Precipitation efficiency (PE) relates cloud condensation to precipitation and thus reflects how much of the total atmospheric condensate reaches the surface as precipitation. Because the PE in convective storms is directly linked to their updraft- and downdraft dynamics, it is a helpful metric to identify convective processes that influence precipitation. However, km-scale model simulations do not properly resolve convective processes such as individual updrafts and entrainment, which raises the question if such simulations can accurately represent PE. Here, we present two methods to derive PE from standard model output. The first method estimates PE from the state variables vertical velocity, temperature and pressure, whereas the second method estimates PE from ice water path (IWP) and precipitation. We validate the proposed methods with the explicitly calculated PE using a set of idealized Weather Research and Forecast model simulations of organized midlatitude convective storms at different horizontal grid spacings. We show that PE can be reliably estimated from state variables with an error of less than 5%, partly due to error cancellation effects. Additionally, PE can be simulated by km-scale models within ~15% accuracy compared to large-eddy simulations (LESs). The IWP-method is slightly less accurate with a stronger grid spacing dependency of the error, but since it is based on observable quantities, it allows for a validation of simulated PE with satellite observations. Finally, we analyze the grid spacing dependency of the climate change signal of PE and find that future decreases in PE in LESs are robustly captured by km-scale models.

Global kilometer-scale models are the future of Earth system models as they can explicitly simulate organized convective storms and their associated extreme weather. Here, we comprehensively examined tropical mesoscale convective system (MCS) characteristics in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non-hydrostatic domains) models for both summer and winter phases by applying eight different feature trackers to the simulations and satellite observations. Although different trackers produce substantial differences (a factor of 2-3) in observed MCS frequency and their contribution to total precipitation, model-observation differences in MCS statistics are more consistent among the trackers. DYAMOND models are generally skillful in simulating tropical mean MCS frequency, with multi-model mean biases of 2.9% over land and -0.5% over ocean. However, most models underestimate the MCS precipitation amount (23%) and their contribution to total precipitation (17%) relative to observations. These biases show large inter-model variability, but are generally smaller over land (13%) than over ocean (21%) on average. MCS diurnal cycle and cloud shield characteristics are better simulated than precipitation. Most models overestimate MCS precipitation intensity and underestimate stratiform rain contribution (up to a factor of 2), particularly over land. Models also predict a wide range of precipitable water in the tropics compared to reanalysis and satellite observations, and many models simulate a greater sensitivity of MCS precipitation intensity to precipitable water. The MCS metrics developed in this work provide process-oriented diagnostics for future model development efforts.

Organized deep convection plays a critical role in the global water cycle and drives extreme precipitation events in tropical and mid-latitude regions. However, simulating deep convection remains challenging for modern weather forecasts and climate models due to the complex interactions of processes from microscales to mesoscales. Recent models with kilometer-scale (km-scale) horizontal grid spacings (Δx) offer notable improvements in simulating deep convection compared to coarser-resolution models. Still, deficiencies in representing key physical processes, such as entrainment, lead to systematic biases. Additionally, evaluating model outputs using process-oriented observational data remains difficult. In this study, we present an ensemble of MCS simulations with Δx spanning the deep convective grey zone (Δx from 12 km to 125 m) in the Southern Great Plains of the U.S. and the Amazon Basin. Comparing these simulations with Atmospheric Radiation Measurement (ARM) wind profiler observations, we find greater Δx sensitivity in the Amazon Basin compared to the Great Plains. Convective drafts converge structurally at sub-kilometer scales, but some discrepancies, such as too-deep up- and downdrafts and too-weak peak downdrafts in both regions or too-strong updrafts in Amazonian storms remain. Overall, we observe higher Δx sensitivity in the tropics, including an artificial buildup in vertical velocities at five times the Δx, suggesting a need for Δx≤250 m. Nevertheless, bulk convergence - agreement of storm average statistics - is achievable with km-scale simulations within a ±10 % error margin, with Δx=1 km providing a good balance between accuracy and computational cost.

Using the Weather Research and Forecasting (WRF) model with two planetary boundary layer schemes, ACM2 and MYNN, convection-permitting model (CPM) regional climate simulations were conducted for a 6-year period at a 15-km grid spacing covering entire South America and a nested convection-permitting 3-km grid spacing covering the Peruvian central Andes region. These two CPM simulations along with a 4-km simulation covering South America produced by National Center for Atmospheric Research, three gridded global precipitation datasets, and rain gauge data in Peru and Brazil, are used to document the characteristics of precipitation and MCSs in the Peruvian central Andes region. Results show that all km-scale simulations generally capture the spatiotemporal patterns of precipitation and MCSs at both seasonal and diurnal scales, although biases exist in aspects such as precipitation intensity and MCS frequency, size, propagation speed, and associated precipitation intensity. The 3-km simulation using MYNN scheme generally outperforms the other simulations in capturing seasonal and diurnal precipitation over the mountain, while both it and the 4-km simulation demonstrate superior performance in the western Amazon Basin, based on the comparison to the gridded precipitation products and gauge data. Dynamic factors, primarily low-level jet and terrain-induced uplift, are the key drivers for precipitation and MCS genesis along the east slope of the Andes, while thermodynamic factors control the precipitation and MCS activity in the western Amazon Basin and over elevated mountainous regions. The study suggests aspects of the model needing improvement and the choice of better model configurations for future regional climate projections.

