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Minghua Zheng
,
Ryan Torn
,
Luca Delle Monache
,
James Doyle
,
Fred Martin Ralph
,
Vijay Tallapragada
,
Christopher Davis
,
Daniel Steinhoff
,
Xingren Wu
,
Anna Wilson
,
Caroline Papadopoulos
, and
Patrick Mulrooney

Abstract

During a 6-day intensive observing period in January 2021, Atmospheric River Reconnaissance (AR Recon) aircraft sampled a series of atmospheric rivers (ARs) over the northeastern Pacific that caused heavy precipitation over coastal California and the Sierra Nevada. Using these observations, data denial experiments were conducted with a regional modeling and data assimilation system to explore the impacts of research flight frequency and spatial resolution of dropsondes on model analyses and forecasts. Results indicate that dropsondes significantly improve the representation of ARs in the model analyses and positively impact the forecast skill of ARs and quantitative precipitation forecasts (QPF), particularly for lead times > 1 day. Both reduced mission frequency and reduced dropsonde horizontal resolution degrade forecast skill. On the other hand, experiments that assimilated only G-IV data and experiments that assimilated both G-IV and C-130 data show better forecast skill than experiments that only assimilated C-130 data, suggesting that the additional information provided by G-IV data is necessary for improving forecast skill. Although this is a case study, the 6-day period studied encompassed multiple AR events that are representative of typical AR behavior. Therefore, the results indicate that future operational AR Recon missions incorporate daily mission or back-to-back flights, maintain current dropsonde spacing, support high-resolution data transfer capacity on the C-130s, and utilize G-IV aircraft in addition to C-130s.

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Clark Weaver
,
Dong L. Wu
,
P. K. Bhartia
,
Gordon Labow
,
David P. Haffner
,
Lauren Borgia
,
Laura McBride
, and
Ross Salawitch

Abstract

We construct a long-term record of Top of Atmosphere (TOA) shortwave (SW) albedo of clouds and aerosols from 340 nm radiances observed by NASA and NOAA satellite instruments from 1980 to 2013. We compare our SW cloud+aerosol albedo with simulated cloud albedo from both AMIP and historical CMIP6 simulations from 47 climate models. While most historical runs did not simulate our observed spatial pattern of the trends in albedo over the Pacific Ocean, four models qualitatively simulate our observed patterns. Those historical models and the AMIP models collectively estimate an Equilibrium Climate Sensitivity (ECS) of ∼3.5°C, with an uncertainty from 2.7 to 5.1°C. Our ECS estimates are sensitive to the instrument calibration which drives the wide range in ECS uncertainty. We use instrument calibrations that assume a neutral change in reflectivity over the Antarctic ice sheet. Our observations show increasing cloudiness over the eastern equatorial Pacific and off the coast of Peru as well as neutral cloud trends off the coast of Namibia and California.

To produce our SW cloud+aerosol albedo we first retrieve a Black-sky Cloud Albedo (BCA) and empirically correct the sampling bias from diurnal variations. Then we estimate the broadband proxy albedo using multiple non-linear regression along with several years of CERES cloud albedo to obtain the regression coefficients. We validate our product against CERES data from the years not used in the regression. Zonal mean trends of our SW cloud+aerosol albedo show reasonable agreement with CERES as well as the Extended Pathfinder Atmospheres (Patmos-x) observational dataset.

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Adrien Pierre
,
Daniel F. Nadeau
,
Antoine Thiboult
,
Alain N. Rousseau
,
François Anctil
,
Charles P. Deblois
,
Maud Demarty
,
Pierre-Erik Isabelle
, and
Alain Tremblay

Abstract

The hydrological processes of cascading hydroelectric reservoirs differ from those of lakes, due to the importance of the inflows and outflows that vary with energy demand. These heat and water advection terms are rarely considered in water body energy balance analyses even though reservoirs are common man-made structures, especially in North America, and thus may affect the regional climate. This study provides a comprehensive assessment of the water and energy balance of the 85-km2 Romaine-2 northern reservoir (50.69°N, 63.24°W), mean depth of 44 m, highlighting the significant contribution of the advection heat fluxes. The water balance input was primarily controlled by upstream (turbine) inflows (77.6%), while lateral (natural) inflows and direct precipitation represented 21.2% and 1.2%, respectively. As for the reservoir’s heat budget, the net advection of heat accounted on average for 25.0% of the input, of which net radiation was the largest component (73.3%). After accounting for the absence of energy balance closure, latent heat and sensible heat fluxes represented 73.2% and 25.1% of total energy output from the reservoir, respectively. The thermal regime was influenced by the hydrological flow conditions, which were regulated by reservoir management. This played a major role in the evolution of the thermocline and the temperature of the epilimnion, and ultimately, in the dynamics of the turbulent heat fluxes. This study suggests that the heat advection term represents a large fraction of the heat budget of northern reservoirs and should be properly considered.

