skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Global Distributions of Tropospheric and Stratospheric Gravity Wave Momentum Fluxes Resolved by the 9-km ECMWF Experiments
Abstract Based on 20-day control forecasts by the 9-km Integrated Forecasting System (IFS) at the European Centre for Medium-Range Weather Forecasts (ECMWF) for selected periods of summer and winter events, this study investigates global distributions of gravity wave momentum fluxes resolved by the highest-resolution-ever global operational numerical weather prediction model. Two supplementary datasets, including 18-km ECMWF IFS experiments and the 30-km ERA5, are included for comparison. In the stratosphere, there is a clear dominance of westward momentum fluxes over the winter extratropics with strong baroclinic instability, while eastward momentum fluxes are found in the summer tropics. However, meridional momentum fluxes, locally as important as the above zonal counterpart, show different behaviors of global distribution characteristics, with northward and southward momentum fluxes alternating with each other especially at lower altitudes. Both events illustrate conclusive evidence that stronger stratospheric fluxes are found in the ECMWF forecast with finer resolution, and that ERA5 datasets have the weakest signals in general, regardless of whether regridding is applied. In the troposphere, probability distributions of vertical motion perturbations are highly asymmetric with more strong positive signals especially over latitudes covering heavy rainfall, likely caused by convective forcing. With the aid of precipitation accumulation, a simple filtering method is proposed in an attempt to eliminate those tropospheric asymmetries by convective forcing, before calculating tropospheric wave-induced fluxes. Furthermore, this research demonstrates promising findings that the proposed filtering method could help in reducing the potential uncertainties with respect to estimating tropospheric wave-induced fluxes. Finally, absolute momentum flux distributions with proposed approaches are presented, for further assessment in the future.  more » « less
Award ID(s):
1829373
PAR ID:
10380867
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Journal of the Atmospheric Sciences
Volume:
79
Issue:
10
ISSN:
0022-4928
Page Range / eLocation ID:
2621 to 2644
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract Pure artificial intelligence (AI)-based weather prediction (AIWP) models have made waves within the scientific community and the media, claiming superior performance to numerical weather prediction (NWP) models. However, these models often lack impactful output variables such as precipitation. One exception is Google DeepMind’s GraphCast model, which became the first mainstream AIWP model to predict precipitation, but performed only limited verification. We present an analysis of the ECMWF’s Integrated Forecasting System (IFS)-initialized (GRAPIFS) and the NCEP’s Global Forecast System (GFS)-initialized (GRAPGFS) GraphCast precipitation forecasts over the contiguous United States and compare to results from the GFS and IFS models using 1) grid-based, 2) neighborhood, and 3) object-oriented metrics verified against the fifth major global reanalysis produced by ECMWF (ERA5) and the NCEP/Environmental Modeling Center (EMC) stage IV precipitation analysis datasets. We affirmed that GRAPGFSand GRAPIFSperform better than the GFS and IFS in terms of root-mean-square error and stable equitable errors in probability space, but the GFS and IFS precipitation distributions more closely align with the ERA5 and stage IV distributions. Equitable threat score also generally favored GraphCast, particularly for lower accumulation thresholds. Fractions skill score for increasing neighborhood sizes shows greater gains for the GFS and IFS than GraphCast, suggesting the NWP models may have a better handle on intensity but struggle with the location. Object-based verification for GraphCast found positive area biases at low accumulation thresholds and large negative biases at high accumulation thresholds. GRAPGFSsaw similar performance gains to GRAPIFSwhen compared to their NWP counterparts, but initializing with the less familiar GFS conditions appeared to lead to an increase in light precipitation. Significance StatementPure artificial intelligence (AI)-based weather prediction (AIWP) has exploded in popularity with promises of better performance and faster run times than numerical weather prediction (NWP) models. However, less attention has been paid to their capability to predict impactful, sensible weather like precipitation, precipitation type, or specific meteorological features. We seek to address this gap by comparing precipitation forecast performance by an AI model called GraphCast to the Global Forecast System (GFS) and the Integrated Forecasting System (IFS) NWP models. While GraphCast does perform better on many verification metrics, it has some limitations for intense precipitation forecasts. In particular, it less frequently predicts intense precipitation events than the GFS or IFS. Overall, this article emphasizes the promise of AIWP while at the same time stresses the need for robust verification by domain experts. 
    more » « less
  2. null (Ed.)
    Abstract. This study quantifies differences among four widely usedatmospheric reanalysis datasets (ERA5, JRA-55, MERRA-2, and CFSR) in theirrepresentation of the dynamical changes induced by springtime polarstratospheric ozone depletion in the Southern Hemisphere from 1980 to 2001.The intercomparison is undertaken as part of the SPARC(Stratosphere–troposphere Processes and their Role in Climate) ReanalysisIntercomparison Project (S-RIP). The reanalyses are generally in goodagreement in their representation of the strengthening of the lowerstratospheric polar vortex during the austral spring–summer season,associated with reduced radiative heating due to ozone loss, as well as thedescent of anomalously strong westerly winds into the troposphere duringsummer and the subsequent poleward displacement and intensification of thepolar front jet. Differences in the trends in zonal wind between thereanalyses are generally small compared to the mean trends. The exception isCFSR, which exhibits greater disagreement compared to the other threereanalysis datasets, with stronger westerly winds in the lower stratospherein spring and a larger poleward displacement of the tropospheric westerlyjet in summer. The dynamical changes associated with the ozone hole are examined byinvestigating the momentum budget and then the eddy heat and momentumfluxes in terms of planetary- and synoptic-scale Rossby wave contributions.The dynamical changes are consistently represented across the reanalysesand support our dynamical understanding of the response of the coupledstratosphere–troposphere system to the ozone hole. Although our resultssuggest a high degree of consistency across the four reanalysis datasets inthe representation of these dynamical changes, there are larger differencesin the wave forcing, residual circulation, and eddy propagation changes compared to the zonal wind trends. In particular, there is a noticeabledisparity in these trends in CFSR compared to the other three reanalyses,while the best agreement is found between ERA5 and JRA-55. Greateruncertainty in the components of the momentum budget, as opposed to meancirculation, suggests that the zonal wind is better constrained by theassimilation of observations compared to the wave forcing, residualcirculation, and eddy momentum and heat fluxes, which are more dependent onthe model-based forecasts that can differ between reanalyses. Lookingforward, however, these findings give us confidence that reanalysis datasetscan be used to assess changes associated with the ongoing recovery ofstratospheric ozone. 
