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Ligia Bernardet
,
Lisa Bengtsson
,
Patrick A. Reinecke
,
Fanglin Yang
,
Man Zhang
,
Kyle Hall
,
James Doyle
,
Matus Martini
,
Grant Firl
, and
Lulin Xue

Abstract

The Common Community Physics Package (CCPP) is a state-of-the-art infrastructure designed to facilitate community-wide development of atmospheric physics parameterizations, support their interoperability among different modeling centers, and enable the transition of research to operations in NWP and climate modeling. The CCPP consists of two elements: the Physics (a repository of parameterizations) and the Framework (an infrastructure for interfacing the parameterizations with host models). The CCPP is a community resource: its latest release has 23 primary parameterizations, which can be organized into six supported suites. It is distributed with a single-column model to facilitate physics development and experimentation. The Developmental Testbed Center provides support to users and developers. A key aspect of the CCPP is its interoperability, that is, its ability to be used by multiple host models. This enables synergistic collaboration among groups dispersed over various institutions and working on various models. In this article we provide an overview of the CCPP and how it is being used in two leading modeling systems. The CCPP is part of the Unified Forecast System (UFS), is included in the NOAA operational Hurricane Analysis and Forecast System (HAFS) version one, and is slated for use in all upcoming NOAA global and limited-area UFS applications for operations. Similarly, the CCPP has been integrated into the Navy Environmental Prediction System Using a Nonhydrostatic Engine (NEPTUNE) model and is undergoing testing for upcoming transition to operations. These experiences make physics interoperability a reality and open the doors for much broader collaborative efforts on ESM development.

Open access
Laura Thomas-Walters
,
Matthew H. Goldberg
,
Sanguk Lee
,
Aidan Lyde
,
Seth A. Rosenthal
, and
Anthony Leiserowitz

Abstract

Extreme weather, including heat waves, poses a significant threat to ecosystems and human health. As global temperatures continue to rise, the frequency and severity of heat waves will increase. Because of this, communicating heat-related risks to the public is increasingly important. One commonly-used communication tool is the Climate Shift Index (CSI), which establishes how much more likely an extreme weather event, such as a heat wave, has been made by climate change. To test the impact of the CSI on people’s understanding of the links between climate change and extreme weather, we conducted an experiment informing 3,902 American adults that climate change made the July 2023 heat wave in the U.S. at least 5 times more likely. In addition to this standard CSI wording and 2 control messages, we also explored the effectiveness of reframing magnitude as a percentage, and whether mechanistic and attribution explanations of the relationship between climate change and heat waves further increase understanding. All treatments increased the belief that climate change made the July 2023 heat wave more likely and is making heat waves in general more likely as well. Additionally, we found that expressing the magnitude as a percentage was more effective than the standard CSI framing. We also found that just talking about the heatwave, without mentioning climate change, was enough to change beliefs.

Restricted access
Roy Barkan
,
Kaushik Srinivasan
, and
James C. McWilliams

Abstract

The interactions between oceanic mesoscale eddies, submesoscale currents, and internal gravity waves (IWs) are investigated in submesoscale-resolving realistic simulations in the North Atlantic Ocean. Using a novel analysis framework that couples the coarse-graining method in space with temporal filtering and a Helmholtz decomposition, we quantify the effects of the interactions on the cross-scale kinetic energy (KE) and enstrophy fluxes. By systematically comparing solutions with and without IW forcing, we show that externally forced IWs stimulate a reduction in the KE inverse cascade associated with mesoscale rotational motions and an enhancement in the KE forward cascade associated with divergent submesoscale currents, i.e., a “stimulated cascade” process. The corresponding IW effects on the enstrophy fluxes are seasonally dependent, with a stimulated reduction (enhancement) in the forward enstrophy cascade during summer (winter). Direct KE and enstrophy transfers from currents to IWs are also found, albeit with weaker magnitudes compared with the stimulated cascades. We further find that the forward KE and enstrophy fluxes associated with IW motions are almost entirely driven by the scattering of the waves by the rotational eddy field, rather than by wave–wave interactions. This process is investigated in detail in a companion manuscript. Finally, we demonstrate that the stimulated cascades are spatially localized in coherent structures. Specifically, the magnitude and direction of the bidirectional KE fluxes at submesoscales are highly correlated with, and inversely proportional to, divergence-dominated circulations, and the inverse KE fluxes at mesoscales are highly correlated with strain-dominated circulations. The predominantly forward enstrophy fluxes in both seasons are also correlated with strain-dominated flow structures.

