Introduction

<p>Quantitative estimates of the relationship between the interannual variability of Antarctic and Arctic sea ice and changes in the surface temperature in the Northern and Southern Hemispheres using satellitedata, observational data and reanalysis data for the last four decades (1980-2019) are obtained. The previously noted general increase in the Antarctic sea ice extent (up to 2016) (according to satellite data available only since the late 1970s), happening simultaneously with global warming and rapid decrease in the Arctic sea ice extent, is associated with the regional manifestation of natural climate fluctuations with periods of up to several decades. The results of correlation and crosswavelet analysis indicate significant coherence and negative correlation of hemispheric surface temperature with not only Arctic,but also Antarctic sea ice extent in recent decades.</p><p>Seasonal and regional peculiarities of snow cover sensitivity to temperature regime changes in the Northern Hemisphere are noted with an assessment of changes in recent decades. Peculiarities of snow cover variability in Eurasia and North America are presented. In particular, the peculiarities of changes in snow cover during the autumn seasons are noted.</p>

<p>The melting of polar ice caps and sea ice are of immediate concern in the context of global warming. The observations suggest that the thickness, as well as the areal extent of the Arctic sea ice, have been declining in the last three decades, in large part due to manmade global warming. The effect of faster sea ice melt on lower latitude climate is not well understood as compared to that of mid and high latitudes. It is reported that the mid-Pacific trough (MPT) can be influenced by a stationary wave train triggered in response to a melt of sea ice over the Bering strait (Deng et al., 2018, J. Clim).   The MPT is known to influence Pacific tropical cyclone (TC) activity.</p><p>         Here, we investigate the effect of the summer sea ice variability over the Arctic on Pacific TC activity. We have seen in the higher melting Sea Ice years showing the strong wave train toward the lower latitude over the northern pacific in comparison to the lower melting years and also affecting the pacific TCs. The summer Arctic sea ice concentration is regressed on TC track density and accumulated cyclone energy (ACE). Both track density and ACE show an increase with increased sea ice concentration. The wind shear over the tropical Pacific is found to have an opposite relation with the Arctic sea ice concentration that led to a more favorable environment for the TC development when the sea ice concentration is high.</p><p><strong>KEYWORDS: </strong>Climate Change; Tropical Cylone;</p>

This study investigates the relationship between the summer North Atlantic Oscillation (SNAO) and the simultaneous Northern Hemisphere (NH) land surface air temperature (SAT) by using the Climate Research Unit (CRU) data. The results show that the SNAO is related to NH land SAT, but this linkage has varied on decadal timescales over the last 52 years, with a strong connection appearing after the late 1970s, but a weak connection before. The mechanism governing the relationship between the SNAO and NH land SAT is discussed based on the NCEP/NCAR reanalysis data. The results indicate that such a variable relationship may result from changes of the SNAO mode around the late 1970s. The SNAO pattern was centered mainly over the North Atlantic before the late 1970s, and thus had a weak influence on the NH land SAT. But after the late 1970s, the SNAO pattern shifted eastward and its southern center was enhanced in magnitude and extent, which transported the SNAO signal to the North Atlantic surrounding continents and even to central East Asia via an upper level wave train along the Asian jet.

This study examines the variation in tropical cyclone (TC) intensity for different phases of the El Niño–Southern Oscillation (ENSO) phenomenon in the Fiji, Samoa, and Tonga (FST) region. The variation in TC intensity is inferred from the accumulated cyclone energy (ACE), which is constructed from the 6-hourly Joint Typhoon Warning Center best-track data for the period 1985–2006. Overall, results suggest that ACE in the FST region is considerably influenced by the ENSO signal. A substantial contribution to this ENSO signal in ACE comes from the region equatorward of 15°S where TC numbers, lifetime, and intensity all play a significant role. However, the ACE–ENSO relationship weakens substantially poleward of 15°S where large-scale environmental variables affecting TC intensity are found to be less favorable during El Niño years than during La Niña years; in the region equatorward of 15°S, the reverse is true. Therefore, TCs entering this region poleward of 15°S are able to sustain their intensity for a longer period of time during La Niña years as opposed to TCs entering the region during El Niño years, when they decay more rapidly.

