About

Melissa Gervais is an Associate Professor of Meteorology and Atmospheric Science and serves as the Associate Head of Diversity, Equity, and Inclusion at Penn State. She is also a faculty fellow of the Institute for Computational and Data Sciences (ICDS) and holds an adjunct position as an Associate Research Scientist at the Lamont Doherty Earth Institute (LDEO). Her research focuses on atmospheric science, with particular interests in climate and weather phenomena, and she contributes to the department's efforts in diversity, equity, and inclusion.

Research topics

• Computer Science
• Artificial Intelligence
• Geology
• Oceanography
• Geography
• Geodesy
• Seismology
• Environmental science
• Climatology
• Telecommunications
• Meteorology
• Remote sensing
• Atmospheric sciences

Selected publications

• Increased Model Resolution Amplifies Boreal Winter Arctic Precipitation and Atmospheric Circulation Response to Sea Ice Loss

  Journal of Climate · 2026-02-27

  articleSenior author

  Abstract The impact of future Arctic sea ice loss on local climate and large-scale atmospheric circulation has been extensively studied, including through the Polar Amplification Model Intercomparison Project (PAMIP). However, the influence of horizontal resolution on these responses remains largely unexplored. This study addresses this gap by conducting a set of PAMIP-type experiments in parallel using the Community Earth System Model, version 2.2 (CESM2.2), at global 110-km and Arctic-refined 14-km resolutions, with outputs regridded to a common grid to enable direct comparison. Sea ice loss is identified as the dominant driver of future Arctic precipitation increases in boreal winter. The Arctic-refined model exhibits a larger increase in precipitation over the sea ice loss region compared to the global 110-km model. This amplified response is linked to stronger updrafts and corresponding intensification of upward moisture transport. Additionally, daily precipitation variability increases in response to sea ice loss, with the change in the Arctic-refined model more than twice that in the global 110-km model, primarily connected to enhanced variability in vertical motion. Furthermore, both model resolutions capture Arctic amplification and associated dynamical responses, but the Arctic-refined model shows stronger warming and greater zonal wind deceleration over the polar cap. The thermodynamic budget analysis indicates that transient eddies associated with vertical motion are a major factor in the enhanced warming in the higher-resolution configuration. Collectively, these findings highlight the role of horizontal resolution in shaping Arctic precipitation and atmospheric circulation responses and underscore vertical motion as a key driver of this sensitivity. Significance Statement This modeling study examines how increasing model horizontal resolution influences the atmospheric response to future Arctic sea ice loss. Using the Community Earth System Model, version 2.2 (CESM2.2), we conducted two sea ice loss experiments, one with a typical climate model resolution and one with very high resolution over the Arctic, following an experiment protocol similar to the Polar Amplification Model Intercomparison Project (PAMIP). The results show that higher resolution leads to greater increases in Arctic precipitation and its variability in response to sea ice loss. Additionally, the simulations with high resolution over the Arctic exhibit stronger lower-tropospheric temperature and circulation responses over the polar cap compared to the coarser-resolution simulations. These enhanced responses are likely linked to resolution-dependent differences in vertical motion. Our findings advance the understanding of high-resolution modeling and highlight the critical role of horizontal resolution in accurately simulating climate and climate change in the Arctic.

  PublisherDOI

• Fingerprints of AMOC Decline Are Sensitive to External and Mechanistic Forcing

  Geophysical Research Letters · 2025-06-19 · 2 citations

  articleOpen access

  Abstract The Atlantic meridional overturning circulation (AMOC) plays a crucial role in past, present, and future climate, and there is substantial interest in using sea surface temperature (SST) as a fingerprint of past AMOC strength. Using a hierarchy of climate model ensembles, we find that the decline in AMOC, and its SST fingerprint within the North Atlantic warming hole region, are sensitive to external forcing level and wind driven ocean forcing. Once external forcing reaches a level at which sea ice melt increases the Labrador Sea vertical salinity gradient, localized cooling and resulting expansion of the sea ice edge decrease vertical mechanical stirring. Under greenhouse gas only forcing, this mechanism plays a large role and under SSP3.70 forcing, it plays a relatively minor role due to larger buoyancy forcing. This implies that an AMOC fingerprint developed from one simulation or external forcing level cannot be applied to other scenarios.