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Zhiwei Zhen
,
Huikyo Lee
,
Ignacio Segovia-Dominguez
,
Meichen Huang
,
Yuzhou Chen
,
Michael Garay
,
Daniel Crichton
, and
Yulia R. Gel

Abstract

Virtually all aspects of our societal functioning—from food security to energy supply to healthcare—depend on the dynamics of environmental factors. Nevertheless, the social dimensions of weather and climate are noticeably less explored by the artificial intelligence community. By harnessing the strength of geometric deep learning (GDL), we aim to investigate the pressing societal question the potential disproportional impacts of air quality on COVID-19 clinical severity. To quantify air pollution levels, here we use aerosol optical depth (AOD) records that measure the reduction of the sunlight due to atmospheric haze, dust, and smoke. We also introduce unique and not yet broadly available NASA satellite records (NASAdat) on AOD, temperature, and relative humidity and discuss the utility of these new data for biosurveillance and climate justice applications, with a specific focus on COVID-19 in the states of Texas and Pennsylvania. The results indicate, in general, that the poorer air quality tends to be associated with higher rates for clinical severity and, in the case of Texas, that this phenomenon particularly stands out in Texan counties characterized by higher socioeconomic vulnerability. This, in turn, raises a concern of environmental injustice in these socioeconomically disadvantaged communities. Furthermore, given that one of NASA’s recent long-term commitments is to address such inequitable burden of environmental harm by expanding the use of Earth science data such as NASAdat, this project is one of the first steps toward developing a new platform integrating NASA’s satellite observations with deep learning (DL) tools for social good.

Significance Statement

By leveraging the strengths of modern deep learning models, particularly, graph neural networks to describe complex spatiotemporal dependencies and by introducing new NASA satellite records, this study aims to investigate the problem of potential environmental injustice associated with COVID-19 clinical severity and caused by disproportional impacts of poor air quality on disadvantaged socioeconomic populations.

Open access
Haochen Tan
,
Rao Kotamarthi
, and
Pallav Ray

Abstract

The surface sensible heat flux induced by precipitation (QP ) is a consequence of the temperature difference between the surface and the rain droplets. Despite its seemingly negligible nature, QP is frequently omitted from both meteorological and climatological models. Nevertheless, it is important to acknowledge the numerous occasions in which the instantaneous values of QP can be significant, particularly during extreme precipitation events. This study undertakes a comprehensive assessment of QP across the contiguous United States (CONUS) utilizing high-resolution reanalysis, observational data, and numerical modeling to examine the influence of QP on precipitation and the surface energy budget. The findings indicate that the spatial distribution of QP climatology is analogous to that of precipitation, with magnitudes ranging from 2 to 3 W m−2 predominantly over the Midwest and Southeast regions. A seasonal analysis of QP reveals that the highest values occurring during the June–August (JJA) period, averaging 3.18 W m−2. Peak QP values of approximately 4 W m−2 are observed during JJA over the Great Plains region. We hypothesize that the QP during an extreme precipitation event would be nonnegligible and have a significant impact on the local weather. To test this conjecture, we perform high-resolution simulations with and without QP during an extreme precipitation event over the Chicago Metropolitan Area (CMA). The results show that the QP may be a dominant factor compared to other components of surface heat flux during the zenith of precipitation hours. Also, QP has the potential to not only diminish precipitation but also alter and reconfigure the remaining surface energy budget components.

Open access
Alex J. Cannon

Abstract

Canadian climate service providers offer projections from the Coupled Model Intercomparison Project (CMIP6) to help inform climate change mitigation and adaptation decisions. CMIP6 includes several “hot” climate models whose sensitivity to greenhouse gas forcings exceeds the likely range inferred from multiple lines of evidence. Global warming estimates assessed in the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) were reduced by applying observational constraints on the historical rate of warming to the CMIP6 ensemble. This study assesses whether globally constrained CMIP6 projections for Canada are appreciably different from unconstrained projections. Two constraints are considered: one that removes models whose transient climate response lies outside the AR6 assessed range (TCRlikely), and the other that weights models to match the assessed distribution of equilibrium climate sensitivity (ECSall). Both constraints lead to appreciably cooler and drier projections than the unconstrained ensemble, with the strongest reductions seen in the upper end of the ensemble range, high-emissions scenario, end-of-century time period, and northern regions of Canada. In this case, constrained projections of annual mean temperature are 2°–3°C cooler than the unconstrained projections, whereas projections of annual total precipitation are typically 20%–40% drier. Appreciable differences are also detected in the ensemble median of temperature extreme indices. Based on these results, it is recommended that a constrained ensemble be considered for regional projections to avoid the “hot model” problem. Alternatively, projections can be communicated conditional on a specified level of global warming, with global constraints then used to inform the timing of the warming level exceedance.

