The research presented in this article enhances the ability to predict and prepare for compound flood events along the U.S. coastline.

This study provides a comprehensive analysis of isolated deep convection & mesoscale convective systems using self-organizing maps to categorize large-scale meteorological patterns and a tracking algorithm to monitor their life cycle.

Pyrocumulonimbus clouds are increasing in frequency as large wildfires become more prevalent in a warming climate. These clouds can inject smoke particles into the atmosphere, where they can remain suspended for several months.

This study explored the future effects of climate change and low-carbon energy transition (i.e., emission reduction) on Arctic offshore oil and gas production.

Using numerical simulations to reproduce the laboratory experiments, this study reveals that liquid droplets are present near the bottom surface, which warms and moistens the air in the chamber.

This work shows that linear pattern scaling is an effective means of obtaining global-to-local relationships for CMIP6 models, as it has been in past model eras.

This study examined the role of river sinuosity using computer models to understand what drives hyporheic exchange, a process that significantly affects water quality and ecosystem health.

Skillful predictions of tropical cyclone activity on subseasonal time scales may help mitigate their destructive impacts. This study investigates the combined impacts of atmospheric phenomena to better understand cyclone activity.

Topographic variations have substantial impacts on surface hydrologic processes. This study introduced a new subgrid structure and methods to increase model accuracy for snow water equivalent predictions.

The Delaware River Basin is projected to experience a regional increase in flood severity under future climate scenarios.

To gain a mechanistic understanding of the physical processes responsible for the enhanced hurricane cold wakes near the Southeast United States, investigators used ocean reanalysis datasets.

Cloud and its radiative effect are among the determining processes for the energy balance of the global climate; they are also the most challenging processes for the climate models to simulate.

Researchers develop a diagnostic framework to identify the sources of global monsoon simulation biases from an energy transport perspective.

The results of this study reveal that the degree of Arctic amplification, despite being controlled by complicated interactions among multiple factors, can be analytically understood.

Researchers found that in a future where the Great Plains are 4 to 6 degrees Celsius (°C) warmer as projected in a high-emission scenario, these storms could bring three times more intense rainfall.

Researchers develop comprehensive framework for the Energy Exascale Earth System Model, incorporating advanced river and ocean models that improve how such interactions are simulated

Researchers use observations and numerical models to examine changes in tropical cyclone intensification rates and their environment in global coastal regions

Scientists develop a water tracer tool in an advanced hydrologic model to understand the importance of modeling lateral flow in hydrologic simulations.

Researchers develop new, weakly coupled land data assimilation system to make better climate predictions and projections.

Scientists use water vapor tracers incorporated in a climate model to tag moisture from local evapotranspiration and trace their evolution through different processes.