Hazards from convective weather pose a serious threat to the continental United States (CONUS) every year. Previous studies have examined how future projected changes in climate might impact the frequency and intensity of severe weather using simulations with both convection-permitting regional models and coarser climate and Earth system models. However, many of these studies have been limited to single representations of the future climate state with little insight into the uncertainty of how the population of convective storms may evolve. To thoroughly explore this aspect, a large ensemble of Earth system model simulations was implemented to investigate how forced responses in large-scale convective environments might be modulated by internal climate variability. Daily data from an ensemble of 50 simulations with the most recent version of the Community Earth System Model was used to examine changes in the severe weather environment over the eastern CONUS during boreal spring from 1870-2100. Results indicate that forced changes in convective environments were small between 1870 and 1990, but throughout the 21st century, convective available potential energy and atmospheric stability (convective inhibition) is projected to increase while 0-6 km vertical wind shear decreases. Internal climate variability can either significantly enhance or suppress these forced changes. The time evolution of bivariate distributions of convective indices illustrates that future springtime convective environments over the eastern CONUS will be characterized by relatively less frequent, less organized, but deeper, more intense convection. Future convective environments will also be less supportive of the most severe convective modes and associated hazards.

Continued climate warming, together with the overall evaluation and implementation of a range of climate mitigation and adaptation approaches, has prompted increasing research into proposed solar climate intervention (SCI) methods, such as stratospheric aerosol injection (SAI). SAI would use aerosols to reflect a small amount of incoming solar radiation away from Earth to stabilize or reduce future warming due to increasing greenhouse gas concentrations. Research into the possible risks and benefits of SAI relative to the risks from climate change is emerging. There is not yet, however, an adequate understanding of how SAI might impact human and natural systems. For instance, little to no research to date has examined how SAI might impact environmental conditions critical to the formation of severe convective weather over the United States (U.S.). This study uses ensembles of Earth system model simulations of future climate change, with and without hypothetical SAI deployment, to examine possible future changes in thermodynamic and kinematic parameters critical to the formation of severe weather during convectively active seasons over the U.S. Results show that simulated forced changes in thermodynamic parameters are significantly reduced under SAI relative to a no-SAI world, while simulated changes in kinematic parameters are more difficult to distinguish. Also, unforced internal climate variability is likely to significantly modulate the projected forced climate changes over large regions of the U.S.

Current global actions to reduce greenhouse gas emissions are very likely to be insufficient to meet the climate targets outlined under the Paris Agreement. This motivates research on possible methods for intervening in the Earth system to minimize climate risk while decarbonization efforts continue. One such hypothetical climate intervention is stratospheric aerosol injection (SAI), where reflective particles would be released into the stratosphere to cool the planet by reducing solar insolation. The climate response to SAI is not well understood, particularly on short-term time horizons frequently used by decision makers and planning practitioners to assess climate information. This knowledge gap limits informed discussion of SAI outside the scientific community. We demonstrate two framings to explore the climate response in the decade after SAI deployment in modeling experiments with parallel SAI and no-SAI simulations. The first framing, which we call a snapshot around deployment, displays change over time within the SAI scenarios and applies to the question “What happens before and after SAI is deployed in the model?” The second framing, the intervention impact, displays the difference between the SAI and no-SAI simulations, corresponding to the question “What is the impact of a given intervention relative to climate change with no intervention?” We apply these framings to annual mean 2-meter temperature, precipitation, and a precipitation extreme in the first two experiments to use large ensembles of Earth system models that comprehensively represent both the SAI injection process and climate response, and connect these results to implications for other climate variables.

While the Madden Julian Oscillation (MJO) is known to influence the midlatitude circulation and its predictability on subseasonal-to-seasonal (S2S) timescales, little is known how this connection may change with anthropogenic warming. This study investigates changes in the causal pathways between the MJO and the North Atlantic Oscillation (NAO) within historical and SSP585 simulations of the CESM2-WACCM coupled climate model. Two data-driven approaches are employed, namely, the STRIPES index and graphical causal models. These approaches collectively indicate that the MJO’s influence on the North Atlantic strengthens in the future, consistent with an extended jet-stream. In addition, the graphical causal models allow us to distinguish the causal pathways associated with the teleconnections. While both a stratospheric and tropospheric pathway connect the MJO to the North Atlantic in CESM2-WACCM, the strengthening of the MJO-NAO causal connection over the 21st century is shown to be due exclusively to teleconnections via the tropospheric pathway.