Changes in snow cover and sea ice extents associated with temperature changes in the Northern and Southern Hemispheres for the period 1979-2020 are analysed using monthly-mean satellite and reanalysis data. Quantitative estimates of the relationship between the Antarctic and Arctic sea ice and changes in the surface air temperature were obtained. Overall increase of the Antarctic sea ice extent is associated with the regional manifestation of natural multidecadal climate modes with periods of up to several decades (against the background of global warming and a rapid decrease in the extent of sea ice in the Arctic). The results of correlation and cross-wavelet analyses show significant coherence and negative correlation of the surface air temperature in both Arctic and Antarctic with the respective sea ice extent in recent decades. Seasonal and regional features of the snow cover sensitivity to changes in the temperature regime in the Northern Hemisphere for the past four decades are noted. The features of snow cover variability in Eurasia and North America are presented.

<p>We will first examine the skill of probabilistic tropical cyclone (TC) occurrence and intensity (ACE - accumulated cyclone energy) predictions in the Subseasonal to Seasonal (S2S) dataset. We show that some of the models in the S2S dataset have skill in predicting TC occurrence 4 weeks in advance. In contrast, only one of the models (ECMWF) has skill in predicting the anomaly of TC occurrence from the seasonal climatology beyond week 1. For models with significant mean biases, calibrating the forecast can improve the models’ prediction skill. In contrast, for models with small mean biases, calibration does not guarantee an improvement in model skill as measured by the Brier Skill Score. </p><p>We then focus only on the ECMWF model and using cluster analysis examine the sensitivity of the North Atlantic TC tracks biases to various factors, such as model resolution, lead time, and tracking. We also explore how well the ECMWF North Atlantic TC model tracks in each cluster simulate the known response to climate modes, such as ENSO and MJO. By applying simple bias corrections to each cluster of Atlantic TC tracks, we examine if we can improve the model skill in landfall prediction in the US and Caribbean.</p>

Abstract. TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth system datasets on either rectilinear or unstructured/native grids. Version 2.1 of the TE framework now provides extensive support for examining both nodal (i.e., pointwise) and areal features, including tropical and extratropical cyclones, monsoonal lows and depressions, atmospheric rivers, atmospheric blocking, precipitation clusters, and heat waves. Available operations include nodal and areal thresholding, calculations of quantities related to nodal features such as accumulated cyclone energy and azimuthal wind profiles, filtering data based on the characteristics of nodal features, and stereographic compositing. This paper describes the core algorithms (kernels) that have been added to the TE framework since version 1.0, including algorithms for editing pointwise trajectory files, composition of fields around nodal features, generation of areal masks via thresholding and nodal features, and tracking of areal features in time. Several examples are provided of how these kernels can be combined to produce composite algorithms for evaluating and understanding common atmospheric features and their underlying processes. These examples include analyzing the fraction of precipitation from tropical cyclones, compositing meteorological fields around extratropical cyclones, calculating fractional contribution to poleward vapor transport from atmospheric rivers, and building a climatology of atmospheric blocks.

The current study explores the relationship between solar variability and tropical cyclone (TC) activity using sunspot number (SSN) and TC best‐track data as respective proxies. We have considered six regions of the globe, for example, EP: Eastern Pacific, NA: North Atlantic, NI: North Indian, SI: South Indian, SP: South Pacific, and WP: Western Pacific. The results show strong anti‐correlation between yearly TC activity and yearly SSN while considering their 11‐year moving averages. This behavior is consistent for TC counts as well as accumulated cyclone energy. However, this is true only for the North Atlantic region. Overall, when we consider all regions together, more TCs (in terms of counts) are observed during lower solar activity periods (SSN < 50) as compared to higher solar activity conditions (SSN > 100). However, the yearly rates remain more or less similar. On the other hand, extreme TC events with a maximum wind speed of 137 knots and higher (category 5) are most likely to occur during the declining phase of a solar cycle and least likely to occur during the ascending phase or the maximum phase. Although solar activity levels are similar during the declining and ascending phases, the yearly occurrence rate is nearly double in the declining phase (1.123) as compared to that in the ascending phase (0.625).

