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1. A Global Perspective of Tropical Cyclone Precipitation in Reanalyses

   https://doi.org/10.1175/JCLI-D-20-0892.1  

   Jones, Evan; Wing, Allison A.; Parfitt, Rhys (November 2021, Journal of Climate)

   Abstract This study compares the spread in climatological tropical cyclone (TC) precipitation across eight different reanalysis datasets: NCEP-CFSR, ERA-20C, ERA-40, ERA5, ERA-Interim, JRA-55, MERRA-2, and NOAA-20C. TC precipitation is assigned using manual tracking via a fixed 500-km radius from each TC center. The reanalyses capture similar general spatial patterns of TC precipitation and TC precipitation fraction, defined as the fraction of annual precipitation assigned to TCs, and the spread in TC precipitation is larger than the spread in total precipitation across reanalyses. The spread in TC precipitation relative to the inter-reanalysis mean TC precipitation, or relative spread, is larger in the east Pacific than in the west Pacific. Partitioned by reanalysis intensity, the largest relative spread across reanalyses in TC precipitation is from high-intensity TCs. In comparison with satellite observations, reanalyses show lower climatological mean annual TC precipitation over most areas. A comparison of area-averaged precipitation rate in TCs composited over reanalysis intensity shows the spread across reanalyses is larger for higher intensity TCs. Testing the sensitivity of TC precipitation assignment to tracking method shows that climatological mean annual TC precipitation is systematically larger when assigned via manual tracking versus objective tracking. However, this tendency is minimized when TC precipitation is normalized by TC density. Overall, TC precipitation in reanalyses is affected by not only horizontal output resolution or any TC preprocessing, but also data assimilation and parameterization schemes. The results indicate that improvements in the representation of TCs and their precipitation in reanalyses are needed to improve overall precipitation. 

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2. Advancing seasonal prediction of tropical cyclone activity with a hybrid ai-physics climate model

   https://doi.org/10.1088/1748-9326/adf864  

   Zhang, Gan; Rao, Megha; Yuval, Janni; Zhao, Ming (August 2025, Environmental Research Letters)

   Abstract Machine learning (ML) models are successful with weather forecasting and have shown progress in climate simulations, yet leveraging them for useful climate predictions needs exploration. Here we show this feasibility using Neural General Circulation Model (NeuralGCM), a hybrid ML-physics atmospheric model developed by Google, for seasonal predictions of large-scale atmospheric variability and Northern Hemisphere tropical cyclone (TC) activity. Inspired by physical model studies, we simplify boundary conditions, assuming sea surface temperature (SST) and sea ice follow their climatological cycle but persist anomalies present at the initialization time. With such forcings, NeuralGCM can generate 100 simulation days in ~8 minutes with a single Graphics Processing Unit (GPU), while simulating realistic atmospheric circulation and TC climatology patterns. This configuration yields useful seasonal predictions (July–November) for the tropical atmosphere and various TC activity metrics. Notably, the predicted and observed TC frequency in the North Atlantic and East Pacific basins are significantly correlated during 1990–2023 (r=~0.7), suggesting prediction skill comparable to existing physical GCMs. Despite challenges associated with model resolution and simplified boundary forcings, the model-predicted interannual variations demonstrate significant correlations with the observation, including the sub-basin TC tracks (p<0.1) and basin-wide accumulated cyclone energy (p<0.01) of the North Atlantic and North Pacific basins. These findings highlight the promise of leveraging ML models with physical insights to model TC risks and deliver seamless weather-climate predictions. 

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3. 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)

   Abstract 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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4. 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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5. How Accurate Are Modern Atmospheric Reanalyses for the Data-Sparse Tibetan Plateau Region?

   https://doi.org/10.1175/JCLI-D-18-0705.1  

   Bao, Xinghua; Zhang, Fuqing (September 2019, Journal of Climate)

   Abstract More than 6000 independent radiosonde observations from three major Tibetan Plateau experiments during the warm seasons (May–August) of 1998, 2008, and 2015–16 are used to assess the quality of four leading modern atmospheric reanalysis products (CFSR/CFSv2, ERA-Interim, JRA-55, and MERRA-2), and the potential impact of satellite data changes on the quality of these reanalyses in the troposphere over this data-sparse region. Although these reanalyses can reproduce reasonably well the overall mean temperature, specific humidity, and horizontal wind profiles against the benchmark independent sounding observations, they have nonnegligible biases that can be potentially bigger than the analysis-simulated mean regional climate trends over this region. The mean biases and mean root-mean-square errors of winds, temperature, and specific humidity from almost all reanalyses are reduced from 1998 to the two later experiment periods. There are also considerable differences in almost all variables across different reanalysis products, though these differences also become smaller during the 2008 and 2015–16 experiments, in particular for the temperature fields. The enormous increase in the volume and quality of satellite observations assimilated into reanalysis systems is likely the primary reason for the improved quality of the reanalyses during the later field experiment periods. Besides differences in the forecast models and data assimilation methodology, the differences in performance between different reanalyses during different field experiment periods may also be contributed by differences in assimilated information (e.g., observation input sources, selected channels for a given satellite sensor, quality-control methods). 

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