Despite global warming, SSTs in the Southern Ocean (SO) have cooled in recent decades largely as a result of internal variability. The global impact of this cooling is assessed by nudging evolving SO SST anomalies to observations in an ensemble of coupled climate model simulations under historical radiative forcing, and comparing against a control ensemble. The most significant remote response to observed SO cooling is found in the tropical South Atlantic, where increased clouds and strengthened trade winds cool the sea surface, partially offsetting the radiatively-forced warming trend. The SO ensemble produces a more realistic tropical South Atlantic SST trend, and exhibits a higher pattern correlation with observed SST trends in the greater Atlantic basin, compared to the control ensemble. SO cooling also produces a significant increase in Antarctic sea ice, but not enough to offset radiatively-induced ice loss; thus, the SO ensemble remains biased in its sea ice trends.

As anthropogenic activities continue to drive increases in extreme events, the fundamental solution of reducing greenhouse gas emissions remains elusive. Thus, there is growing interest in stratospheric aerosol injection (SAI) to offset some of the most dangerous consequences of climate change. If SAI was deployed at a global scale, it would likely be easy to detect by some metrics. However, the detectability of SAI on extreme events might be more difficult, given the presence of natural climate variability. We examine this question in climate model simulations of SAI. Specifically, we train a logistic regression model to predict whether a map of global extremes came from climate simulations with or without SAI. The timing of accurate predictions is a quantification of the time to detection of SAI impacts. We find that regional changes in extreme temperature and precipitation are robustly detected within 1 and 15 years of initial SAI injection, respectively.

The week 3-6 averaged winter North Atlantic Oscillation (NAO) predictive skill in a state-of-the-art coupled climate prediction system is attributed to two principle sources: upper and lower boundary conditions linked to the stratosphere and El Niño-Southern Oscillation (ENSO), respectively. A 20-member ensemble of 45-day reforecasts over 1999-2015 is utilized, together with uninitialized simulations with the atmospheric component of the prediction system forced with observed radiative forcing and lower boundary conditions. NAO forecast skill for lead times out to six weeks is higher following extreme stratospheric polar vortex conditions (weak and strong vortex events) compared to neutral states. Enhanced week 3-6 NAO predictive skill for weak vortex events results primarily from stratospheric downward coupling to the troposphere, while enhanced skill for strong vortex events can be partly attributed to lower boundary forcing related to the ENSO phenomenon. Implications for forecast system development and improvement are discussed.

A framework to enable Earth system predictability research on the subseasonal timescale is developed with the Community Earth System Model, version 2 (CESM2) using two model configurations that differ in their atmospheric components. One configuration uses the Community Atmosphere Model, version 6 (CAM6) with its top near 40 km, referred to as CESM2(CAM6). The other employs the Whole Atmosphere Community Climate Model, version 6 (WACCM6) whose top extends to ~ 140 km in the vertical and it includes fully interactive tropospheric and stratospheric chemistry (CESM2(WACCM6)). Both configurations were used to carry out subseasonal reforecasts for the time period 1999 to 2020 following the Subseasonal Experiment’s (SubX) protocol. CESM2(CAM6) and CESM2(WACCM6) show very similar subseasonal prediction skill of 2-meter temperature, precipitation, the Madden-Julian Oscillation (MJO), and North Atlantic Oscillation (NAO) to the Community Earth System Model, version 1 with the Community Atmosphere Model, version 5 (CESM1(CAM5)) and to operational models. CESM2(CAM6) and CESM2(WACCM6) reforecast sets provide a comprehensive dataset for predictability research of multiple Earth system components, including three-dimensional output for many variables, and output specific to the mesosphere and lower-thermosphere (MLT) region. We show that MLT variability can be predicted ~ 10 days in advance of sudden stratospheric warming events. Weekly real-time forecasts with CESM2(WACCM6) contribute to the multi-model mean ensemble forecast used to issue the NOAA weeks 3-4 outlooks. As a freely available community model, both CESM2 configurations can be used to carry out additional experiments to elucidate sources of subseasonal predictability.