An empirical approach for analyzing tropical cyclone climate is presented. The approach uses lifetime-maximum wind speed and cyclone frequency to induce two orthogonal variables labeled “activity” and “efficiency of intensity”. The paired variations of activity and efficiency of intensity along with the opponent variations of frequency and intensity configure a framework for evaluating tropical cyclone climate. Although cyclone activity as defined in this framework is highly correlated with the commonly used exponent indices like accumulated cyclone energy, it does not contain cyclone duration. Empirical quantiles are used to determine threshold intensity levels, and variant year ranges are used to find consistent trends in tropical cyclone climatology. In the western North Pacific, cyclone activity is decreasing despite increases in lifetime-maximum intensity. This is due to overwhelming decreases in cyclone frequency. These changes are also explained by an increasing efficiency of intensity. The North Atlantic shows different behavior. Cyclone activity is increasing due to increasing frequency and, to a lesser extent, increasing intensity. These changes are also explained by a decreasing efficiency of intensity. Tropical cyclone trends over the North Atlantic basin are more consistent over different year ranges than tropical cyclone trends over the western North Pacific.

The potential use of the National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) for downscaling seasonal tropical cyclone (TC) activity was analyzed here. The NCEP RSM with horizontal resolution of 50 km, was used to downscale the ECHAM4.5 Atmospheric General Circulation Model (AGCM) simulations forced with observed sea surface temperature (SST) over the western North Pacific. An ensemble of ten runs for June–November 1994 and 1998 was studied. The representation of the TCs is much improved compared to the low‐resolution forcing AGCM, but the TCs are not as intense as observed ones, as the RSM horizontal resolution is not sufficiently high. The large‐scale fields of the RSM are examined and compared to both the AGCM and the European Centre for Medium‐Range Weather Forecasts (ECMWF) reanalysis. The large‐scale fields of RSM characteristics are in general similar to those of the reanalysis. Various properties of the TCs in the RSM are also examined such as first positions, tracks, accumulated cyclone energy (ACE) and duration. While the RSM does not reproduce the higher number of TCs in 1994 than in 1998, other measures of TC activity (ACE, number of cyclone days) in the RSM are higher in 1994 than in 1998. Copyright © 2006 Royal Meteorological Society.

The global tropical cyclone (TC) number has historically been relatively constant from year‐to‐year, however, the reason remains unknown. Furthermore, climate projections are inconclusive regarding future global TC frequency changes. Here, we investigated years in which observed global TC activity deviated from the mean and potential links to ocean drivers from 1980 to 2021. We found that the annual global number of named storm days and accumulated cyclone energy (ACE) were significantly linked with El Niño–Southern Oscillation (ENSO) and the Atlantic Meridional Mode (AMM). La Niña and positive AMM are associated with the bottom percentiles of both TC metrics, and vice versa for El Niño and negative AMM. The ENSO Longitude Index explains variability in annual global named storm days and ACE as well as the Niño 3.4 index. This research reveals that reliable future projections of ENSO are necessary, but not sufficient, to understand future changes in global TC frequency.

<p>The summer 2018 was extremely dry and hot in Germany and many parts of Europe. We investigate to which extend SST increases in the North Atlantic Ocean and sea ice extent decreases in the polar sea influence such extremes. We simulate in total the four years 1998, 2003, 2014 and 2015 as years with cool, extremely warm, warm and average SST by multiple integrations of the Model for Prediction Across Scales (MPAS). For each year we perform 30 global MPAS runs in approximately 60 km resolution with SST and sea ice extent from ERA-Interim data as boundary condition. The runs are initialized on different days in December and run until the following September 1st.</p><p>The contribution investigates the results obtained from the total of 120 simulations. It discusses the resulting probability density functions (PDF) and changes in the summer precipitation and temperature in connection to changes in the summer North Atlantic Oscillation (sNAO). The results indicate that the SST and sea ice extent influence the range and mean values of the precipitation and temperature distribution functions.  Extreme values, however, occur with both cool and warm SST.</p>