  PublisherOA PDFDOI

• Increased Model Resolution Amplifies Arctic Precipitation and Atmospheric Circulation Response to Sea-Ice Loss

  2025-07-03 · 1 citations

  preprintOpen accessSenior author

  The impact of future Arctic sea-ice loss on local climate and large-scale atmospheric circulation has been extensively studied, including through the Polar Amplification Model Intercomparison Project (PAMIP).However, the influence of horizontal resolution on these responses remains largely unexplored.This study addresses this gap by conducting a set of PAMIP-type experiments in parallel using the Community Earth System Model Version 2.2 (CESM2.2) at global 110-km and Arctic-refined 14-km resolutions, with outputs regridded to a common grid to enable direct comparison.Sea ice loss is identified as the dominant driver of future Arctic precipitation increases in boreal winter.The Arcticrefined model exhibits a larger increase in precipitation over the sea ice loss region compared to the global 110-km model.This amplified response is linked to stronger updrafts and corresponding intensification of upward moisture transport.Additionally, daily precipitation variability increases in response to sea ice loss, with the change in the Arctic-refined model more than twice that in the global 110-km model, primarily connected to enhanced variability in vertical motion.Furthermore, both model resolutions capture Arctic amplification and associated dynamical responses, but the Arctic-refined model shows stronger warming and greater zonal wind deceleration over the polar cap.Thermodynamic budget analysis indicates that transient eddies associated with vertical motion are a major factor in the enhanced warming in the higher-resolution configuration.Collectively, these findings highlight the role of horizontal resolution in shaping Arctic precipitation and atmospheric circulation responses and underscore vertical motion as a key driver of this sensitivity.

  PublisherOA PDFDOI

• Identifying, Tracking, and Evaluating Mechanisms of North American Cold Air Outbreaks (CAOs) Using a Feature Tracking Approach

  Monthly Weather Review · 2025-01-01 · 2 citations

  article

  Abstract North American cold air outbreaks (CAOs) are large-scale temperature extremes that typically originate in the high latitudes and impact the midlatitudes in winter. As they transit southward, they can have significant socioeconomic consequences. CAOs from winter (DJF) 1979 to 2020 were identified in the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) using an automated feature tracking approach (TempestExtremesV2.1). This allowed for the systematic identification of a large number of cases without using predetermined, Eulerian regions. Another important advantage of this approach was the ability to compute a feature tracked thermodynamic energy budget in a nonfixed domain for every identified CAO event. As an example, the thermodynamic energy budget analysis was used to quantify important processes for the 18–23 January 1985 CAO. The dominant mechanisms of cooling and warming as well as lysis locations (i.e., eastern or western) were then used to generalize detected CAO events into subcategories. The associated statistics, spatial footprints, and composites of 500-hPa height, sea level pressure, and temperature and winds at 850 hPa were analyzed for three subcategories that contained the majority of events. This analysis revealed that CAO events that form and dissipate through different mechanisms occur in different regions, have different intensities, and are associated with different large-scale circulation patterns. Finally, the analysis of associated North Atlantic Oscillation (NAO) and Pacific–North American (PNA) teleconnection pattern revealed that the PNA is typically in a positive phase for eastern CAO events and in a negative phase for western events resulting primarily from horizontal advection, whereas the NAO did not have any significant relationship.

