Large-scale Environmental Variables Research Articles

Environmentally-driven variations in reproductive traits in a sandy beach bivalve throughout its geographic range

The yellow clam Amarilladesma mactroides (Reeve, 1854) inhabits the intertidal zone of sandy beaches along the temperate Atlantic coast from Brazil to Argentina (24°-41°S). Over the past decades, there has been a noticeable decline in its abundance, underscoring the importance of understanding their reproductive strategies and phenotypic plasticity for conservation efforts. This study explored large-scale variations in the size at first sexual maturity (SL50), the age at maturity (A50), the vitellogenic oocyte area (VOA), and a reproductive age index (RAI) that combines A50 and the lifespan of each clam population, in relation to local habitat characteristics (e.g., beach morphodynamics) and large-scale environmental variables, such as salinity, sea surface temperature (SST) and chlorophyll-a. Four sandy beach clam populations from Uruguay and Argentina (latitudinal range: 33°45′S - 38°56′S) were sampled monthly between June and August during the reproductive season. SL50 increased linearly with the mean and maximum SST (SSTmean, SSTmax), while VOA and RAI decreased with tidal range and width of intertidal zone, respectively. In beaches that had higher SSTmean and SSTmax, yellow clams reached maturity earlier and spent more than 70% of their lifespan in reproduction. The yellow clam population at the lowest latitude showed the largest oocyte sizes and a shorter lifespan of 3.5 years, while the population at the highest latitude had a longer lifespan (7 years) and the smallest VOA values. Populations at intermediate latitudes showed delayed sexual maturity, a 50–60% investment of their lifespan in reproduction, the longest lifespan (up to 9 years) and intermediate VOA values. The influence of the Rio de la Plata and Bahia Blanca estuaries modified the expected latitudinal gradient in reproductive traits, but local habitat conditions prevailed over large-scale environmental variables as explanatory factors of the reproductive strategy of the yellow clam. Therefore, the species displays phenotypic plasticity in its reproductive aspects to ensure population success.

An Extratropical Pathway for the Madden–Julian Oscillation’s Influence on North Atlantic Tropical Cyclones

Abstract This study investigates the combined impacts of the Madden–Julian oscillation (MJO) and extratropical anticyclonic Rossby wave breaking (AWB) on subseasonal Atlantic tropical cyclone (TC) activity and their physical connections. Our results show that during MJO phases 2–3 (enhanced Indian Ocean convection) and 6–7 (enhanced tropical Pacific convection), there are significant changes in basinwide TC activity. The MJO and AWB collaborate to suppress basinwide TC activity during phases 6–7 but not during phases 2–3. During phases 6–7, when AWB occurs, various TC metrics including hurricanes, accumulated cyclone energy, and rapid intensification probability decrease by ∼50%–80%. Simultaneously, large-scale environmental variables, like vertical wind shear, precipitable water, and sea surface temperatures become extremely unfavorable for TC formation and intensification, compared to periods characterized by suppressed AWB activity during the same MJO phases. Further investigation reveals that AWB events during phases 6–7 occur in concert with the development of a stronger anticyclone in the lower troposphere, which transports more dry, stable extratropical air equatorward, and drives enhanced tropical SST cooling. As a result, individual AWB events in phases 6–7 can disturb the development of surrounding TCs to a greater extent than their phases 2–3 counterparts. The influence of the MJO on AWB over the western subtropical Atlantic can be attributed to the modulation of the convectively forced Rossby wave source over the tropical eastern Pacific. A significant number of Rossby waves initiating from this region during phases 5–6 propagate into the subtropical North Atlantic, preceding the occurrence of AWB events in phases 6–7.

Nonlocal Controls on Tropical Cyclogenesis: A Trajectory-Based Genesis Potential Index

