About

John Cassano is an associate professor in the Department of Atmospheric and Oceanic Sciences and a fellow at the Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado Boulder. He leads a research group focused on studying the meteorology and climate of both polar regions. His group employs regional climate models and numerical weather prediction models, complemented by in-situ and remotely sensed observations and various data analysis techniques. The research emphasizes regional climate modeling and model development, analysis of coupled climate system components, and numerical weather prediction. The Polar Climate and Meteorology Group, which he leads, is located on the University of Colorado Boulder campus. Cassano's work contributes to understanding atmospheric processes and climate dynamics in polar environments, with a particular focus on Arctic and Antarctic meteorology and climate systems.

Research topics

• Environmental science
• Atmospheric sciences
• Oceanography
• Geology
• Climatology
• Meteorology
• Geography

Selected publications

• Bulletin of the American Meteorological Society Flying into the Cold: The NSF Cold Air outbreak Experiment in the Sub-Arctic (CAESAR)

  2026-01-20

  articleOpen access

  The NSF-sponsored Cold-Air outbreak Experiment over the Sub-Arctic Region (CAESAR) characterized the aerosol, thermodynamic and dynamic environment occupied by mixed-phase clouds within air masses moving southward off the Arctic sea ice over the Greenland/Norwegian Seas in the spring of 2024.The aircraft campaign supported an unprecedented suite of remote sensing and in situ measurements below, within and above the mostly shallow convective clouds.A typical flight began with a high-altitude survey leg that included dropsondes, followed by a spiral descent at the further point, then in situ sampling back to the coast.Three flights reached the Greenland Marginal Ice Zone.A golden-case cold-air outbreak was sampled from its origin in clear skies over 100% sea ice to its convergence with a land-skirting polar low.Another flight focused on a polar low, and a third flight characterized a closed-to-open cell transition occurring under high aerosol loading originating from Siberia.Novel instrumentation included a Raman lidar able to profile sub-plane temperature and water vapor, an upward-point radiometer resolving mesoscale super-cooled liquid variability, and detailed cloud particle imaging.Combined ice particle and ice nucleating particle concentrations suggest most ice production is primary at cloud temperatures < -20 0C, with secondary ice production evident at cloud temperatures > -20 0C.

  PublisherOA PDFDOI

• The observed evolution of Arctic amplification over the past 45 years

  ˜The œcryosphere · 2026-01-20

  articleOpen access

  Abstract. To address research gaps in understanding Arctic Amplification, we use data from ERA5, an observational surface temperature dataset, and sea ice concentration to examine the seasonal, spatial and decadal evolution of Arctic 2 m and lower tropospheric temperatures and lower tropospheric (surface to 850 hPa) static stability over the past 45 years. A Local Amplification Anomaly (LAA) metric is used to examine how spatial patterns of Arctic 2 m temperature anomalies compare to anomalies for the globe as a whole. Pointing to impacts of seasonally-delayed albedo feedback, growing areas of end-of-summer (September) open water largely co-locate with the strongest positive anomalies of 2 m temperatures through autumn and winter and their growth through time; small summer trends reflect the effects of a melting sea ice cover. Because of seasonal ice growth, the association between rising 2 m temperatures and sea ice weakens from autumn into winter, except in the Barents Sea where there have been prominent downward trends in winter ice extent. Imprints of variable atmospheric circulation are prominent in the Arctic temperature evolution. Low-level (surface to 850 hPa) stability over the Arctic increases from autumn through winter, consistent with the greater depth of surface-based atmospheric heating seen in autumn. However, trends towards weaker static stability dominate the Arctic Ocean in autumn and winter, especially over areas of September and wintertime ice loss. Sea ice thinning, leading to increased conductive heat fluxes though the ice, likely also contributes to reduced stability.

  PublisherDOI

• Ship‐Based Lidar Evaluation of Southern Ocean Low Clouds in the Storm‐Resolving General Circulation Model ICON and the ERA5 and MERRA‐2 Reanalyses

  Journal of Geophysical Research Atmospheres · 2025-11-21

  articleOpen access

  Abstract 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.

  PublisherOA PDFDOI

• CMIP6 Representation of Declining Sea Ice and Arctic Cyclones in the Current Climate

  Journal of Geophysical Research Atmospheres · 2025-07-14 · 2 citations

  article

  Abstract The Arctic climate system is changing rapidly with important implications in the Arctic and beyond. The interaction between the sea ice and Arctic cyclones makes it an important topic to be understood in the warming climate. We analyzed an ensemble of Coupled Multimodel Intercomparison Project (CMIP6) model simulations from 1985 to 2014 to determine model skill in depicting Arctic cyclones and their relationship with sea ice. A comprehensive climatology of Arctic cyclones and sea ice concentrations (SIC) was produced and compared to the ERA5 reanalysis product. The models reproduced the observed sea ice spatial patterns and trend well. However, the models struggled to capture the concurrent patterns and trends in Arctic cyclone characteristics that were evident in the reanalysis data. The models underestimated local cyclogenesis in the Arctic, which led to an overall underestimation of Arctic cyclone counts. Lead/lag analysis of ERA5 data suggests that reduced sea ice in the warm season can drive increased cyclone counts in the following cold season, which then reduces SIC in the next warm season in a feedback cycle that appears to be missing from the CMIP6 models. The results also revealed deviations between CMIP6 and ERA5 cyclone intensities. The magnitude and sign of the intensity differences varied based on model resolution, surface roughness parameterization, and skill in the representation of cyclogenesis location. This work highlights the need to improve sea ice‐atmosphere interactions and the representation of synoptic systems in the next generation of global models.

