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Abstract Assessing the role of anthropogenic warming from temporally inhomogeneous historical data in the presence of large natural variability is difficult and has caused conflicting conclusions on detection and attribution of tropical cyclone (TC) trends. Here, using a reconstructed long-term proxy of annual TC numbers together with high-resolution climate model experiments, we show robust declining trends in the annual number of TCs at global and regional scales during the twentieth century. The Twentieth Century Reanalysis (20CR) dataset is used for reconstruction because, compared with other reanalyses, it assimilates only sea-level pressure fields rather than utilize all available observations in the troposphere, making it less sensitive to temporal inhomogeneities in the observations. It can also capture TC signatures from the pre-satellite era reasonably well. The declining trends found are consistent with the twentieth century weakening of the Hadley and Walker circulations, which make conditions for TC formation less favourable. 

Abstract Compared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen and Bellingshausen, Indian, and West Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently-developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal timescales. 

Studies of projected changes in tropical cyclones under anthropogenic climate change, as well as their modulation by internal climate modes, make use of global climate models. To this end, tropical cyclones can be tracked in the output of higher resolution models. Using climate models to make future projections of tropical cyclones relies upon having a baseline of the characteristics of model storms under the current climate. This study focuses on two high-resolution datasets – the NASA GEOS-5 Model (Goddard Earth Observing System Model, Version 5) and the MERRA-2 Reanalysis (Modern-Era Retrospective analysis for Research and Applications, Version 2). Both of these datasets were created using exactly the same atmospheric model during the same period. However, while GEOS-5 is a free-running atmospheric model forced only with sea surface temperature, MERRA-2 is a reanalysis product, i.e. the model assimilates data from a large variety of data sources. Thus, by comparing tropical cyclones tracked in these datasets to each other and global best track datasets in the period 1980-1999, this project aims to evaluate 1) the sensitivity of this model to how it is forced and 2) how well the storms tracked in GEOS-5 and MERRA-2 replicate observed tropical cyclones’ characteristics. We used two different tracking schemes on both datasets and found no significant difference in the performance of the model and the reanalysis in simulating tropical cyclones. Standard diagnostics for tropical cyclones, such as the mean number, intensity distribution, as well as their interannual variability are very similar in the free-running model and the reanalysis. Both GEOS-5 and MERRA-2 show a bias towards weaker tropical cyclones than observed and GEOS-5 has storms that occur closer to the equator than in the observed record. Neither GEOS-5 nor MERRA-2 accurately reproduce tropical cyclone modulation by ENSO. Additionally, comparison of MERRA-2 to other reanalysis datasets shows that MERRA-2 on average generates fewer total but also more intense storms than the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim) and Japanese 55-Year Reanalysis (JRA-55). Further research must be performed to understand why this data assimilation is failing to provide a positive impact on the tropical cyclone simulation in this model. 

This study evaluates the tropical cyclone (TC) activity in two high‐resolution data sets—MERRA‐2 Reanalysis (Modern‐Era Retrospective Analysis for Research and Applications, Version 2) and MERRA‐2 AMIP (Atmospheric Model Intercomparison Project). These data sets use the same atmospheric model, the Goddard Earth Observing System Model, Version 5 (GEOS‐5) during the same period. However, while MERRA‐2 AMIP is a free‐running atmospheric simulation forced only with sea surface temperature (SST), MERRA‐2 Reanalysis uses an advanced data assimilation system to include a large variety of data sets. Thus, we analyze (1) the sensitivity of TC activity to the model forcing, (2) how well the TCs in both data sets replicate observed TC characteristics, (3) the sensitivity of these results to tracking schemes and thresholds. Standard diagnostics such as the number of tropical cyclones and their intensity distribution are very similar in the AMIP model and the reanalysis. TCs in both data sets are weaker than observed, as is typical for the spatial resolution of these global models. Overall, the use of data assimilation in the MERRA‐2 Reanalysis does not lead to a significantly better TC climatology than in AMIP. Furthermore, comparison of the MERRA‐2 Reanalysis to two other reanalysis data sets shows that MERRA‐2 generates fewer, but more intense TCs, than those reanalysis products.