Atmospheric rivers (ARs) are filamentary structures within the atmosphere that account for a substantial portion of poleward moisture transport and play an important role in Earth’s hydroclimate. However, there is no one quantitative definition for what constitutes an atmospheric river, leading to uncertainty in quantifying how these systems respond to global change. This study seeks to better understand how different AR detection tools (ARDTs) respond to changes in climate states utilizing single-forcing climate model experiments under the aegis of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP). We compare a simulation with an early Holocene orbital configuration and another with CO2 levels of the Last Glacial Maximum to a pre-industrial control simulation to test how the ARDTs respond to changes in seasonality and mean climate state, respectively. We find good agreement among the algorithms in the AR response to the changing orbital configuration, with a poleward shift in AR frequency that tracks seasonal poleward shifts in atmospheric water vapor and zonal winds. In the low CO2 simulation, the algorithms generally agree on the sign of AR changes but there is substantial spread in their magnitude, indicating that mean-state changes lead to larger uncertainty. This disagreement likely arises primarily from differences between algorithms in their thresholds for water vapor and its transport used for identifying ARs. These findings warrant caution in ARDT selection for paleoclimate and climate change studies in which there is a change to the mean climate state, as ARDT selection contributes substantial uncertainty in such cases.

This manuscript discusses the challenges in detecting and attributing recently observed trends in the Atlantic hurricanes and the epistemic uncertainty we face in assessing future hurricane risk. Data used here include synthetic storms downscaled from five CMIP5 models by the Columbia HAZard model (CHAZ), and directly simulated storms from high-resolution climate models. We examine three aspects of recent hurricane activity: the upward trend and multi-decadal oscillation of the annual frequency, the increase in storm wind intensity, and the downward trend in the forward speed. Some datasets suggest that these trends and oscillation are forced while others suggest that they can be explained by natural variability. Future projections under warming climate scenarios also show a wide range of possibilities, especially for the annual frequencies, which increase or decrease depending on the choice of moisture variable used in the CHAZ model and on the choice of climate model. The uncertainties in the annual frequency lead to epistemic uncertainties in the future hurricane risk assessment. Here, we investigate the reduction of epistemic uncertainties on annual frequency through a statistical practice – likelihood analysis. We find that historical observations are more consistent with the simulations with increasing frequency but we are not able to rule out other possibilities. We argue that the most rational way to treat epistemic uncertainty is to consider all outcomes contained in the results. In the context of hurricane risk assessment, since the results contain possible outcomes in which hurricane risk is increasing, this view implies that the risk is increasing.

Extreme temperature events have traditionally been detected assuming a unimodal distribution of temperature data. We found that surface temperature data can be described more accurately with a multimodal rather than a unimodal distribution. Here, we applied Gaussian Mixture Models (GMM) to daily near-surface maximum air temperature data from the historical and future Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations for 46 land regions defined by the Intergovernmental Panel on Climate Change (IPCC). Using the multimodal distribution, we found that temperature extremes, defined based on daily data in the warmest mode of the GMM distributions, are getting more frequent in all regions. Globally, a 10-year extreme temperature event relative to 1980-2010 conditions will occur 15 times more frequently in the future under 3.0oC of Global Warming Levels (GWL). The frequency increase can be even higher in tropical regions, such that 10-year extreme temperature events will occur almost twice a week. Additionally, we analysed the change in future temperature distributions under different GWL and found that the hot temperatures are increasing faster than cold temperatures in low latitudes, while the cold temperatures are increasing faster than the hot temperatures in high latitudes. The smallest changes in temperature distribution can be found in tropical regions, where the annual temperature range is small. Our method captures the differences in geographical regions and shows that the frequency of extreme events will be even higher than reported in previous studies.

The 1997 New Year’s flood event was the most costly in California’s history. This compound extreme event was driven by a category 5 atmospheric river that led to widespread snowmelt. Extreme precipitation, snowmelt, and saturated soils produced heavy runoff causing widespread inundation in the Sacramento Valley. This study recreates the 1997 flood using the Regionally Refined Mesh capabilities of the Energy Exascale Earth System Model (RRM-E3SM) under prescribed ocean conditions. Understanding the processes causing extreme events inform practical efforts to anticipate and prepare for such events in the future, and also provides a rich context to evaluate model skill in representing extremes. Three California-focused RRM grids, with horizontal resolution refinement of 14km down to 3.5km, and six forecast lead times, 28 December 1996 at 00Z through 30 December 1996 at 12Z, are assessed for their ability to recreate the 1997 flood. Planetary to synoptic scale atmospheric circulations and integrated vapor transport are weakly influenced by horizontal resolution refinement over California. Topography and mesoscale circulations, such as the Sierra barrier jet, are prominently influenced by horizontal resolution. The finest resolution RRM-E3SM simulation best represents storm total precipitation and storm duration snowpack changes. Traditional time-series and causal analysis frameworks are used to examine runoff sensitivities state-wide and above major reservoirs. These frameworks show that horizontal resolution plays a more prominent role in shaping reservoir inflows, namely the magnitude and time-series shape, than forecast lead time, 2-to-4 days prior to the 1997 flood onset.