Abstract Tropical cyclone (TC) genesis is initiated by convective precursors or “seeds” and influenced by environmental conditions along the seed-to-TC trajectories. Genesis potential indices (GPIs) provide a simple way to evaluate TC genesis likelihood from environmental conditions but have two limitations that may introduce bias. First, the globally fixed GPIs fail to represent interbasin differences in the relationship between environments and genesis. Second, existing GPIs are only functions of local environmental conditions, whereas nonlocal factors may have a significant impact. We address the first limitation by constructing basin- and time-scale-specific GPIs (local-GPIs) over the eastern North Pacific (ENP) and North Atlantic (NA) using Poisson regression. A sequential feature selection (SFS) algorithm identifies vertical wind shear and a heating condition as leading factors controlling TC genesis in the ENP and the NA, respectively. However, only a slight improvement in performance is achieved, motivating us to tackle the second limitation with a novel trajectory-based GPI (traj-GPI). We merge adjacent nonlocal environments into each grid point based on observed seed trajectory densities. The seed activity, driven mainly by upward motion, and the transition to TCs, controlled primarily by vertical wind shear or heating conditions, are captured simultaneously in the traj-GPI, yielding a better performance than the original GPIs. This study illustrates the importance of seed activity in modeling TC genesis and identifies key environmental factors that influence the process of TC genesis at different stages. Significance Statement The genesis potential index (GPI) is an effective tool for modeling the likelihood of tropical cyclone (TC) genesis for a given time and location. This study reveals that existing GPIs are primarily biased by a lack of information about nonlocal TC seed activity, since they are based only on local large-scale environmental variables. According to our study, upward motion and vertical wind shear are the most influential environmental factors in seed genesis and the transition from seed to TC, respectively. Based on the observed seed trajectories, we build trajectory-based GPIs that include the information from seed activity. Spatiotemporal performances of TC genesis are significantly improved over the original GPIs.

Potential distribution and conservation of the hog-nosed skunk (genus Conepatus, Mammalia: Mephitidae)

Understanding the range-wide distribution and abundance of species is critical for their conservation and management. Hog-nosed skunks, genus Conepatus, are an understudied small carnivores’ taxa with a broad geographic range across America. However, the factors that shape their distribution are poorly understood and large-scale analyses of Conepatus species’ range and ecological niche are absent. On the basis of species occurrence records, large-scale environmental variables (bioclimatic and topographic) and using niche-based species distribution modeling, we estimate the suitable areas and the most important variables that underlie their distribution. Although general pattern of Conepatus distribution is mostly congruent with those described from the literature, C. semistriatus seems to be expanding they distribution range, likely at the expense of changes in anthropogenic land use. Our results show that isothermally (for C. chinga and C. leuconotus) and the mean of the monthly temperature ranges (for C. semistriatus) are the main variables influencing hog-nosed skunks’ distribution. Also, the three species responded negatively to the presence of trees. Finally, and according to our models, sites of high habitat suitability, at least for C. chinga, are underrepresented in protected areas. We believe that the geographic and potential distribution maps presented here constitutes a valuable tool to better understand Conepatus ecology as well as for their conservation planning.

Process-Oriented Diagnosis of Tropical Cyclones in Reanalyses Using a Moist Static Energy Variance Budget

Abstract Global models are frequently used for tropical cyclone (TC) prediction and climate projections but have biases in their representation of TCs that are not fully understood. The objective of this work is to assess how well and how robustly physical processes that are important for TC development are represented in modern reanalysis products and to consider whether reanalyses can serve as an observationally constrained reference against which model representation of these physical processes can be evaluated. Differences in the representation of large-scale environmental variables relevant to TC development do not readily explain the spread in TC climatologies across climate models, as found in prior work, or across reanalysis datasets, as shown here. This motivates the use of process-oriented diagnostics that focus on how convection, moisture, clouds, and related processes are coupled and can be used to identify areas to target for model improvement. Using the column-integrated moist static energy (MSE) variance budget, we analyze radiative and surface flux feedbacks across five different reanalyses. We construct an intensity-bin composite of the MSE variance budget to compare storms of similar intensity. Our results point to some fundamental differences across reanalyses in how they represent MSE variance and surface flux and radiative feedbacks in TCs, which could contribute to differences across reanalyses in how they represent TCs, but other factors also likely contribute. Any future work that evaluates these diagnostics in GCMs against reanalyses should do so cautiously, and efforts should be undertaken to provide a true observational estimate of these processes.