  PublisherDOI

• Analyzing Self-Organizing Maps of Modeled U.S. Coastal Wind Regimes with a Comparison to Observations

  Artificial Intelligence for the Earth Systems · 2025-03-28

  articleOpen access

  Abstract Wind offshore of the northeastern United States is a vast and plentiful resource. However, wind variability needs accounting for when planning, installing, and operating offshore wind farms. Therefore, increased knowledge of four general areas becomes vital: 1) common coastal wind regimes and their impact on wind energy production, 2) common regime transitions, 3) how near-surface wind shear varies between wind-shear regimes, and 4) whether numerical forecast model skill is regime dependent. A self-organizing map (SOM) clusters hub-height (80 m) wind data from the High-Resolution Rapid Refresh (HRRR) model covering the northeast coast to address areas 1–3. The SOM identifies three general wind pattern types: unidirectional flow, confluent/diffluent flow, and cyclonic/anticyclonic flow. The strongest mean HRRR wind speeds offshore of New York are associated with a low pressure system near Maine (12 m s −1 ) and wintertime–springtime westerlies (11 m s −1 ), while the weakest winds are associated with a nearby high (≤3 m s −1 ) and a diffluence zone (4 m s −1 ). Using a separate SOM trained on 10–80-m wind differences, warm air advection over cooler northern waters typically leads to lower-level stabilization and thus increased shear. Regarding area 4, modeled winds are compared to buoy lidar observations for each SOM regime. As in observations, the HRRR monthly averaged wind speed decreased in the summer. HRRR generally underforecasts wind speed near the buoys, although the monthly averaged bias decreased over 2 years from 1.4 to 0.1–0.2 m s −1 . Greater bias occurred for regimes representing nearby pressure systems, indicating that HRRR skill can be regime dependent. Significance Statement Optimal wind energy utilization requires accurate wind forecasts, which in turn require an understanding of regional wind regimes. Our study used a machine learning method called a self-organizing map to identify the main types of weather regimes that affect offshore wind power for the northeastern U.S. coast: unidirectional flow, confluent/diffluent flow, and nearby pressure systems. In general, a unidirectional wind or a low pressure system north of the domain is a high-wind-speed regime beneficial for offshore wind energy, whereas confluence/diffluence zones or pressure systems within the domain are generally low-wind-speed regimes that are less beneficial for offshore wind energy. Future studies can apply this analysis for regime-based forecasting methods.

  PublisherOA PDFDOI

• Wind-Driven Ocean Circulation Changes Can Amplify Future Cooling of the North Atlantic Warming Hole

  Journal of Climate · 2025-04-10 · 4 citations

  articleOpen accessSenior author

  Abstract The North Atlantic warming hole is an area of relative cooling in the North Atlantic subpolar gyre. Observations and models have suggested numerous causes of the warming hole, including a role for wind-driven ocean circulation changes. We investigate the role of wind-driven ocean circulation changes on the development and projected future of the North Atlantic warming hole by comparing two ensembles within the Community Earth System Model, version 2 (CESM2). One ensemble includes wind-driven ocean circulation changes, while the other does not. The difference between the ensemble means isolates the role of wind-driven ocean circulation changes on the externally forced North Atlantic warming hole. We find that wind-driven ocean circulation changes do not alter the timing of the formation of an externally forced warming hole. However, anthropogenic changes to the near-surface winds lead to enhanced upwelling near Greenland, and wind stress changes enable a positive feedback loop that relies on changes to mechanical stirring. These mechanisms amplify the cooling in the high latitude North Atlantic and lead to increased sea level pressure and reduced precipitation near the southern tip of Greenland. Thus, changes to wind-driven ocean circulation are a crucial component of future changes in North Atlantic climate. Improved understanding of ocean–atmosphere coupling in this region will improve projections of sea surface temperatures and associated atmospheric impacts. Significance Statement The purpose of this study is to quantify the role that changes to the wind-driven component of ocean circulation have on future sea surface temperatures in the North Atlantic subpolar gyre region. This region has warmed less than the global average, often referred to as a “warming hole.” We use a targeted climate model experiment to demonstrate that wind-driven ocean circulation changes do not cause the modeled North Atlantic warming hole. However, wind-driven ocean circulation changes alter the warming hole beginning in 2040. This demonstrates that monitoring and understanding changes to the surface winds and ocean currents in the North Atlantic is important for understanding future climate changes in the region.