The Atmospheric River (AR) Tracking Method Intercomparison Project (ARTMIP) is a community effort to systematically assess how the uncertainties from AR detectors (ARDTs) impact our scientific understanding of ARs. This study describes the ARTMIP Tier 2 experimental design and initial results using the Coupled Model Intercomparison Project (CMIP) Phases 5 and 6 multi-model ensembles. We show that AR statistics from a given ARDT in CMIP5/6 historical simulations compare remarkably well with the MERRA-2 reanalysis. In CMIP5/6 future simulations, most ARDTs project a global increase in AR frequency, counts, and sizes, especially along the western coastlines of the Pacific and Atlantic oceans. We find that the choice of ARDT is the dominant contributor to the uncertainty in projected AR frequency when compared with model choice. These results imply that new projects investigating future changes in ARs should explicitly consider ARDT uncertainty as a core part of the experimental design.

In 2021, Nepal underwent its most severe fire season, resulting in a fire rate 10 times greater than the historical average in many areas of the country with record-high air pollution levels. Leading the fire outbreaks in March of 2021, the country experienced an extreme precipitation deficit and drought in the post-monsoon season. Current community forest management practices and resultant forest growth may have exacerbated the conflagration, but an analysis using observational, reanalysis, and climate model ensemble data indicates that climate variability and climate change induced severe drought conditions that resulted in the anomalous fire season. While warning of the likely re-occurrence of extremely active fire seasons in Nepal through the end of the 21st century, this research also proposes a statistical model for sub-seasonal prediction that could help mitigate the projected effects of the drought-fire paradigm.

Climate models at high resolution (~25 km horizontal grid spacing) can permit realistic simulations of tropical cyclones (TCs), thus promising the investigation of these high-impact extreme events under present and future climates. On the global scale, simulations with the Community Atmosphere Model version 5 (CAM5) present a reasonable TC climatology under prescribed present-day (1980-2005) sea surface temperature (SST) and greenhouse gas (GHG) forcing. However, for the disaster-prone western North Pacific (WNP) region, biases in TC genesis frequency and location persist across various configurations. The biases under-represent the basin’s share in global TC climatology, complicating the fidelity of future projections. This study addresses these model biases in WNP by evaluating the large-scale environmental controls of TC genesis in CAM5 with two aerosol configurations. Across the two configurations, the lack of mid-level moisture is consistently identified as the leading cause of the deficit in simulated WNP TC genesis. This lack of mid-level moisture in WNP TC main develop region is potentially linked to previously identified deficits in Pacific warm pool precipitation at high horizontal resolution in CAM5, as well as biases in the East Asian Summer Monsoon circulation and moisture transport. Additional CAM5 simulation experiments will explore the effect of moisture nudging on the large-scale environment and subsequent TC genesis, tracks, and intensity development. For a chosen year, simulations covering WNP peak TC season (July - October) under otherwise identical forcing (SST, GHG etc.) will be run with and without nudging the specific humidity field towards MERRA-2 reanalysis. The insight into the biases of basin-scale TC simulation under the present climate and potential improvements will help reduce the uncertainty in future-climate projections, in the interest of disaster risk management.

Daily maximum temperatures during the 2021 heatwave in the Pacific Northwest United States and Canada shattered century old records. Multiple causal factors, including anthropogenic climate change, contributed to these high temperatures, challenging traditional methods of attributing human influence. We demonstrate that the observed 2021 daily maximum temperatures are far above the bounds of Generalized Extreme Value distributions fitted from historical data. Hence, confidence in Granger causal inference statements about the human influence on this heatwave is low. Alternatively, we present a more conditional hindcast attribution study using two regional models. We performed ensembles of simulations of the heatwave to investigate how the event would have changed if it had occurred without anthropogenic climate change and with future warming. We found that human activities caused a 1C increase in heatwave temperatures. Future warming would lead to a 5C increase in heatwave temperature by the end of the 21st century.