified from depolarization measurements. On average, 8 % of all satellite profiles were identified as single-layer cold clouds with no apparent seasonal differences. The average cloud top and base heights combining the ground-based and satellite instrument are 9.1 km and 6.9 km, respectively, resulting in an average thickness of 2.2 km. Seasonal differences between the average top and base heights are on the order of 1–2 km and are largest when comparing autumn (highest) and spring (lowest). However, seasonal variations are small compared to the observed day-to-day variability. Cloud top temperatures agree well between both platforms with warmer cloud top temperatures in summer. The presented study demonstrates the capabilities of long-term cloud observations in the Norwegian Arctic from the ground-based lidar at Andenes.

Mixed-phase clouds play an important role in determining Arctic warming, but are parametrized in models and difficult to constrain with observations. We use two satellite-derived cloud phase metrics to investigate the vertical structure of Arctic clouds in two global climate models that use the Community Atmosphere Model version 6 (CAM6) atmospheric component. We report a model error limiting ice nucleation, produce a set of Arctic-constrained model runs by adjusting model microphysical variables to match the cloud phase metrics, and evaluate cloud feedbacks for all simulations. Models in this small ensemble uniformly overestimate total cloud fraction in the summer, but have variable representation of cloud fraction and phase in the winter and spring. By relating modeled cloud phase metrics and changes in low-level liquid cloud amount under warming to longwave cloud feedback, we show that mixed-phase processes mediate the Arctic climate by modifying how wintertime and springtime clouds respond to warming.

In-cloud icing is a major hazard for aviation traffic and forecasting of these events is an important task for weather agencies worldwide. A common tool utilized by aviation forecasters is an icing intensity index based on supercooled liquid water from numerical weather prediction models. We seek to validate the modified microphysics scheme, ICE-T, in the HARMONIE-AROME numerical weather prediction model with respect to aircraft icing. Icing intensities and supercooled liquid water derived from two 3-month winter season simulations with the original microphysics code, CTRL, and ICE-T are compared with pilot reports of icing and satellite retrieved values of liquid and ice water content from CloudSat–CALIPSO and liquid water path from AMSR-2. The results show increased supercooled liquid water and higher icing indices in ICE-T. Several different thresholds and sizes of neighborhood areas for icing forecasts were tested out, and ICE-T captures more of the reported icing events for all thresholds and nearly all neighborhood areas. With a higher frequency of forecasted icing, a higher false alarm ratio cannot be ruled out, but is not possible to quantify due to the lack of no-icing observations. The increased liquid water content in ICE-T shows a better match with the retrieved satellite observations, yet the values are still greatly underestimated at lower levels. Future studies should investigate this issue further, as liquid water content also has implications for downstream processes such as the cloud radiative effect, latent heat release, and precipitation.

Climate change is stated as one of the largest issues of our time, resulting in many unwanted effects on life on earth. Cloud fractional cover (CFC), the portion of the sky covered by clouds, might affect global warming and different other aspects of human society such as agriculture and solar energy production. It is therefore important to improve the projection of future CFC, which is usually projected using numerical climate methods. In this paper, we explore the potential of using machine learning as part of a statistical downscaling framework to project future CFC. We are not aware of any other research that has explored this. We evaluated the potential of two different methods, a convolutional long short-term memory model (ConvLSTM) and a multiple regression equation, to predict CFC from other environmental variables. The predictions were associated with much uncertainty indicating that there might not be much information in the environmental variables used in the study to predict CFC. Overall the regression equation performed the best, but the ConvLSTM was the better performing model along some coastal and mountain areas. All aspects of the research analyses are explained including data preparation, model development, ML training, performance evaluation and visualization.