  PublisherDOI

• Quantifying the impacts of atmospheric rivers on the surface energy budget of the Arctic based on reanalysis

  ˜The œcryosphere · 2025-10-20 · 1 citations

  articleOpen access

  Abstract. We present a comprehensive analysis of Arctic surface energy budget (SEB) components during atmospheric river (AR) events identified by integrated water vapor transport exceeding the monthly 85th-percentile climatological threshold in 3-hourly ERA5 reanalysis data from January 1980 to December 2019. Analysis of average anomalies in SEB components, net SEB, and the overall AR contribution to both the seasonal SEB components and net SEB climatology reveals clear seasonality and distinct land–sea–sea ice contrast patterns. Over the sea-ice-covered central Arctic Ocean, ARs significantly impact net SEB, inducing substantial surface warming in fall, winter, and spring. This warming is primarily driven by large anomalies in surface downward longwave radiation (LWD), which average 29–45 W m−2 during the cold seasons. In contrast, AR-related LWD anomalies are smaller in summer, averaging around 15 W m−2, indicating a reduced impact during this season. Over sub-polar oceans, ARs have the most substantial positive impact on net SEB in cold seasons, mainly attributed to significant positive turbulent heat flux anomalies. AR-related turbulent heat anomalies reduce the upward turbulent flux, contributing up to −11 % relative to its seasonal climatology. In summer, ARs induce negative impacts on net SEB, primarily due to reduced shortwave radiation from increased cloud cover during AR events. Over continents, ARs generate smaller absolute impacts on net SEB because the large LWD anomalies are largely offset by corresponding increases in upward longwave radiation, particularly during cold seasons. Additionally, the seemingly large relative contributions of ARs to the net SEB over land primarily reflects the small magnitude of the climatological net SEB over continents. Greenland, especially western Greenland, exhibits significant downward longwave radiation anomalies associated with ARs, which drive large net SEB anomalies and contribute &gt;54 % to mean SEB and induce amplified surface warming year-round. This holds significance for melt events, particularly during summer. Additionally, results of AR-related SEB impacts strongly depend on detection methods, as restrictive AR detection algorithms that emphasize extreme AR events, with large AR-related anomalies, do not necessarily indicate a large overall contribution to the SEB climatology due to the low occurrence frequency of these events. This study quantifies the role of ARs in the surface energy budget, contributing to our understanding of the Arctic warming and sea ice decline in ongoing Arctic amplification.

  PublisherOA PDFDOI

• Performance of the PolarRES and Arctic CORDEX regional climate ensemble

  2025-03-15

  preprintOpen accessCorresponding

  Within the Horizon 2020 project PolarRES, a new ensemble of regional climate simulations has been developed using the latest generation of regional climate models (RCMs) for the Arctic. These state-of-the-art RCMs downscale the ERA5 reanalysis over the period 2001-2020, covering the entire Arctic region at a grid spacings of approximately 12km. Furthermore, all simulations follow the Polar CORDEX protocol for the next generation of regional climate projections of the polar regions. This new ensemble of high-resolution climate simulations offers considerable opportunities to advance our understanding of the present-day climate of the Arctic. However, a first step to realising this potential is to evaluate the performance of the regional climate models, highlighting their strengths and limitations. This is also necessary for understanding and interpreting the future projections that will be generated by these RCMs using a novel storylines approach to downscale CMIP6 models.The work presented here will focus on the simulations of the present-day climate driven by the ERA5 reanalysis. As part of the evaluation process, a clustering technique is applied to reanalysis data to identify regions with similar annual and seasonal characteristics of surface temperature and precipitation. This approach allows for a better understanding of the regional climates of the Arctic, provides a more physically consistent basis for model evaluation, and eases the investigation of model deficiencies in simulating regional scale forcings. This work will focus on the regionalisation of the Arctic for model evaluation and present preliminary results of the application of this regionalisation to the aforementioned Arctic climate simulations.

