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1. Is our dynamical understanding of the circulation changes associated with the Antarctic ozone hole sensitive to the choice of reanalysis dataset?

   https://doi.org/10.5194/acp-21-7451-2021  

   Orr, Andrew ; Lu, Hua ; Martineau, Patrick ; Gerber, Edwin P. ; Marshall, Gareth J. ; Bracegirdle, Thomas J. ( January 2021 , Atmospheric Chemistry and Physics)

   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. 

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2. The Tropical Easterly Jet over Africa, its representation in six reanalysis products, and its association with Sahel rainfall

   https://doi.org/10.1002/joc.6623  

   Nicholson, Sharon E. ; Klotter, Douglas ( June 2020 , International Journal of Climatology)

   This paper compares the characteristics of the Tropical Easterly Jet (TEJ) and upper‐level winds in six reanalysis products, compares them with soundings at seven West African locations, examines the relationship between Sahel rainfall and the TEJ, and examines factors influencing the TEJ. The jet characteristics assessed by MERRA2, NCEP 1, JRA 55, and ERA 5 are similar. CFSR and 20th Century Reanalysis are outliers in nearly every analysis, overestimating wind speeds by as much as 25 to 40% compared to other reanalyses. Over the period 1948 to 2014, the correlation between rainfall and TEJ magnitude is .72. Arguments based on observations and modelling studies provide evidence that on interannual scales changes in the TEJ are not forced by rainfall, that large‐scale factors drive the TEJ. Potential mechanisms are discussed for a causal relationship such that a strong jet leads to high rainfall. However, further modelling efforts are needed to conclusively determine whether the TEJ/Sahel rainfall link is a result of common forcing factors. The factors that appear to control jet strength include sea‐surface temperature (SST) contrast between the central equatorial Pacific and central equatorial Indian Ocean (correlation of −.64), SST contrast between the central equatorial and the southern subtropical Indian Ocean (correlation of −.39), the latitude of the shift between upper‐tropospheric easterlies and westerlies in the Southern Hemisphere (correlation of −.84 at 150 hPa), and the intensity of the Southern Hemisphere westerlies (correlation of +.52 at 200 hPa). This suggests considerable control on the TEJ by extra‐tropical circulation in the Southern Hemisphere.

    

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3. Internal atmospheric variability of net surface heat flux in reanalyses and CMIP5 AMIP simulations

   https://doi.org/10.1002/joc.7232  

   Chen, Hua ; Schneider, Edwin K. ; Zhu, Zhiwei ( June 2021 , International Journal of Climatology)

   The internal atmospheric variability (IAV) of the net surface heat flux (NHF) in the observed 20th/21st century atmosphere is estimated as the residual after removing the sea surface temperature (SST) and externally forced atmospheric response derived from the four atmospheric models of the Atmospheric Model Intercomparison Project (AMIP) simulations under phase 5 of the Coupled Model Intercomparison Project (CMIP5). The mean NHF of four atmospheric reanalysis datasets is an estimate of the observed NHF. Although the AMIP models are forced with the same SST and external forcing, the forced responses differ significantly among AMIP models, suggestive of uncertainty in the models. Besides, the uncertainty of IAV in the reanalyses could also arise from the uncertainty in reanalyses as observations contain errors and reanalysis includes interpolation by models. It is concluded that: (a) The SST/NHF and SST/forced NHF correlations are significantly negative over most of world ocean in the AMIP models, indicating damping of the SST anomalies by the NHF. (b) The IAV of the AMIP models is not correlated with SST, while the positive IAV/SST correlations in the reanalyses suggests the role of IAV in forcing the SST variability in the extra‐tropics. (c) The standard deviation (STD) of the IAV of AMIP models is indistinguishable from that of the mean reanalysis over a majority of world ocean, and the STD of the NHF of the AMIP models is larger than that of the mean reanalysis in the subtropics and midlatitudes. (d) The IAV in the mean reanalysis plays a role in forcing the SST variability in the extra‐tropics (e.g., Atlantic Multidecadal Variability), while it may not be an important forcing in the tropical oceans (e.g., ENSO).

    

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4. Wind Stress, Wind Stress Curl, and Upwelling Velocities in the Northwest Atlantic (80-45W, 30-45N) during 1980-2019

   https://doi.org/10.5281/zenodo.8200832  

   Gifford, Ian ; Gangopadhyay, Avijit ; Andres, Magdalena ; Gawarkiewicz, Glen ; Oliver, Hilde ; Silver, Adrienne ( January 2023 , Zenodo)

   This dataset contains three netcdf files that pertain to monthly, seasonal, and annual fields of surface wind stress, wind stress curl, and curl-derived upwelling velocities over the Northwest Atlantic (80-45W, 30-45N) covering a forty year period from 1980 to 2019. Six-hourly surface (10 m) wind speed components from the Japanese 55-year reanalysis (JRA-55; Kobayashi et al., 2015) were processed from 1980 to 2019 over a larger North Atlantic domain of 100W to 10E and 10N to 80N. Wind stress was computed using a modified step-wise formulation, originally based on (Gill, 1982) and a non-linear drag coefficient (Large and Pond, 1981), and later modified for low speeds (Trenberth et al., 1989). See Gifford (2023) for more details.   