Estimates for equilibrium climate sensitivity from current climate models continue to exhibit a large spread, from 2.1 to 4.7 K per carbon dioxide doubling. Recent studies have found that the treatment of precipitation efficiency in deep convective clouds—specifically the conversion rate from cloud condensate to rain Cp—may contribute to the large intermodel spread. It is common for convective parameterization in climate models to carry a constant Cp, although its values are model and resolution dependent. In this study, we investigate how introducing a potential iris feedback, the cloud–climate feedback introduced by parameterizing Cp to increase with surface temperature, affects future climate simulations within a slab ocean configuration of the Community Earth System Model. Progressively stronger dependencies of Cp on temperature unexpectedly increase the equilibrium climate sensitivity monotonically from 3.8 to up to 4.6 K. This positive iris feedback puzzle, in which a reduction in cirrus clouds increases surface temperature, is attributed to changes in the opacity of convectively detrained cirrus. Cirrus clouds reduced largely in ice content and marginally in horizontal coverage, and thus the positive shortwave cloud radiative feedback dominates. The sign of the iris feedback is robust across different cloud macrophysics schemes, which control horizontal cloud cover associated with detrained ice. These results suggest a potentially strong but highly uncertain connection among convective precipitation, detrained anvil cirrus, and the high cloud feedback in a climate forced by increased atmospheric carbon dioxide concentrations. Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-18-0845.s1. © 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

In this study, cloud‐resolving simulations of a case study for a limited area of the hibernal Arctic were performed with the atmospheric modeling system ICON‐ART (ICOsahedral Nonhydrostatic‐Aerosol and Reactive Trace gases). A thorough comparison with data both from satellite as well as aircraft measurement is presented to validate the simulations. In addition, the model is applied to clarify the microphysical processes occurring when introducing artificial aerosol particles into the upper troposphere with the aim of modifying cirrus clouds in the framework of climate engineering. Former modeling studies investigating the climate effect of this method were performed with simplifying assumptions and much coarser resolution, reaching partly contradicting conclusions concerning the method's effectiveness. The primary effect of seeding is found to be a reduction of ice crystal number concentrations in cirrus clouds, leading to increased outgoing longwave radiative fluxes at the top of the atmosphere, thereby creating a cooling effect. Furthermore, a secondary effect is found, as ice crystals formed from the injected seeding aerosol particles lead to enhanced riming of cloud droplets within the planetary boundary layer. This effectively reduces the coverage of mixed‐phase clouds, thus generating additional cooling by increased upward longwave radiative fluxes at the surface. The efficacy of seeding cirrus clouds proves to be relatively independent from the atmospheric background conditions, scales with the number concentrations of seeding particles, and is highest for large aerosol particles.

An observationally-constrained time series of historical aerosol effective radiative forcing (ERF) from 1750 to 2019 is developed in this paper. We find that the time history of aerosol ERFs diagnosed in CMIP6 models exhibits considerable variation and explore how the time history of aerosol forcing influences the probability distributions of present-day aerosol forcing and emergent metrics such as climate sensitivity. Using a simple energy balance model, trained on CMIP6 climate models and constrained by observed near-surface warming and ocean heat uptake, we derive estimates for the historical aerosol forcing. We find 2005-2014 mean aerosol ERF to be -1.1 (-1.8 to -0.5) W m-2 relative to 1750. Assuming recently published historical emissions from fossil fuel and industrial sectors and biomass burning emissions from SSP2-4.5, aerosol ERF in 2019 is -0.9 (-1.5 to -0.4) W m-2. There is a modest recovery in aerosol forcing (+0.025 W m-2 decade-1) between 1980 and 2014. This analysis also gives a 5-95% range of equilibrium climate sensitivity (ECS) of 1.8-5.1°C (best estimate 3.1°C) with a transient climate response (TCR) of 1.2-2.6°C (best estimate 1.8°C).

