Cooperative Institute for Research in Environmental Sciences

Exploring divergent long-term stratospheric aerosol injection scenarios with the G2-SAI and ARISE-hybrid experiments

Abstract. Stratospheric aerosol injection (SAI) simulations are often short relative to climatic timescales and conducted against a background that evolves due to changes in anthropogenic greenhouse gas emissions and other forcings. This can cause challenges in assessing certain...

Gravity Wave Energy Baselines (∼30–110 km) Established From Lidar Observations at McMurdo (77.84°S, 166.67°E), Antarctica: Energy Dissipation and Constituent Diffusion

Abstract Lidar observations above McMurdo, Antarctica from 2011 to 2024 are used to characterize gravity wave (GWs) potential energies () in the mesosphere and lower thermosphere (MLT). Alongside characterizations in the middle atmosphere (30–70 km), the GW baselines show dissipa...

Exploring the feasibility of an air sensor array for real-time detection and characterization of VOCs

Abstract. Volatile organic compounds (VOCs) are an important class of atmospheric chemical species that can be directly harmful to human health and contribute to the formation of hazardous secondary products. Measurements of ambient VOCs are typically made using “offline” techniq...

Middle atmosphere chemical and dynamical effects in the CCMI-2022 stratospheric aerosol injection scenario

Abstract. Stratospheric aerosol injection (SAI) could slow surface warming, however, potential side effects include changes in stratospheric ozone and changes in regional surface temperatures and precipitation resulting from tropical lower stratospheric warming. Previous multi-mo...

Evaluation of the Impact of Aged Biomass Burning Emissions on the Global Ozone From the Surface to the Lower Stratosphere

Abstract Biomass burning (BB) is a major global sources of ozone (O 3 ) precursors, including nitrogen oxides (NO x ), carbon monoxide (CO), and volatile organic compounds (VOCs). Here, we combine Community Earth System Model 1 with airborne measurements from the NASA Atmospheric...

Layer‐Dependent Diurnal Moist Static Energy Biases in the Unified Forecast System During the MJO Preconditioning Stage: A DYNAMO Case Study

Abstract Accurate simulation of the Madden‐Julian Oscillation (MJO) requires realistic diurnal evolution and vertical distribution of moist static energy (MSE). While prior studies documented MSE bias in simulations, vertically dependent physical processes responsible for these b...

Can Neural Networks Think Like Geomorphologists?

Abstract Understanding the connection between topography and the processes and properties that shape landscapes both helps us better understand landscape evolution, and use topography to infer information relevant for a wide range of human activities. Deep learning methods, like...