Large-scale spatial variation and phenotypic integration in three Argynnini species inform about functions and evolutionary drivers of butterfly wings

Understanding how large-scale environmental variability may shape the distribution of phenotypic variation remains central to evolutionary biology. Across-species comparisons of trait variation alongside environmental gradients may offer valuable insights into how different species may respond to similar selective pressures. We conducted a comparative morphological study (>32° latitude and >47° longitude) on three closely related Argynnini butterfly species, Speyeria aglaja, Fabriciana adippe, and F. niobe. We measured wing size and coloration to assess (1) whether they respond similarly or differently to environmental factors (longitude, latitude, altitude, temperature, precipitation, solar radiation, wind speed); (2) if these factors correspond with those associated with the species’ genetic structure based on a previous study; and (3) whether correlations between phenotypic traits within individuals are species-specific. We found common and species-specific associations of climatic (precipitation, wind speed) and geographic (longitude, altitude) factors with the composite phenotypic variation. Wing size was associated with different variables in the studied species, while melanisation mainly increased in cooler regions in all species, suggesting that the need for temperature regulation is a strong selective pressure on melanisation. Wing size was associated with the genetic structure in all species, highlighting the functional importance of this trait. The environmental drivers associated with the phenotypic variation in S. aglaja and F. adippe were largely the same as those associated with their genetic structure, hinting at a genetic underpinning of the observed morphological variation due to local adaption. We report some distinct intraspecific trait correlations in S. aglaja and F. adippe, indicative of independent phenotypic integration. These phenotypes seem to be associated with protection against predators and thermal regulation in the respective habitats of both species, suggesting that similar selective pressures have resulted in the evolution of different trait combinations. Some of the inter-specific differences could be related to diverging niche breadths and dispersal capacities, exemplifying that the evolution of trait integration and spatial phenotypic differentiation may differ between closely related species with overlapping distribution ranges. Our findings highlight the importance of comparative assessments of variation, and demonstrate that the relative effects of drivers of variability may vary between sister species.

Hydrological properties predict the composition of microbial communities cycling methane and nitrogen in rivers

Sediment microbial communities drive the biogeochemical cycles that make rivers globally important sources and sinks of carbon (C) and nitrogen (N). The structure of these communities is strongly determined by the local physico-chemical environment. However, we currently lack an understanding of the factors that determine microbial community structures at the catchment scale. Here, we show that the contribution of groundwater to total river flow (quantified as base flow index; BFI) predicts the structure and diversity of the different microbial functional groups that cycle N and C across nine UK rivers, spanning a geological BFI gradient from 0.23 (clay sediment) to 0.95 (chalk gravel sediment). Furthermore, the GC-content (percentage of guanine-cytosine bases in a DNA sequence) and codon-usage bias of ammonia monooxygenase DNA sequences, and the hydrophobicity and net-charge of the corresponding amino acid sequences, were all strongly correlated with BFI, likely reflecting physiological adaptations to different riverbed sediment structure along the BFI gradient. Our results offer an opportunity to overcome the “paradox of scales” that has seen microbial ecologists focus on small- rather than large-scale environmental variables, enabling us to scale-up our understanding of microbial biogeochemistry to the catchment and beyond.

Statistical and machine learning methods applied to the prediction of different tropical rainfall types

Predicting rain from large-scale environmental variables remains a challenging problem for climate models and it is unclear how well numerical methods can predict the true characteristics of rainfall without smaller (storm) scale information. This study explores the ability of three statistical and machine learning methods to predict 3-hourly rain occurrence and intensity at 0.5° resolution over the tropical Pacific Ocean using rain observations the Global Precipitation Measurement (GPM) satellite radar and large-scale environmental profiles of temperature and moisture from the MERRA-2 reanalysis. We also separated the rain into different types (deep convective, stratiform, and shallow convective) because of their varying kinematic and thermodynamic structures that might respond to the large-scale environment in different ways. Our expectation was that the popular machine learning methods (i.e., the neural network and random forest) would outperform a standard statistical method (a generalized linear model) because of their more flexible structures, especially in predicting the highly skewed distribution of rain rates for each rain type. However, none of the methods obviously distinguish themselves from one another and each method still has issues with predicting rain too often and not fully capturing the high end of the rain rate distributions, both of which are common problems in climate models. One implication of this study is that machine learning tools must be carefully assessed and are not necessarily applicable to solving all big data problems. Another implication is that traditional climate model approaches are not sufficient to predict extreme rain events and that other avenues need to be pursued.