  PublisherOA PDFDOI

• Wide range of possible trajectories of North Atlantic climate in a warming world

  Nature Communications · 2024-05-17 · 20 citations

  articleOpen access

  Decadal variability in the North Atlantic Ocean impacts regional and global climate, yet changes in internal decadal variability under anthropogenic radiative forcing remain largely unexplored. Here we use the Community Earth System Model 2 Large Ensemble under historical and the Shared Socioeconomic Pathway 3-7.0 future radiative forcing scenarios and show that the ensemble spread in northern North Atlantic sea surface temperature (SST) more than doubles during the mid-twenty-first century, highlighting an exceptionally wide range of possible climate states. Furthermore, there are strikingly distinct trajectories in these SSTs, arising from differences in the North Atlantic deep convection among ensemble members starting by 2030. We propose that these are stochastically triggered and subsequently amplified by positive feedbacks involving coupled ocean-atmosphere-sea ice interactions. Freshwater forcing associated with global warming seems necessary for activating these feedbacks, accentuating the impact of external forcing on internal variability. Further investigation on seven additional large ensembles affirms the robustness of our findings. By monitoring these mechanisms in real time and extending dynamical model predictions after positive feedbacks activate, we may achieve skillful long-lead North Atlantic decadal predictions that are effective for multiple decades.

  PublisherOA PDFDOI

• Impacts of projected Arctic sea ice loss on daily weather patterns over North America

  2024-03-09

  preprintOpen access1st authorCorresponding

  Future Arctic sea ice loss has a known impact on Arctic Amplification (AA) and mean atmospheric circulation. Furthermore, several studies have shown it leads to a decreased variance in temperature over North America. In this study, we analyze results from two fully-coupled Community Earth System Model (CESM) Whole Atmosphere Community Climate Model (WACCM4) simulations with sea ice nudged to either the ensemble mean of WACCM historical runs averaged over the 1980-1999 period for the control (CTL) or projected RCP8.5 values over the 2080-2099 period for the experiment (EXP). Dominant large-scale meteorological patterns (LSMPs) are then identified using self-organizing maps applied to winter daily 500 hPa geopotential height anomalies (𝑍′500) over North America. We investigate how sea ice loss (EXP-CTL) impacts the frequency of these LSMPs and, through composite analysis, the sensible weather associated with them. We find differences in LSMP frequency but no change in residency time indicating there is no stagnation of the flow with sea ice loss. Sea ice loss also acts to de-amplify and/or shift the 𝑍′500 that characterize these LSMPs and their associated anomalies in potential temperature  at 850hPa. Impacts on precipitation anomalies are more localized and consistent with changes in anomalous sea level pressure. With this LSMP framework we provide new mechanistic insights,  demonstrating a role for thermodynamic, dynamic and diabatic processes in sea ice impacts on atmospheric variability. Understanding these processes from a synoptic perspective is critical as some LSMPs play an outsized role in producing the mean response to Arctic sea ice loss.

  PublisherDOI

• Diagnosing flavors of tropospheric Rossby wave breaking and their associated dynamical and sensible weather features 

  2024-03-09

  preprintOpen accessSenior authorCorresponding

  Rossby wave breaking (RWB) can be manifested by the irreversible overturning of isentropes on constant potential vorticity (PV) surfaces. RWB events can lead to tropospheric impacts ranging from changes in intensity and position of the jet stream to extremes in precipitation resulting in significant societal impacts. Traditionally, RWB events are categorized as anticyclonic (AWB) or cyclonic (CWB) and can be identified using the orientation of streamers of high potential temperature (θ) and low θ air on a potential vorticity surface. Self-organizing maps (SOM), a machine learning method, was used to cluster RWB events into archetypal patterns, or “flavors”, for each RWB event type (i.e., AWB and CWB). This allowed for an examination of differences in RWB event flavors, and their associated tropospheric impacts, using the European Centre for Medium Range Weather Forecasts Reanalysis v5 (ERA5) dataset. AWB and CWB flavors capture variations in the θ minima/maxima of each streamer and the localized meridional θ gradient (∇θ) flanking the streamers. Variations in the magnitude and position of ∇θ between flavors correspond to a diversity of jet structures leading to differences in vertical motion patterns and troposphere-deep circulations. A subset of flavors of AWB (CWB) events are associated with the development of strong surface high (low) pressure systems and the generation of extreme poleward moisture transport. For CWB, many events occurred in similar geographical regions, but the precipitation and moisture patterns were vastly different between flavors. Given these impacts and their importance for regional climates, it is important to also understand how RWB events, and their associated sensible weather features, are represented in climate models. Therefore, AWB and CWB events were identified from overturning isentropes on the dynamic tropopause (DT) in the Community Earth System Large Ensemble v2 (CESM-LENS2) climate model output during December, January, and February (DJF) 1980-2014 (i.e., historical period). RWB flavors are identified in the LENS2 for comparison to the ERA5 dataset for the same time period. Composites of tropospheric dynamic and thermodynamic fields were calculated for each RWB flavor in the LENS2 which allows for an evaluation of the impact of AWB and CWB structure on sensible weather extremes. First, the frequency of occurrence of each RWB flavor between datasets was found. Second, differences in the sensible weather features associated with each flavor were quantified. This process-orientated climate model evaluation of the LENS2 as compared to the ERA5 can provide insight into the source of model errors in the LENS2 climate model.