Open access
Ning Yang
,
Debin Su
,
Luyao Sun
, and
Tao Wang

Abstract

Atmospheric ducting is a highly refractive propagation condition that frequently occurs at sea and significantly impacts radar and communication equipment. This paper analyzes the spatiotemporal distribution of Lower Atmospheric Ducts (LAD) in the South China Sea (SCS) and the variation of their occurrence rate with the monsoon by using reanalysis data from the ECMWF from 1980 to 2022. Additionally, the study discusses the relationship between ducting occurrences and atmospheric and oceanic conditions. The results indicate that wind dynamics in the SCS significantly impact ducting incidents. During the high-incidence period of LAD, humidity gradient-constructed ducts are the primary mechanism. Before the onset of the monsoon, the mountains in the western part of Luzon Island obstruct the easterly wind, resulting in high temperatures and strong evaporation along the western coast of the mountains. Meanwhile, low temperatures and humidity prevail in the eastern part of the mountains, it leads to a stratified atmosphere characterized by dry and cold upper layers and warm and humid lower layers in the western part of Luzon Island, which causes a distinct decrease in humidity with height. After the onset of the monsoon, the air from the Indochina Peninsula to the ocean is dry and cold, but the high-altitude area blocks it. This weakens the horizontal mobility of the low-level humid atmosphere over the sea, resulting in atmospheric stratification in the eastern coastal area of the Indochina Peninsula. This stratification leads to dry and cold upper layers and warm and humid lower layers.

Restricted access
Paul A. Sanders
,
Martijn D. Dorrestijn
, and
Theo Gerkema

Abstract

The along-slope propagation of subinertial trapped internal tides is studied for the configuration of a simple step. It is revealed that they form a beam structure in the along-slope direction that is evanescent above the top of the step; these beams lack strict periodicity in the along-slope direction. As in classical internal Kelvin waves, they become less sharp away from the step, as higher modes decay more rapidly in the cross-slope direction. We discuss implications for abyssal mixing and outline the necessary ingredients for their generation.

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Zied Ben Bouallègue
,
Mariana C A Clare
,
Linus Magnusson
,
Estibaliz Gascón
,
Michael Maier-Gerber
,
Martin Janoušek
,
Mark Rodwell
,
Florian Pinault
,
Jesper S Dramsch
,
Simon T K Lang
,
Baudouin Raoult
,
Florence Rabier
,
Matthieu Chevallier
,
Irina Sandu
,
Peter Dueben
,
Matthew Chantry
, and
Florian Pappenberger

Abstract

Data-driven modeling based on machine learning (ML) is showing enormous potential for weather forecasting. Rapid progress has been made with impressive results for some applications. The uptake of ML methods could be a game-changer for the incremental progress in traditional numerical weather prediction (NWP) known as the “quiet revolution” of weather forecasting. The computational cost of running a forecast with standard NWP systems greatly hinders the improvements that can be made from increasing model resolution and ensemble sizes. An emerging new generation of ML models, developed using high-quality reanalysis datasets like ERA5 for training, allow forecasts that require much lower computational costs and that are highly-competitive in terms of accuracy. Here, we compare for the first time ML-generated forecasts with standard NWP-based forecasts in an operational-like context, initialized from the same initial conditions. Focusing on deterministic forecasts, we apply common forecast verification tools to assess to what extent a data-driven forecast produced with one of the recently developed ML models (PanguWeather) matches the quality and attributes of a forecast from one of the leading global NWP systems (the ECMWF IFS). The results are very promising, with comparable accuracy for both global metrics and extreme events, when verified against both the operational IFS analysis and synoptic observations. Overly smooth forecasts, increasing bias with forecast lead time, and poor performance in predicting tropical cyclone intensity are identified as current drawbacks of ML-based forecasts. A new NWP paradigm is emerging relying on inference from ML models and state-of-the-art analysis and reanalysis datasets for forecast initialization and model training.

Open access
Soumik Ghosh
,
Orli Lachmy
, and
Yohai Kaspi

Abstract

Climate models generally predict a poleward shift of the midlatitude circulation in response to climate change induced by increased greenhouse gas concentration, but the inter-model spread of the eddy-driven jet shift is large and poorly understood. Recent studies point to the significance of midlatitude mid-tropospheric diabatic heating for the inter-model spread in the jet latitude. To examine the role of diabatic heating in the jet response to climate change, a series of simulations are performed using an idealized aquaplanet model. It is found that both increased CO2 concentration and increased saturation vapor pressure induce a similar warming response, leading to a poleward and upward shift of the midlatitude circulation. An exception to this poleward shift is found for a certain range of temperatures, where the eddy-driven jet shifts equatorward, while the latitude of the eddy heat flux remains essentially unchanged. This equatorward jet shift is explained by the connection between the zonal mean momentum and heat budgets: increased diabatic heating in the midlatitude mid-troposphere balances the cooling by the Ferrel cell ascending branch, enabling an equatorward shift of the Ferrel cell streamfunction and eddy-driven jet, while the latitude of the eddy heat flux remains unchanged. The equatorward jet shift and the strengthening of the midlatitude diabatic heating are found to be sensitive to the model resolution. The implications of these results for a potential reduction in the jet shift uncertainty through the improvement of convective parameterizations are discussed.

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