   After the six-hourly zonal and meridional wind stresses were calculated, the zonal change in meridional stress (curlx) and the negative meridional change in zonal stress (curly) were found using NumPy’s gradient function in Python (Harris et al., 2020) over the larger North Atlantic domain (100W-10E, 10-80N). The curl (curlx + curly) over the study domain (80-45W, 10-80N) is then extracted, which maintain a constant order of computational accuracy in the interior and along the boundaries for the smaller domain in a centered-difference gradient calculation. 

   The monthly averages of the 6-hour daily stresses and curls were then computed using the command line suite climate data operators (CDO, Schulzweida, 2022) monmean function. The seasonal (3-month average) and annual averages (12-month average) were calculated in Python using the monthly fields with NumPy (NumPy, Harris et al., 2020). 

   Corresponding upwelling velocities at different time-scales were obtained from the respective curl fields and zonal wind stress by using the Ekman pumping equation of the study by Risien and Chelton (2008; page 2393). Please see Gifford (2023) for more details.   

   The files each contain nine variables that include longitude, latitude, time, zonal wind stress, meridional wind stress, zonal change in meridional wind stress (curlx), the negative meridional change in zonal wind stress (curly), total curl, and upwelling. Units of time begin in 1980 and are months, seasons (JFM etc.), and years to 2019. The longitude variable extends from 80W to 45W and latitude is 30N to 45N with uniform 1.25 degree resolution.  

   Units of stress are in Pascals, units of curl are in Pascals per meter, and upwelling velocity is described by centimeters per day. The spatial grid is a 29 x 13 longitude x latitude array. 

   Filenames: 

   monthly_windstress_wsc_upwelling.nc: 480 time steps from 80W to 45W and 30N to 45N.

   seasonal_windstress_wsc_upwelling.nc: 160 time steps from 80W to 45W and 30N to 45N.

   annual_windstress_wsc_upwelling.nc: 40 time steps from 80W to 45W and 30N to 45N.

   Please contact igifford@earth.miami.edu for any queries. {"references": ["Gifford, I.H., 2023. The Synchronicity of the Gulf Stream Free Jet and the Wind Induced Cyclonic Vorticity Pool. MS Thesis, University of Massachusetts Dartmouth. 75pp.", "Gill, A. E. (1982). Atmosphere-ocean dynamics (Vol. 30). Academic Press.", "Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357\u2013362 (2020). DOI: 10.1038/s41586-020-2649-2.", "Japan Meteorological Agency/Japan (2013), JRA-55: Japanese 55-year Reanalysis, Daily 3-Hourly and 6-Hourly Data, https://doi.org/10.5065/D6HH6H41, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, Colo. (Updated monthly.)", "Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H. and Miyaoka, K., 2015. The JRA-55 reanalysis: General specifications and basic characteristics.\u202fJournal of the Meteorological Society of Japan. Ser. II,\u202f93(1), pp.5-48.", "Large, W.G. and Pond, S., 1981. Open ocean momentum flux measurements in moderate to strong winds.\u202fJournal of physical oceanography,\u202f11(3), pp.324-336.", "Risien, C.M. and Chelton, D.B., 2008. A global climatology of surface wind and wind stress fields from eight years of QuikSCAT scatterometer data.\u202fJournal of Physical Oceanography,\u202f38(11), pp.2379-2413.", "Schulzweida, Uwe. (2022). CDO User Guide (2.1.0). Zenodo. https://doi.org/10.5281/zenodo.7112925.", "Trenberth, K.E., Large, W.G. and Olson, J.G., 1989. The effective drag coefficient for evaluating wind stress over the oceans.\u202fJournal of Climate,\u202f2(12), pp.1507-1516."]} 

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5. Can current reanalyses accurately portray changes in Southern Annular Mode structure prior to 1979?

   https://doi.org/10.1007/s00382-022-06292-3  

   Marshall, Gareth J. ; Fogt, Ryan L. ; Turner, John ; Clem, Kyle R. ( April 2022 , Climate Dynamics)

   Early reanalyses are less than optimal for investigating the regional effects of ozone depletion on Southern Hemisphere (SH) high-latitude climate because the availability of satellite sounder data from 1979 significantly improved their accuracy in data sparse regions, leading to a coincident inhomogeneity. To determine whether current reanalyses are better at SH high-latitudes in the pre-satellite era, here we examine the capabilities of the European Centre for Medium-range Weather Forecasts (ECMWF) fifth generation reanalysis (ERA5), the Twentieth Century Reanalysis version 3 (20CRv3), and the Japanese Meteorological Agency (JMA) 55-year reanalysis (JRA-55) to reproduce and help explain the pronounced change in the relationship between the Southern Annular Mode (SAM) and Antarctic near-surface air temperatures (SAT) between 1950 and 1979 (EARLY period) and 1980–2020 (LATE period). We find that ERA5 best reproduces Antarctic SAT in the EARLY period and is also the most homogeneous reanalysis across the EARLY and LATE periods. ERA5 and 20CRv3 provide a good representation of SAM in both periods with JRA-55 only similarly skilful in the LATE period. Nevertheless, all three reanalyses show the marked change in Antarctic SAM-SAT relationships between the two periods. In particular, ERA5 and 20CRv3 demonstrate the observed switch in the sign of the SAM-SAT relationship in the Antarctic Peninsula: analysis of changes in SAM structure and associated meridional wind anomalies reveal that in these reanalyses positive SAM is linked to cold southerly winds during the EARLY period and warm northerly winds in the LATE period, thus providing a simple explanation for the regional SAM-SAT relationship reversal.

    

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