Maile's Abstract:
Diagnosing CMIP6 Sea Surface Temperature Variability in the Indo-Pacific Warm Pool
In recent decades, sea surface temperatures (SSTs) in the eastern tropical Pacific have been observed to be cooling, while the western Indo-Pacific warm pool has been experiencing intensified warming. However, models in the Coupled Model Intercomparison Project phase 6 (CMIP6) have been unable to recreate this warming pattern. They indicate an even warming pattern over most of the earth, with intensified warming in the eastern tropical Pacific where we see cooling today. Understanding the cause for this discrepancy is made difficult by the fact that we currently do not have a good understanding of the mechanisms that control decadal scale variability in the tropical Pacific.
Here we focus on understanding how climate models handle internal climate variability. Climate models in CMIP6 seem to agree that interdecadal surface air temperature variability is very muted in tropical areas of ascent, and therefore high precipitation. As a result, climate change is disproportionately increasing the temperature variability in the warm pool and along the Intertropical Convergence Zone. Across CMIP6 models, the strength of ascent is positively correlated with the signal to noise ratio of forced response to unforced variability.
We use a simple Hasselmann model to partition SST variability into contributions from forcing and feedback terms for both oceanic and atmospheric heat fluxes. We find that the suppressed variability in tropical regions of ascent is related to strong atmospheric damping of SST anomalies.
McKenzie's Abstract:
A Comparison of Microphysical Properties in Lake-Effect and Synoptic Snowfall near Buffalo, NY
To improve forecasting of snow events, it is imperative to understand how the snowfall event type, mesoscale versus synoptic, dictates what an observer sees at the ground level. One location that endures extreme snowfall is Buffalo, NY, which is located along the long axis of Lake Erie, and can experience large lake-effect snow and synoptic snow accumulations. The University of Illinois System for Characterizing and Measuring Precipitation (SCAMP) is a suite of deployable ground-based instruments consisting of a profiling Micro Rain Radar2, a Parsivel2 laser disdrometer, a precipitation weighing gauge, and a weather sensor. This instrument suite measures various characteristics of precipitation such as precipitation amounts, radar reflectivities and Doppler velocities, and hydrometeor particle size distributions, supplemented by environmental conditions. SCAMP was deployed east of Buffalo, NY, for two full winter seasons, allowing for sampling of both synoptic and mesoscale snow events. Using three events from the SCAMP dataset (two lake-effect events on 18-20 November 2022, and 23-25 December 2022, and a synoptic event observed on 17 January 2022), the observed snowfall parameters will be examined to help gain an understanding as to how the event type might impact surface snowfall characteristics.