Mesoscale convective systems (MCSs) are clusters of thunderstorms that are important in Earth’s water and energy cycle. Additionally, they are responsible for extreme events such as large hail, strong winds, and extreme precipitation. Automated object-based analyses that track MCSs have become popular since they allow us to identify and follow MCSs over their entire life cycle in a Lagrangian framework. This rise in popularity was accompanied by an increasing number of MCS tracking algorithms, however, little is known about how sensitive analyses are concerning the MCS tracker formulation. Here, we assess differences between six MCS tracking algorithms on South American MCS characteristics and evaluating MCSs in kilometer-scale simulations with observational-based MCSs over three years. All trackers are run with a common set of MCS classification criteria to isolate tracker formulation differences. The tracker formulation substantially impacts MCS characteristics such as frequency, size, duration, and contribution to total precipitation. The evaluation of simulated MCS characteristics is less sensitive to the tracker formulation and all trackers agree that the model can capture MCS characteristics well across different South American climate zones. Dominant sources of uncertainty are the segmentation of cloud systems and the treatment of splitting and merging of storms in MCS trackers. Our results highlight that comparing MCS analyses that use different tracking algorithms is challenging. We provide general guidelines on how MCS characteristics compare between trackers to facilitate a more robust assessment of MCS statistics in future studies.

Globally, extreme precipitation events cause enormous impacts. Climate change increases the frequency and intensity of extreme precipitation, which in combination with rising population enhances exposure to major floods. An improved understanding of the atmospheric processes that cause extreme precipitation events would help to advance predictions and projections of such events. To date, such analyses have typically been performed rather unsystematically and over limited areas (e.g., the U.S.) which has resulted in contradictory findings. Here we present the Multi Object Analysis of Atmospheric Phenomenon (MOAAP) algorithm that uses a set of nine common atmospheric variables to identify and track tropical and extra-tropical cyclones, anticyclones, atmospheric rivers (ARs), mesoscale convective systems (MCSs), and frontal zones. We apply the algorithm to global historical data between 2000 to 2020. We find that MCSs produce the vast majority of extreme precipitation in the tropics and some mid-latitude land regions, while extreme precipitation in mid- and high-latitude ocean and coastal regions are dominated by cyclones and ARs. Importantly, most extreme precipitation events are associated with interacting features across scales that intensify precipitation. These interactions, however, can be a function of the rarity (e.g., return period) of extreme events. The presented methodology and results could have wide-ranging applications including training of machine learning methods, lagrangian-based evaluation of climate models, and process-based understanding of extreme precipitation in a changing climate.

Holistic approaches are needed to investigate the capacity of current water resource operations and infrastructure to sustain water supply and critical ecosystem health under projected drought conditions. Drought vulnerability is complex, dynamic, and challenging to assess, requiring simultaneous consideration of changing water demand, use and management, hydrologic system response, and water quality. We are bringing together a community of scientists from the U.S. Geological Survey, National Center for Atmospheric Research, Department of Energy, and Cornell University to create an integrated human-hydro-terrestrial modeling framework, linking pre-existing models, that can explore and synthesize system response and vulnerability to drought in the Delaware River Basin (DRB). The DRB provides drinking water to over 15 million people in New York, New Jersey, Pennsylvania, and Delaware. Critical water management decisions within the system are coordinated through the Delaware River Basin Commission and must meet requirements set by prior litigation. New York City has rights to divert water from the upper basin for water supply but must manage reservoir releases to meet downstream flow and temperature targets. The Office of the Delaware River Master administers provisions of the Flexible Flow Management Program designed to manage reservoir releases to meet water supply demands, habitat, and specified downstream minimum flows to repel upstream movement of saltwater in the estuary that threatens Philadelphia public water supply and other infrastructure. The DRB weathered a major drought in the 1960s, but water resource managers do not know if current operations and water demands can be sustained during a future drought of comparable magnitude. The integrated human-hydro-terrestrial modeling framework will be used to identify water supply and ecosystem vulnerabilities to drought and will characterize system function and evolution during and after periods of drought stress. Models will be forced with consistent input data sets representing scenarios of past, present, and future conditions. The approaches used to unify and harmonize diverse data sets and open-source models will provide a roadmap for the broader community to replicate and extend to other water resource issues and regions.