Restricted access
Lihui Ji
and
Ana P. Barros

Abstract

A 3D numerical model was built to serve as a virtual microphysics laboratory (VML) to investigate rainfall microphysical processes. One key goal for the VML is to elucidate the physical basis of warm precipitation processes toward improving existing parameterizations beyond the constraints of past physical experiments. This manuscript presents results from VML simulations of classical tower experiments of raindrop collisional collection and breakup. The simulations capture large raindrop oscillations in shape and velocity in both horizontal and vertical planes and reveal that drop instability increases with diameter due to the weakening of the surface tension compared with the body force. A detailed evaluation against reference experimental datasets of binary collisions over a wide range of drop sizes shows that the VML reproduces collision outcomes well including coalescence, and disk, sheet, and filament breakups. Furthermore, the VML simulations captured spontaneous breakup, and secondary coalescence and breakup. The breakup type, fragment number, and size distribution are analyzed in the context of collision kinetic energy, diameter ratio, and relative position, with a view to capture the dynamic evolution of the vertical microstructure of rainfall in models and to interpret remote sensing measurements.

Significance Statement

Presently, uncertainty in precipitation estimation and prediction remains one of the grand challenges in water cycle studies. This work presents a detailed 3D simulator to characterize the evolution of drop size distributions (DSDs), without the space and functional constraints of laboratory experiments. The virtual microphysics laboratory (VML) is applied to replicate classical tower experiments from which parameterizations of precipitation processes used presently in weather and climate models and remote sensing algorithms were derived. The results presented demonstrate that the VML is a robust tool to capture DSD dynamics at the scale of individual raindrops (precipitation microphysics). VML will be used to characterize DSD dynamics across scales for environmental conditions and weather regimes for which no measurements are available.

Open access
C. Lyn Comer
,
Braedon Stouffer
,
David J. Stensrud
,
Yunji Zhang
, and
Matthew R. Kumjian

Abstract

Convective boundary layer (CBL) depth can be estimated from dual-polarization WSR-88D radars using the product differential reflectivity (ZDR ), because the CBL top is co-located with a local ZDR minimum produced by Bragg scatter at the interface of the CBL and the free troposphere. Quasi-vertical profiles (QVPs) of ZDR are produced for each radar volume scan and profiles from successive times are stitched together to form a time-height plot of ZDR from sunrise to sunset. QVPs of ZDR often show a low-ZDR channel that starts near the ground and rises during the morning and early afternoon, identifying the CBL top. Unfortunately, results show that this channel within the QVP can occasionally be misleading. This motivated creation of a new variable: DVar , which combines ZDR with its azimuthal variance and is particularly helpful at identifying the CBL top during the morning hours. Two methods are developed to track the CBL top from QVPs of ZDR and DVar . Although each method has strengths and weaknesses, the best results are found when the two methods are combined using inverse variance weighting. The ability to detect CBL depth from routine WSR-88D radar scans rather than from rawinsondes or lidar instruments would vastly improve our understanding of CBL depth variations in the daytime by increasing the temporal and spatial frequency of the observations.

Restricted access
Takuya Kurihana
,
Ilijana Mastilovic
,
Lijing Wang
,
Aurelien Meray
,
Satyarth Praveen
,
Zexuan Xu
,
Milad Memarzadeh
,
Alexander Lavin
, and
Haruko Wainwright

Abstract

The complexity of growing spatiotemporal resolution of climate simulations produces a variety of climate patterns under different projection scenarios. This paper proposes a new data-driven climate classification workflow via an unsupervised deep learning technique that can dimensionally reduce the vast volume of spatiotemporal numerical climate projection data into a compact representation. We aim to identify distinct zones that capture multiple climate variables as well as their future changes under different climate change scenarios. Our approach leverages convolutional autoencoders combined with k–means clustering (standard autoencoder) and online clustering based on the Sinkhorn-Knopp algorithm (clustering autoencoder) across the continental United States (CONUS) to capture unique climate patterns in a data-driven fashion from the Geophysical Fluid Dynamics Laboratory Earth System Model (GFDL-ESM2G). The developed approach compresses 70 years of GFDL-ESM2G simulation at 0.125° spatial resolution across the CONUS under multiple warming scenarios to a lower dimensional space by a factor of 660000, and then tested on 150 years of GFDL-ESM2G simulation data. The results show that five climate clusters capture physically reasonable and spatially stable climatological patterns matched to known climate classes defined by human experts. Results also show that using a clustering autoencoder can reduce the computational time for clustering by up to 9.2 times when compared to using a standard autoencoder. Our five unique climate patterns resulting from the deep learning-based clustering of the lower dimensional space thereby enable us to provide insights on hydrometeorology and its spatial heterogeneity across the continental US immediately without downloading large climate datasets.

Open access
Qinghua Ding
and
Hailan Wang

Abstract

This study aims to understand the underlying mechanism of large scale circulation control on atmospheric rivers (AR) and precipitation variability across the Contiguous United States (CONUS) in winter. The El Niño-Southern Oscillation (ENSO), known as a key driver of global circulation, has shown a modest impact on CONUS precipitation, prompting us to focus our attention on other climate drivers. Here, we find that barotropic instability over the exit region of the North Pacific subtropical jet stream plays a critical role in forming a downstream stationary Rossby wave train during winter (referred to as the West Mode). This wave pattern influences CONUS precipitation by affecting AR activity and explains approximately 50% of rainfall changes in the Western US, as well as numerous extreme wet and drought years along the West Coast, such as the wet winter in 2022/23. Over the past eight decades, the West Mode exhibited limited sensitivity to both Sea Surface Temperature (SST) and increasing anthropogenic forcing and was more influential in shaping interannual and interdecadal CONUS precipitation variability than ENSO. This result may explain why ENSO alone can only account for a limited portion of CONUS precipitation variability, thereby imposing an inherent constraint on the precision of seasonal predictions of CONUS precipitation made by climate models. Due to the significance of the West Mode in governing precipitation variability over the Western US, winter precipitation in that region may possess some resilience to the effects of global warming in the coming decades, as supported by large ensemble simulations driven by projected radiative forcing.