  PublisherDOI

• The Observed Evolution of Arctic Amplification over the Past 45 Years

  2025-08-13

  articleOpen access

  Abstract. To address research gaps in understanding Arctic Amplification, we use data from ERA5 and sea ice concentration to examine the seasonal, spatial and decadal evolutuion of Arctic 2-meter and lower tropospheric temperatures and lower tropospheric (surface to 850 hPa) static stability over the past 45 years. A Local Amplification Anomaly (LAA) metric is used to examine how spatial patterns of Arctic 2-meter temperature anomalies compare to anomalies for the globe as a whole. Pointing to impacts of seasonally-delayed albedo feedback, growing areas of end-of-summer (September) open water largely co-locate with the strongest positive anomlies of 2-meter temperatures through autumn and winter and their growth through time; small summer trends reflect the effects of a melting sea ice cover. Because of seasonal ice growth, the association between rising 2-meter temperatures and sea ice weakens from autumn into winter, except in the the Barents Sea where there have been prominent downward trends in winter ice extent. Imprints of variable atmospheric circulation are prominent in the Arctic temperature evolution. Low-level (surface to 850 hPa) stability over the Arctic increases from autumn through winter, consistent with the greater depth of surface-based atmospheric heating seen in autumn. However, trends towards weaker static stability dominate the Arctic Ocean in autumn and winter, especially over areas of September and wintertime ice loss. Sea ice thinning, leading to increased conductive heat fluxes though the ice, likely also contributes to reduced stability.

  PublisherDOI

• Evaluating Atmospheric River Impacts on Energy and Moisture Transport in the Arctic Using Different Detection Algorithms

  2025-01-18

  preprint

  Atmospheric rivers (ARs) significantly impact the Arctic climate system by enhancing atmospheric heat and moisture transport and altering the local energy budget. Developing AR detection tools (ARDTs) is critical yet challenging. This study evaluates 11 ARDTs in the Arctic to assess their performance in representing atmospheric heat (represented by moist static energy) and moisture transport, as well as surface downward longwave radiation (LWD) impacts, spanning 2000 to 2019 using ERA5 reanalysis. We find that AR occurrence frequency in the Arctic varies widely, from less than 1% to over 13%, depending on the ARDT. This variability leads to differences in contributions to poleward atmospheric heat (&lt; 1% to 33%) and moisture (&lt;1% to 49%) transport. The highest AR frequency, and corresponding contributions to atmospheric heat and moisture transport, occurs over the Atlantic sector during non-summer seasons for most ARDTs. This region aligns with the primary poleward moisture pathway and the end of climatological mid-latitude storm tracks, highlighting strong connections between Arctic ARs and mid-latitude cyclones. ARs induce significant LWD anomalies, with the largest in winter and smallest in summer. Global ARDTs, which detect fewer ARs in the Arctic, show greater anomalies (&gt;100 W m-2 in higher Arctic), but their integrated contribution to seasonal climatological LWD is much smaller (&lt;1%). In contrast, polar-specific ARDTs detect higher AR occurrences and account for 10-15% of seasonal LWD. This suggests that algorithms emphasizing extreme events with large LWD anomalies do not necessarily indicate a large overall radiative climate impact.

  PublisherDOI

• Meteorology and Climate of Antarctica

  Cambridge University Press eBooks · 2025-12-10

  book-chapter
  PublisherDOI

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

  2025-01-10 · 3 citations

  preprintOpen access

  Global storm-resolving models (GSRMs) are the upcoming 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 still 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 reanalyses using about 2400 days of lidar observations and 2300 radiosonde profiles from 31 voyages and Macquarie Island station during 2010-2021, compared with the models using a ground-based lidar simulator. We found that 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 fog or near-surface clouds, and MERRA-2 underestimates cloud occurrence at almost all heights. Outgoing shortwave radiation is overestimated in the reanalyses, implying a ”too few, too bright” cloud problem. Thermodynamic conditions are relatively well represented, but ICON is less stable than observations and MERRA-2 is too humid. SO cloud biases are a substantial issue in the GSRM, but it matches the observations better than the lower-resolution reanalyses.

  PublisherDOI

Recent grants

• Collaborative Research: Analysis of McCall Glacier ice core and related modern process studies

  NSF · $285k · 2010–2015

• Collaborative Research: Antarctic Automatic Weather Station Program 2016-2019

  NSF · $100k · 2016–2020

• Collaborative Research: Observing the Atmospheric Boundary over the West Antarctic Ice Sheet

  NSF · $478k · 2018–2027

• Collaborative Research: Ocean-Ice-Atmosphere Interactions in the Terra Nova Bay Polynya, Antarctica

  NSF · $1.1M · 2011–2015

• Collaborative Research: Antarctic Automatic Weather Station Program

  NSF · $286k · 2010–2014

Frequent coauthors

• Gijs de Boer

  University of Colorado Boulder

  136 shared

• Mark W. Seefeldt

  University of Colorado Boulder

  119 shared

• Gina Jozef

  University of Colorado Boulder

  86 shared

• David H. Bromwich

  The Ohio State University

  74 shared

• Robert Osiński

  Polish Academy of Sciences

  62 shared

• Wieslaw Maslowski

  Naval Postgraduate School

  61 shared

• Elizabeth N. Cassano

  61 shared

• Jordan G. Powers

  NSF National Center for Atmospheric Research

  57 shared

Labs

• John Cassano groupPI

  We study the meteorology and climate of both polar regions using regional climate models and numerical weather prediction models