We highlight the inconsistencies of climate projections for much of the Global South from six generations of IPCC assessments and note that these have compounded the many challenges it faces in adapting to climate change.
Using two climate models (E3SM2 and CESM2), we attribute the differences in the strength of monsoon-ENSO connections to two specific processes, with about half due to mean tropical SSTs and half due to ENSO amplitude.
The world saw another year full of extreme weather events resulting from climate change in 2022, from intense storms to soaring temperatures and rising sea levels.
Ice nucleation in mixed-phase clouds influences climate projections. McGraw et al. show that total cloud feedback is independent of ice nucleation scheme when cloud phase is matched to satellite retrievals, showing the need to capture observed cloud phase to predict future change.
A deep convective cloud (DCC) detection approach for MODIS observations is developed with a CloudSat trained machine learning model. This approach provides high temporal and spatial resolution DCC distributions to support intra-seasonal to interannual climate variability studies.
The nonstationary roles of regional forcings from alongshore wind stress and sea level pressure (SLP) in driving low-frequency (interannual-to-decadal) sea level variability along the U.S. east coast for the 1959–2020 period are investigated.