Restricted access
Siegfried D. Schubert
,
Yehui Chang
,
Anthony M. DeAngelis
,
Young-Kwon Lim
,
Natalie P. Thomas
,
Randal D. Koster
,
Michael G. Bosilovich
,
Andrea M. Molod
,
Allison Collow
, and
Amin Dezfuli

Abstract

In late December of 2022 and the first half of January 2023, an unprecedented series of atmospheric rivers (ARs) produced near-record heavy rains and flooding over much of California. Here, we employ the NASA GEOS AGCM run in a “replay” mode, together with more idealized simulations with a stationary wave model, to identify the remote forcing regions, mechanisms, and underlying predictability of this flooding event. In particular, the study addresses the underlying causes of a persistent positive Pacific–North American (PNA)-like circulation pattern that facilitated the development of the ARs. We show that the pattern developed in late December as a result of vorticity forcing in the North Pacific jet exit region. We further provide evidence that this vorticity forcing was the result of a chain of events initiated in mid-December with the development of a Rossby wave (as a result of forcing linked to the MJO) that propagated from the northern Indian Ocean into the North Pacific. As such, both the initiation of the event and the eventual development of the PNA depended critically on internally generated Rossby wave forcings, with the North Pacific jet playing a key role. This, combined with contemporaneous SST (La Niña) forcing that produced a circulation response in the AGCM that was essentially opposite to the positive PNA, underscores the fundamental lack of predictability of the event at seasonal time scales. Forecasts produced with the GEOS-coupled model suggest that useful skill in predicting the PNA and extreme precipitation over California was in fact limited to lead times shorter than about 3 weeks.

Restricted access
Chenyu Zheng
,
Shaojun Zheng
,
Ming Feng
,
Lingling Xie
,
Lei Wang
,
Tianyu Zhang
, and
Li Yan

Abstract

The East African Coastal Current (EACC) is an important western boundary current of the tropical South Indian Ocean and plays an important role in the ocean circulation and biogeochemical cycles in the Indian Ocean. This study investigates the interannual variability of the EACC and its dynamical mechanisms. The result shows that the EACC has interannual variability associated with the El Niño-Southern Oscillation (ENSO) during 1993-2017. The EACC shows a significantly positive correlation with the Niño3.4 index with a correlation coefficient of 0.65, lagging the Niño3.4 index by 18 months. During the decaying phases of El Niño (La Niña) events, the negative (positive) sea level anomaly (SLA) propagates westward as upwelling (downwelling) Rossby waves from the southeast Indian Ocean to the southwest Indian Ocean, and then strengthens (weakens) the EACC due to zonal SLA gradient off the East African coast under geostrophic equilibrium. The SLA gradually weakens in the southeast Indian Ocean during its westward propagation but strengthens in the southwest Indian Ocean promoted by local wind stress curl anomaly. This study can improve our understanding of the relationship between the western boundary current of the tropical South Indian Ocean and large-scale ENSO air-sea processes, and is important for managing marine fisheries and ecosystems on the East African coast.

Restricted access
Josep Bonsoms
,
Marc Oliva
,
Juan I. López-Moreno
, and
Xavier Fettweis

Abstract

The Greenland Ice Sheet (GrIS) meltwater runoff has increased considerably since the 1990s, leading to implications for the ice sheet mass balance and ecosystem dynamics in ice-free areas. Extreme weather events will likely continue to occur in the coming decades. Therefore, a more thorough understanding of the spatiotemporal patterns of extreme melting events is of interest. This study aims to analyze the evolution of extreme melting events acrossthe GrIS and determine the climatic factors that drive them. Specifically, we have analyzed extreme melting events (90th percentile) across the GrIS from 1950 to 2022 and examined their links to the surface energy balance (SEB) and large-scale atmospheric circulation. Extreme melting days account for approximately 35-40% of the total accumulated melting per season. We found that extreme melting frequency, intensity, and contribution to the total accumulated June, July and August (summer) melting show a statistically significant upward trend at a 95% confidence level. The largest trends are detected across the northern GrIS. The trends are independent of the extreme melting percentile rank (90th, 97th, or 99th) analyzed, and are consistent with average melting trends that exhibit an increase of similar magnitude and spatial configuration. Radiation plays a dominant role in controlling the SEB during extreme melting days. The increase in extreme melting frequency and intensity is driven by the increase of anticyclonic weather types during summer and more energy available for melting. Our results help to enhance the understanding of extreme events in the Arctic.

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