Cloud-Ocean Feedbacks and Their Impact on Ocean Temperature Variability

State-of-the art cllimate models have large biases in their representation of tropical clouds. At Lawrence Livermore National Laboratory, I worked with Dr. Mark Zelinka to examine how cloud bias may impact equatorial Pacific ocean temperatures.

In recent decades, observed sea surface temperature (SST) trends have cooled in the eastern tropical Pacific and warmed in the western tropical Pacific. Historical simulations using state-of-the-art climate models fail to reproduce this pattern of warming. We find that the feedback between low-lying clouds and SSTs, to first-order, explains model-to-model differences in the magnitude of naturally occurring SST variability in the southeast Pacific. On average, climate models have too weak a cloud-SST feedback, and as a result underpredict the likelihood of multi-decadal cooling in the eastern Pacific in preindustrial experiments. Cloud-SST feedback enhances local radiation anomalies and amplifies the wind-evaporation-SST feedback.

In the upper left corner we show that surface ocean temperatures have cooled in the eastern Pacific (black triangular sector) over the last 40 years, a trend that most climate models fail to reproduce. The right column shows that state-of-the-art global climate models (CMIP6 & CMIP5) disagree with observations (top right panel) on the magnitude of coupling between low-lying clouds and ocean temperatures off the coast of South America - measured as the toa-of-atmosphere shortwave cloud feedback. We show that bias in the cloud-ocean feedback off the coast of South America may be related to the magnitude of multidecadal variability on each CMIP5 and CMIP6 model (bottom right panel).

Interannual Variability of Sea Ice Area

Sea ice is an important component of the climate system and one that is rapidly changing due to global warming. While it is well known that mean Arctic sea ice area is declining due, less attention has been focused on understanding how the interannual variaiblity of sea ice area is changing in a warming planet. Changes to sea ice area variability have important implications for forecasting, extreme events, and attribution science. Here, we study how interannual varability of sea ice area in the Arctic and Antarctic is changing due to climate change. We explore seasonal, hemispheric, and model differences, and compare observations with models.

Machine Learning Gravity Wave Parameterization Generalizes to Capture the QBO and Response to Increased CO2

Can machine learning be used to accurately and stably emulate a gravity wave parameterization in a global climate model?

Espinosa, Z. I., Sheshadri, A., Cain, G. R., Gerber, E. P., & DallaSanta, K. J. (2022). Machine learning gravity wave parameterization generalizes to capture the QBO and response to increased CO2. Geophysical Research Letters, 49, e2022GL098174.

Atmospheric gravity waves (GWs) or “buoyancy waves” are generated by perturbations in a stably-stratified environment. They mediate momentum transport between the lower and middle atmospheres and play a leading-order role in driving middle atmospheric circulation. Due to computational constraints, global climate models “parameterize” or estimate the effect of GWs on the largescale flow. Current climate predictions are sensitive to uncertainties in these representations. Here, we examine whether machine learning, given limited data, can be used for gravity wave parameterization (GWP) in climate prediction. This approach represents an appealing technique to build data-driven GWPs that can reduce existing uncertainties by incorporating observations.

Pressure-time profiles of the zonal mean zonal wind, averaged between 5°S and 5°N and smoothed with a 15-day low pass filter, show the behavior of the Quasi-Biennial Oscillation (QBO) in integrations of (a) the control version of Model of an Idealized Moist Atmosphere with the AD99 parameterization, (b) the model coupled with WaveNet, (c) a 4xCO2 integration with the AD99 parameterization, and (d) a 4xCO2 integration coupled with WaveNet. Vertical dashed lines separate 5 years segments. The westerly (red) and easterly (blue) bands correspond to winds associated with opposite phases of the QBO. The QBO period and amplitudes are calculated using the transition time (TT) method. The dashed-horizontal line in each panel delineates the model level (≈10 hPa) where the TT method is used.

Drivers of the Seasonal Delay of Rainfall in the Amazon Rainforest

The findings of this work were presented at the Pacific Northwest National Laboratory (PNNL) August 2021 Intern Symposium

Tropical precipitation has a distinct annual cycle characterized by an amplitude, the range between wet and dry seasons, and phase, their onset timing. Previous studies have reported a seasonal delay in the onset of precipitation in observations over the northern tropical land driven by changes in greenhouse gases (GHG) and anthropogenic aerosols (AER). Though it is well known that the phase of the annual precipitation cycle is projected to be delayed over land under global warming, it is unclear whether changes in GHG and AERs are the dominate external forcings for different regions. Due to its outsized importance in the global Earth system, changes in the precipitation cycle in the Amazon are of particular interest. Here, we use multi-model output of historical and individual forcing simulations to show that the seasonal delay of precipitation in the Amazon rainforest cannot be fully explained by changes in GHG and AER. We examine the impact of land use and land cover change (LULCC; e.g. deforestation) on the seasonal delay of rainfall by examining the historical and future simulations without changes in LULCC, and we perform an atmospheric energy budget analysis for the Amazon Basin.

This work was done at PNNL in collaboration with Dr. L. Ruby Leung and Dr. Fengfei Song.

NetQuil: A Quantum Playground for Distributed Quantum Computing Simulations

  • White Paper
  • Pypi Installation
  • Doc Center
  • GitHub Source Code
  • NetQuil is an open-source Python framework for quantum networking simulations built on the quantum computing framework pyQuil, by Rigetti Computing. NetQuil is built for testing ideas in quantum network topology and distributed quantum protocol. It allows users to create multi-agent networks, connect parties through classical and quantum channels, and introduce noise. NetQuil also makes running multiple trials for non-deterministic experiments, reviewing traffic in real-time, and synchronizing agents based on local and master clocks simple and easy. We provide an overview of the state of distributed quantum protocol and a basic introduction to netQuil's framework. Finally, we present several demonstrations of canonical quantum information protocols built using netQuil's distributed quantum gates and pyQuil.

    This project was completed with Matthew Radzihovsky and Yewon Gim at AT&T Foundry, Palo Alto.

    Henry's Fork Foundation Water Quality Monitoring Network


    While at the Henry's Fork Foundation (HFF), I helped build an automated data-collection network of sensors in the Henry’s Fork Water Shed near Yellowstone National Park. To accompany this data-collection network, I then led an effort to build and published a real-time water quality monitoring website. Since 2018, the website has been frequently used by local scientists to study the health of the Henry's Fork hydrological system and has served as the cornerstone of HFF’s scientific communication mission.

    This project was completed with Melissa Muradian and Justin Appleby.