  PublisherDOI

• Diagnosing Flavors of Tropospheric Rossby Wave Breaking and Their Associated Dynamical and Sensible Weather Features

  Monthly Weather Review · 2023-12-19 · 4 citations

  articleSenior author

  Abstract Rossby wave breaking (RWB) can be manifested by the irreversible overturning of isentropes on constant potential vorticity (PV) surfaces. Traditionally, the type of breaking is categorized as anticyclonic (AWB) or cyclonic (CWB) and can be identified using the orientation of streamers of high potential temperature ( θ ) and low θ air on a PV surface. However, an examination of the differences in RWB structure and their associated tropospheric impacts within these types remains unexplored. In this study, AWB and CWB are identified from overturning isentropes on the dynamic tropopause (DT), defined as the 2 potential vorticity unit (PVU; 1 PVU = 10 −6 K kg −1 m 2 s −1 ) surface, in the ERA5 dataset during December, January, and February 1979–2019. Self-organizing maps (SOM), a machine learning method, is used to cluster the identified RWB events into archetypal patterns, or “flavors,” for each type. AWB and CWB flavors capture variations in the θ minima/maxima of each streamer and the localized meridional θ gradient (∇ θ ) flanking the streamers. Variations in the magnitude and position of ∇ θ between flavors correspond to a diversity of jet structures leading to differences in vertical motion patterns and troposphere-deep circulations. A subset of flavors of AWB (CWB) events are associated with the development of strong surface high (low) pressure systems and the generation of extreme poleward moisture transport. For CWB, many events occurred in similar geographical regions, but the precipitation and moisture patterns were vastly different between flavors. Our findings suggest that the location, type, and severity of the tropospheric impacts from RWB are strongly dictated by RWB flavor. Significance Statement Large-scale atmospheric waves ∼15 km above Earth’s surface are responsible for the daily weather patterns that we experience. These waves can undergo wave breaking, a process that is analogous to ocean waves breaking along the seashore. Wave breaking events have been linked to extreme weather impacts at the surface including cold and heat waves, strong low pressure systems, and extreme precipitation events. Machine learning is used to identify and analyze different flavors, or patterns, of wave breaking events that result in differing surface weather impacts. Some flavors are able to generate notable channels of moisture that result in extreme high precipitation events. This is a crucial insight as forecasting of extreme weather events could be improved from this work.

  PublisherDOI

Frequent coauthors

• Yochanan Kushnir

  Columbia University

  43 shared

• Jeffrey Shaman

  37 shared

• Qinxue Gu

  Princeton University

  14 shared

• Eyad H. Atallah

  University of Arizona

  5 shared

• John R. Gyakum

  McGill University

  5 shared

• Bruno Tremblay

  McGill University

  5 shared

• Guido Cervone

  Pennsylvania State University

  4 shared

• C. Wauthier

  Pennsylvania State University

  4 shared

Awards & honors

• Faculty fellow of the Institute for Computational and Data S…