Cyclones and Global Floods from an Observation-Simulation Evaluation: Contributions and Long-Term Changes

As two of the most destructive natural disasters, tropical cyclones (TCs) and extratropical cyclones (ETCs) can bring about huge casualties and socioeconomic losses to the major continents. However, little is known about the contributions of TCs and ETCs to global flooding and the spatial-temporal variations both in the magnitude and frequency of cyclone-related floods. We collected, to our knowledge, the most complete global streamflow dataset with at least 25-year complete records between 1979 and 2012. Using this observed streamflow dataset and simulated runoff dataset from ERA5 reanalysis, for the first time at the global scale, our results show that the highest contributions of TCs to annual floods occur in coastal areas of East Asia and Australia (both > 40%), followed by North America (>25%), with a general decrease from coastline to inland, while the highest contributions of ETCs to annual floods occur in Eurasia (>70%) and North America (>60%), followed by South America and South Africa (>50%). Seasonally, TC-induced floods are clustered to occur in boreal (austral) summer and autumn, respectively, in the Northern (Southern) Hemisphere, while there is no significant difference in ETC-induced floods among the four seasons. Additionally, contributions of cyclones to floods in simulations are highly consistent with the observations. Except for East Asia, both the magnitude and frequency of TC-induced floods show increasing trends in South Asia, coastal North America, Mexico, north Australia and southeastern South Africa, which is basically and directly explained by increasing TC tracks, duration and density in these areas. For ETCs, widespread decreasing trends in magnitude and frequency of ETC-induced floods were detected across Eurasia, South America, and most of North America. Changes in large-scale environmental variables also correspond well to cyclone activities, which further confirms the reasonable trends in magnitude and frequency of cyclone floods.

Effects of climate variability on an estuarine green crab Carcinus maenas population

The increase in frequency and intensity of extreme climate events over the last few decades has been leading to profound changes in estuarine and marine ecosystems worldwide, with strong implications for the species inhabiting these ecosystems as well as for the services provided by them. In this study, we analysed the effects of climate variability on the temporal and spatial variations in population dynamics of the green crab Carcinus maenas in the Mondego estuary (Portugal), between 2003 and 2018. In this 15-year period, a greater recruitment of C. maenas was observed during drought periods, periods which was matched by an increase in secondary production. Ontogenic stage segregation was also observed, with juveniles being found mainly in the further upriver areas of the estuary. The estuarine population was mainly composed of the green morphotype, with the orange and red morphotypes present in more downstream areas of the estuary. Redundancy analysis (RDA) showed high spatial and temporal variability of C. maenas in the estuary which was related with environmental changes over the 15-year period. A correlation between C. maenas biological features and several local-scale (salinity and river runoff) and large-scale (North Atlantic Oscillation index and Eastern Atlantic pattern) environmental variables was identified through cumulative sums analysis (CUSUM), indicating a strong environmental control on C. maenas population dynamics. This paper shows the importance of relatively long-term datasets to unravel the effects of extreme weather events due to climate change on key epibenthic estuarine species, and also how they might cope with a changing marine environment.

Projected Changes in South Asian Monsoon Low Pressure Systems

AbstractMonsoon low pressure systems (MLPSs) are among the most important synoptic-scale disturbances of the South Asian summer monsoon. Potential changes in their characteristics in a warmer climate would have broad societal impacts. Yet, the findings from a few existing studies are inconclusive. We use the Geophysical Fluid Dynamics Laboratory (GFDL) coupled climate model CM4.0 to examine the projected changes in the simulated MLPS activity under a future emission scenario. It is shown that CM4.0 can skillfully simulate the number, genesis location, intensity, and lifetime of MLPSs. Global warming gives rise to a significant decrease in MLPS activity. An analysis of several large-scale environmental variables, both dynamic and thermodynamic, suggests that the decrease in MLPS activity can be attributed mainly to a reduction in low-level relative vorticity over the core genesis region. The decreased vorticity is consistent with weaker large-scale ascent, which leads to less vorticity production through the stretching term in the vorticity equation. Assuming a fixed radius of influence, the projected reduction in MLPSs would significantly lower the associated precipitation over north-central India, despite an overall increase in mean precipitation.

Impact of the Extended Boreal Summer Intraseasonal Oscillation on Western North Pacific Tropical Cloud Cluster Genesis Productivity