    more » « less
  3. Abstract Clouds and radiation play an important role in warming events over the Southern Ocean (SO). Here we evaluate European Center for Medium‐Range Weather Forecasts Reanalysis version 5 (ERA5) and Polar Weather Research Forecast (PWRF) output through comparison to surface‐based measurements of clouds, radiation, and the atmospheric state over the SO during 2017–2023 at Escudero Station (62.2°S, 58.97°W) on King George Island. ERA5 mean monthly downward shortwave (DSW) radiative fluxes are found to be 38–50 W m−2higher than observations in summer, whereas ERA5 mean monthly downward longwave (DLW) is biased by −18 to −22 W m−2in summer and −16 W m−2on average over the year. Comparisons of temperature, humidity, and lowest‐cloud base heights between ERA5 and observations rule these factors out as large contributors to the DLW flux biases. The similarity between observed DLW cloud forcing distributions for atmospheric columns containing low‐level liquid and ice‐only clouds suggests limited influence of cloud phase errors on DLW biases. Thus the most likely explanation for DLW flux biases in ERA5 is underestimated cloud optical depth, which is also consistent with DSW flux biases. Similar biases in ERA5 are found during atmospheric river (AR) events. By contrast, PWRF flux bias magnitudes are much smaller during AR events (−12 W m−2for DSW and −2 W m−2for DLW). After bias correction, ERA5 monthly average net cloud forcing over 2017–2023 is found to be a minimum of −107 W m−2in January and a maximum of 65 W m−2in June. 
    more » « less
  4. Abstract A Lagrangian snow‐evolution model (SnowModel‐LG) was used to produce daily, pan‐Arctic, snow‐on‐sea‐ice, snow property distributions on a 25 × 25‐km grid, from 1 August 1980 through 31 July 2018 (38 years). The model was forced with NASA's Modern Era Retrospective‐Analysis for Research and Applications‐Version 2 (MERRA‐2) and European Centre for Medium‐Range Weather Forecasts (ECMWF) ReAnalysis‐5th Generation (ERA5) atmospheric reanalyses, and National Snow and Ice Data Center (NSIDC) sea ice parcel concentration and trajectory data sets (approximately 61,000, 14 × 14‐km parcels). The simulations performed full surface and internal energy and mass balances within a multilayer snowpack evolution system. Processes and features accounted for included rainfall, snowfall, sublimation from static‐surfaces and blowing‐snow, snow melt, snow density evolution, snow temperature profiles, energy and mass transfers within the snowpack, superimposed ice, and ice dynamics. The simulations produced horizontal snow spatial structures that likely exist in the natural system but have not been revealed in previous studies spanning these spatial and temporal domains. Blowing‐snow sublimation made a significant contribution to the snowpack mass budget. The superimposed ice layer was minimal and decreased over the last four decades. Snow carryover to the next accumulation season was minimal and sensitive to the melt‐season atmospheric forcing (e.g., the average summer melt period was 3 weeks or 50% longer with ERA5 forcing than MERRA‐2 forcing). Observed ice dynamics controlled the ice parcel age (in days), and ice age exerted a first‐order control on snow property evolution. 
    more » « less
  5. Abstract The Arctic region is experiencing significant changes due to climate change, and the resulting decline in sea ice concentration and extent is already impacting ocean dynamics and exacerbating coastal hazards in the region. In this context, numerical models play a crucial role in simulating the interactions between the ocean, land, sea ice, and atmosphere, thus supporting scientific studies in the region. This research aims to evaluate how different sea ice products with spatial resolutions varying from 2 to 25 km influence a phase averaged spectral wave model results in the Alaskan Arctic under storm conditions. Four events throughout the Fall to Winter seasons in 2019 were utilized to assess the accuracy of wave simulations generated under the dynamic sea ice conditions found in the Arctic. The selected sea ice products used to parameterize the numerical wave model include the National Snow and Ice Data Center (NSIDC) sea ice concentration, the European Centre for Medium‐Range Weather Forecasts (ECMWF) Re‐Analysis (ERA5), the HYbrid Coordinate Ocean Model‐Community Ice CodE (HYCOM‐CICE) system assimilated with Navy Coupled Ocean Data Assimilation (NCODA), and the High‐resolution Ice‐Ocean Modeling and Assimilation System (HIOMAS). The Simulating WAves Nearshore (SWAN) model's accuracy in simulating waves using these sea ice products was evaluated against Sea State Daily Multisensor L3 satellite observations. Results show wave simulations using ERA5 consistently exhibited high correlation with observations, maintaining an accuracy above 0.83 to the observations across all events. Conversely, HIOMAS demonstrated the weakest performance, particularly during the Winter, with the lowest correlation of 0.40 to the observations. Remarkably, ERA5 surpassed all other products by up to 30% in accuracy during the selected storm events, and even when an ensemble was assessed by combining the selected sea ice products, ERA5's individual performance remained unmatched. Our study provides insights for selecting sea ice products under different sea ice conditions for accurately simulating waves and coastal hazards in high latitudes. 
    more » « less