This is a test-case study assessing the ability of deep learning methods to generalize to a future climate (end of 21st century) when trained to classify thunderstorms in model output representative of the present-day climate. A convolutional neural network (CNN) was trained to classify strongly-rotating thunderstorms from a current climate created using the Weather Research and Forecasting (WRF) model at high-resolution, then evaluated against thunderstorms from a future climate, and found to perform with skill and comparatively in both climates. Despite training with labels derived from a threshold value of a severe thunderstorm diagnostic (updraft helicity), which was not used as an input attribute, the CNN learned physical characteristics of organized convection and environments that are not captured by the diagnostic heuristic. Physical features were not prescribed but rather learned from the data, such as the importance of dry air at mid-levels for intense thunderstorm development when low-level moisture is present (i.e., convective available potential energy). Explanation techniques also revealed that thunderstorms classified as strongly rotating are associated with learned rotation signatures. Results show that the creation of synthetic data with ground truth is a viable alternative to human-labeled data and that a CNN is able to generalize a target using learned features that would be difficult to encode due to spatial complexity. Most importantly, results from this study show that deep learning is capable of generalizing to future climate extremes and can exhibit out-of-sample robustness with hyperparameter tuning in certain applications.

Five of California’s ten largest wildfires occurred in 2020, with the largest complex shattering the previous record by more than 100 %. The year follows a decade containing extraordinary fire activity. Trend investigations focused on changes in human activities and atmospheric thermodynamics, while the impacts of changing atmospheric dynamics are largely unknown. Here we identify extreme weather types (XWTs) associated with historically large daily burned areas in eight Californian regions. These XWTs characterize dominant fire weather regimes varying in fire behavior types (plume-driven vs. wind-driven fires) and seasonality. 2020’s exceptional fires partly occurred during previously unrecognized XWTs, whose characteristics and recurrence intervals were largely unknown. Most of the strongly large-scale forced XWTs such as Santa Ana and Diablo events increased in frequency during the 20th century particularly in the Sand Diego and Bay Area region. These changes are likely not anthropogenically caused and predominantly due to climate internal variability. However, raising greenhouse gas concentrations significantly decrease thermal low XWTs in southern and increase them in central California. These XWTs occur during the hottest time in the year and will alter fire risk in the summer season.

Mesoscale convective systems (MCSs) are the main source of precipitation in the tropics and parts of the mid-latitudes and are responsible for high-impact weather worldwide. Studies showed that deficiencies in simulating mid-latitude MCSs in state-of-the-art climate models can be alleviated by kilometer-scale models. However, whether these models can also improve tropical MCSs and weather we can find model settings that perform well in both regions is understudied. We take advantage of high-quality MCS observations collected over the Atmospheric Radiation Measurement (ARM) facilities in the U.S. Southern Great Plains (SGP) and the Amazon basin near Manaus (MAO) to evaluate a perturbed physics ensemble of simulated MCSs with 4\,km horizontal grid spacing. A new model evaluation method is developed that enables to distinguish biases stemming from spatiotemporal displacements of MCSs from biases in their reflectivity and cloud shield. Amazon MCSs are similarly well simulated across these evaluation metrics than SGP MCSs despite the challenges anticipated from weaker large-scale forcing in the tropics. Generally, SGP MCSs are more sensitive to the choice of model microphysics, while Amazon cases are more sensitive to the planetary boundary layer (PBL) scheme. Although our tested model physics combinations had strengths and weaknesses, combinations that performed well for SGP simulations result in worse results in the Amazon basin and vice versa. However, we identified model settings that perform well at both locations, which include the Thompson and Morrison microphysics coupled with the Yonsei University (YSU) PBL scheme and the Thompson scheme coupled with the Mellorâ\euro“Yamadaâ\euro“Janjic (MYJ) PBL scheme.

Floods claim a high toll in fatalities and economic impacts. Despite their societal relevance, there is much more to learn about the projected changes in discharge and flooding. Here we force an operational hydrologic model over the state of Iowa with high-resolution convection-permitting climate-model precipitation to evaluate the response of 140 watersheds to climate change. At the end of the century, under the most aggressive scenario in terms of fossil fuel use, we show that the transition from snow to rainfall and a ~30% increase in extreme precipitation rates lead to a doubling of maximum discharge during the spring and extended the flood season into the fall. Total discharge volumes are also expected to increase. Our results suggest that flood projections based on extreme precipitation increases alone substantially underestimate future risk due to the nonlinearity of the hydrologic response explained by long-term soil moisture memory and its feedbacks with precipitation.