Aerosol forcing remains the most uncertain component of the total climate forcing on the Earth system. RFMIP and AerChemMIP contained experiments that allow us to determine time-slice present day (2014 minus 1850) from 17 CMIP6 models, and transient (1850 to 2014, or 2100) aerosol forcing from 11 models. In CMIP6, aerosol present-day aerosol forcing is -1.01 (full range -1.37 to -0.63) W m-2, a range considerably narrower than comprehensive assessments of aerosol forcing from multiple lines of evidence such as AR5 (-1.9 to -0.1 W m-2) or Bellouin et al. 2020 (-2.0 to -0.35 W m-2). The transient experiments also show a diversity in time histories, with most models showing a peak negative aerosol forcing at some time between 1975 and 2010, and recent trends varying from strongly recovering to slightly strengthening aerosol forcing. Models that were run to 2100 under SSP2-4.5 all show a projected weakening aerosol forcing.By fitting a simple relationship of how globally integrated emissions of black carbon, organic carbon and SO2 relate to effective radiative forcing from aerosol-radiation interactions (ERFari) and aerosol-cloud interactions (ERFaci), an emissions to forcing relationship can be determined for these 11 RFMIP and AerChemMIP models. Using a 100,000 member Monte Carlo ensemble of historical aerosol time series, where coefficients are drawn from these model-derived distributions, and total 1850 to 2014 aerosol forcing is taken from the wider distributions of Bellouin et al. (2020), we create a best estimate historical time series for aerosol forcing (with uncertainty) that is constrained to historical warming and observed ocean heat uptake using a simple climate model. This method can also be used to predict aerosol forcing from future emissions scenarios, such as the SSPs and those derived from integrated assessment models, and provides estimates of the likely ranges for equilibrium climate sensitivity and transient climate response based on the historical aerosol forcing.

One of the largest uncertainties in numerical weather prediction and climate models is the representation of mixed-phase clouds. With the aim of understanding how the supercooled liquid fraction (SLF) in clouds with temperature from -40$^\circ$C to 0$^\circ$C is related to temperature, geographical location, and cloud type, our analysis contains a comparison of four satellite-based datasets (one derived from active and three from passive satellite sensors), and focuses on SLF distribution near-globally, but also stratified by latitude and continental/maritime regions. Despite the warm bias in cloud top temperature of the passive sensor compared to the active sensor and the phase mismatch in collocated data, all datasets indicate, at the same height-level, an increase of SLF with cloud optical thickness, and generally larger SLF in the Southern Hemisphere than in the Northern Hemisphere (up to about 20\% difference), with the exception of continental low-level clouds, for which the opposite is true.

In the winter, orographic precipitation falls as snow in the mid to high latitudes where it causes avalanches, affects local infrastructure, or leads to flooding during the spring thaw. We present a technique to validate operational numerical weather prediction model simulations in complex terrain. The presented verification technique uses a combined retrieval approach to obtain surface snowfall accumulation and vertical profiles of snow water at the Haukeliseter test site, Norway. Both surface observations and vertical profiles of snow are used to validate model simulations from the Norwegian Meteorological Institute’s operational forecast system and two simulations with adjusted cloud microphysics.Retrieved surface snowfall is validated against measurements conducted with a double-fence automated reference gauge (DFAR). In comparison, the optimal estimation snowfall retrieval produces + 10.9% more surface snowfall than the DFAR. The predicted surface snowfall from the operational forecast model and two additional simulations with microphysical adjustments (CTRL and ICE-T) are overestimated at the surface with +41.0 %, +43.8 %, and +59.2 %, respectively. Simultaneously, the CTRL and ICE-T simulations underestimate the mean snow water path by -1071.4% and -523.7 %, respectively.The study shows that we would reach false conclusions only using surface accumulation or vertical snow water content profiles. These results highlight the need to combine ground-based in-situ and vertically-profiling remote sensing instruments to identify biases in numerical weather prediction.

International audience; The Antarctic Ice Sheet experiences perpetual katabatic winds, transporting snow, and moisture from the interior towards the periphery. However, the impacts of Antarctic moisture and drifting snow on cloud structure and surface energy fluxes have not been widely investigated. Here, we use a regional climate model with a newly developed drifting snow scheme to show that accounting for drifting snow notably alters the spatial distribution, vertical structure and radiative effect of clouds over Antarctica. Overall, we find that accounting for drifting snow leads to a greater cloud cover providing an increase of +2.74 Wm-2 in the surface radiative energy budget. Additionally, a comparison with 20 weather stations reveals a 2.17 Wm-2 improvement in representing the radiative energy fluxes. Our results highlight the need to study the impact of drifting snow processes on the future evolution of clouds, the surface energy budget and the vertical atmospheric structure over Antarctica.