This article introduces an accumulated cyclone energy (ACE) approach for estimating the return period of tropical cyclone (TC) wind risk in Florida. As opposed to calculating return periods directly from maximum sustained wind speed, the ACE-based approach also describes the duration of the strong winds, giving an additional dimension to the assessment of TC wind risks. Because Florida is a peninsula, TCs can move across the state within six hours of landfall, causing an underestimation of the inland wind footprint if only the six-hour reanalysis track points are employed as an input data source. This study uses four different scenarios and an inland exponential decay function to interpolate the wind speed between the six-hour reanalysis track points to feed the ACE-based return period calculation based on a 121-year record from 1900 to 2020. South Florida has the shortest return period (five to ten years) of TCs with an ACE equivalent to one hour of hurricane intensity (≥ 64 kt; 1 kt ∼ 0.51 m s−1) caused by intense historical hurricane strikes, and Polk County in inland central Florida has an equal return period due to frequent and long-duration TC occurrences, acting as an intersection for landfalling TCs in the Florida peninsula.

Using Joint Typhoon Warning Center tropical cyclone (TC) track data over the North Indian Ocean (NIO), National Centers for Environmental Prediction monthly reanalysis wind and outgoing long-wave radiation data, and National Oceanic and Atmospheric Administration sea surface temperature data from 1981 to 2010, spatiotemporal distributions of NIO TC activity and relationships with local sea surface temperature (SST) were studied with statistical diagnosis methods. Results of empirical orthogonal function (EOF) analysis of NIO TC occurrence frequency show that the EOF1 mode, which accounts for 16% of total variance, consistently represents variations of TC occurrence frequency over the whole NIO basin. However, spatial distributions of EOF1 mode are not uniform, mainly indicating variations of westward-moving TCs in the Bay of Bengal. The prevailing TC activity variation mode oscillates significantly on a quasi-5 year interannual time scale. NIO TC activity is notably influenced by the Indian Ocean dipole (IOD) mode. When the Indian Ocean is in a positive (negative) phase of the IOD, NIO SST anomalies are warm in the west (east) and cold in the east (west), which can weaken (strengthen) convection over the Bay of Bengal and eastern Arabian Sea, and cause anticyclonic (cyclonic) atmospheric circulation anomalies at low levels. This results in less (more) TC genesis and reduced (increased) opportunities for TC occurrence in the NIO. In addition, positive (negative) IOD events may strengthen (weaken) westerly steering flow over the Bay of Bengal, which further leads to fewer (more) westward-moving TCs which appear in regions west of 90°E in that bay.

This paper assesses the impact of air–sea coupling on Indian Ocean tropical cyclones (TCs) by comparing a 20-year long simulation of a ¼° regional coupled ocean–atmosphere model with a twin experiment, where the atmospheric component is forced by sea surface temperature from the coupled simulation. The coupled simulation reproduces the observed spatio-temporal TCs distribution and TC-induced surface cooling reasonably well, but overestimates the number of TCs. Air–sea coupling does not affect the cyclogenesis spatial distribution but reduces the number of TCs by ~ 20% and yields a better-resolved bimodal seasonal distribution in the northern hemisphere. Coupling also affects intensity distribution, inducing a four-fold decrease in the proportion of intense TCs (Cat-2 and stronger). Air–sea coupling damps TCs growth through a reduction of inner-core upward enthalpy fluxes due to the TC-induced cooling. This reduction is particularly large for the most intense TCs of the northern Indian Ocean (up to 250 W m−2), due to higher ambient surface temperatures and larger TC-induced cooling there. The negative feedback of air–sea coupling on strongest TCs is mainly associated with slow-moving storms, which spend more time over the cold wake they induce. Sensitivity experiments using a different convective parameterization yield qualitatively similar results, with a larger (~ 65%) reduction in the number of TCs. Because of their relatively coarse resolution (¼°), both set of experiments however fail to reproduce the most intense observed TCs. Further studies with finer resolution models in the Bay of Bengal will be needed to assess the expectedly large impact of air–sea coupling on those intense and deadly TCs.