Anthropogenic aerosol emissions have increased considerably over the last century, but climate effects and quantification of the emissions are highly uncertain as one goes back in time. This uncertainty is partly due to a lack of observations in the pre-satellite era, and previous studies show that Earth system models (ESMs) do not adequately represent surface energy fluxes over the historical era. We investigated global and regional aerosol effects over the time period 1961-2014 by looking at surface downwelling shortwave radiation (SDSR).We used observations from ground stations as well as multiple experiments from five ESMs participating in the Coupled Model Intercomparison Project Version 6 (CMIP6). Our results show that this subset of models reproduces the observed transient SDSR well in Europe, but poorly in China. The models do not reproduce the observed trend reversal in SDSR in China in the late 1980s, which is attributed to a change in the emission of SO2 in this region. The emissions of SO2 show no sign of a trend reversal that could explain the observed SDSR evolution over China, and neither do other aerosols relevant to SDSR. The results from various aerosol emission perturbation experiments from DAMIP, RFMIP and AerChemMIP suggest that its likely, that aerosol effects are responsible for the dimming signal, although not its full amplitude. Simulated cloud cover changes in the different models are not correlated with observed changes over China. Therefore we suggest that the discrepancy between modeled and observed SDSR evolution is partly caused by erroneous aerosol and aerosol precursor emission inventories. This is an important finding as it may help interpreting whether ESMs reproduce the historical climate evolution for the right or wrong reason.

While most observations indicate well-buffered clouds to aerosol perturbations, global models do not. Among the suggested mechanisms for this discrepancy is the models' lack of connections between cloud droplet size and two processes that can contribute to reduced cloudiness when droplets become more numerous and smaller: evaporation and entrainment. In this study, we explore different implementations of size-dependent evaporation and entrainment in the global atmospheric model CAM5.3-Oslo.We study their impact on the preindustrial-to-present day change in liquid water path (LWPPD-PI) and the corresponding aerosol indirect effect (AIEPD-PI). Impacts of the 2014–2015 fissure eruption in Holuhraun, Iceland, are also presented. Our entrainment modifications only have a moderate effect on AIEPD-PI (changes from −1.07Wm−2 to −0.98Wm−2), and a small impact on the signal from the Holuhraun eruption compared to other suggested compensating mechanisms. Simulations with added size-dependent evaporation in the top of the stratiform clouds also show small evaporation differences between PI and PD. Moderate changes in AIEPD-PI were achieved when also including an entrainment feedback to the evaporation changes, mixing air between the cloudtop layer and the layer above. These changes were not associated with the size dependency, but changes in the cloud susceptibility to aerosols in both PI and PD when adding evaporation.We find that increased evaporation of smaller droplets at stratiform cloud tops can reduce LWP, but can increase LWP in some areas due to enhanced shallow convection caused by destabilization.

The idea of modifying cirrus clouds to directly counteract greenhouse gas warming has gained momentum in recent years, despite disputes over its physical feasibility. Previous studies that analyzed modifications of cirrus clouds by seeding of ice nucleating particles showed large uncertainties in both cloud and surface climate responses, ranging from no effect or even a small warming to a globally averaged cooling of about 2.5 °C. We use two general circulation models that showed very different responses in previous studies, ECHAM6-HAM and CESM-CAM5, to determine which radiative and climatic responses to cirrus cloud seeding in a 1.5 × CO2 world are common and which are not. Seeding reduces the net cirrus radiative effect for −1.8 W m−2 in CESM compared with only −0.8 W m−2 in ECHAM. Accordingly, the surface temperature decrease is larger in CESM, counteracting about 70% of the global mean temperature increase due to CO2 and only 30% in ECHAM. While seeding impacts on mean precipitation were addressed in past studies, we are the first to analyze extreme precipitation responses to cirrus seeding. Seeding decreases the frequency of the most extreme precipitation globally. However, the extreme precipitation events occur more frequently in the Sahel and Central America, following the mean precipitation increase due to a northward shift of the Intertropical Convergence Zone. In addition, we use a quadratic climate damage metric to evaluate the amount of CO2-induced damage cirrus seeding can counteract. Seeding decreases the damage by about 50% in ECHAM, and by 85% in CESM over the 21 selected land regions. Climate damage due to CO2 increase is significantly reduced as a result of seeding in all of the considered land regions., Environmental Research Letters, 15 (5), ISSN:1748-9326, ISSN:1748-9318