AbstractTropical cloud clusters (TCCs) are traditionally viewed as precursors of tropical cyclone (TC) genesis. Most studies have focused on the impact of the extended boreal summer intraseasonal oscillation (ISO) on TC activity over the western North Pacific (WNP), while the modulation of the ISO on WNP TCC genesis productivity (TCCGP), that is, the ratio of TC to TCC counts, has been investigated much less frequently. This study suggests that the extended boreal summer ISO modulates WNP TCCGP, with higher (lower) TCCGP during convectively active (inactive) ISO phases. Changes in TCCGP are found to be closely associated with changes of large-scale environmental factors. During the convectively active ISO phase, significantly increased TCCGP is associated with strengthened low-level cyclonic circulation anomalies and increased midlevel relative humidity anomalies over the WNP basin. The genesis potential index (GPI) contains several large-scale environmental variables demonstrated to relate to TCs and TCCs. The GPI can adequately depict the ISO modulation of WNP TCCGP through its alterations of large-scale parameters. Low-level vorticity makes the largest contribution to the change of TCCGP with a secondary contribution from midlevel relative humidity. Interestingly, the nonlinear GPI terms make comparable contributions, which can be partly explained by the synoptic-scale wave activity associated with the ISO mode. Stronger (weaker) 3–8-day synoptic-scale wave train intensity and increased (decreased) low-level eddy kinetic energy are found to be associated with the enhanced (weakened) monsoon circulation over the WNP basin during convectively active (inactive) ISO phases.

EFFECTS OF SELECTED ENVIRONMENTAL VARIABLE UPON THE DISTRIBUTION OF HOVER FLY (INSECTA: DIPTERA: SYRPHIDAE) ALONG WITH AN ALTITUDINAL GRADIENT

The relationship between species richness of Syrphidae with large scale environmental variables, altitude and latitudinal gradients has been widely studied in selected study area of the state of Himachal Pradesh, India. The study was done over a period of one year (2015-2016) along the selected four districts of Himachal Pradesh. Altogether 34 species under 16 genera have been collected from the selected study areas along the altitudinal slope.

Statistical Analysis of Tropical Cyclones in the Solomon Islands

This study examines tropical cyclone (TC) activity around the Solomon Islands (SIs) using best track data from the Tropical Cyclone Warning Centre, Brisbane, and the Regional Specialized Meteorological Centre, Nadi. Analysis of long-term trends showed that the frequency of TCs has decreased in this region, while the average TC intensity has increased. Datasets were classified according to the phase of Madden–Julian Oscillation (MJO) and the index of El Niño Southern Oscillation (ENSO), provided by Bureau of Meteorology. The MJO significantly influenced TC activity in the SIs, with TC genesis occurring most frequently in phases 6–8. In contrast, TC genesis occurred less frequently in phase 5. ENSO also influenced TC genesis; more TCs were generated in El Niño periods. The TC genesis locations during El Niño (La Niña) periods were significantly displaced to the north (south) over the SIs. TCs generated during El Niño conditions tended to be strong. This study also explores the modulation of TCs in terms of the seasonal climatic variability of large-scale environmental variables such as sea surface temperature (SST), low-level relative vorticity, vertical wind shear, and upper level divergence.

A Poisson regression approach to model monthly hail occurrence in Northern Switzerland using large-scale environmental variables

In Switzerland, hail regularly causes substantial damage to agriculture, cars and infrastructure, however, little is known about its long-term variability. To study the variability, the monthly number of days with hail in northern Switzerland is modeled in a regression framework using large-scale predictors derived from ERA-Interim reanalysis. The model is developed and verified using radar-based hail observations for the extended summer season (April–September) in the period 2002–2014. The seasonality of hail is explicitly modeled with a categorical predictor (month) and monthly anomalies of several large-scale predictors are used to capture the year-to-year variability. Several regression models are applied and their performance tested with respect to standard scores and cross-validation.The chosen model includes four predictors: the monthly anomaly of the two meter temperature, the monthly anomaly of the logarithm of the convective available potential energy (CAPE), the monthly anomaly of the wind shear and the month. This model well captures the intra-annual variability and slightly underestimates its inter-annual variability. The regression model is applied to the reanalysis data back in time to 1980. The resulting hail day time series shows an increase of the number of hail days per month, which is (in the model) related to an increase in temperature and CAPE. The trend corresponds to approximately 0.5days per month per decade. The results of the regression model have been compared to two independent data sets. All data sets agree on the sign of the trend, but the trend is weaker in the other data sets.

Favorable environments for the occurrence of overshooting tops in tropical cyclones

Based on Multifunctional Transport Satellite data and the infrared window-texture detection algorithm, the level of overshooting top (OT) activity within a tropical cyclone (TC), which is defined as the hourly mean number of OT occurrence, was statistically investigated in the western North Pacific basin for the period 2005–12. Based on the level of OT activity, the samples were divided into OT and non-OT cases or high-activity-OT (HA-OT) and low-activity-OT (LA-OT) cases. The differences in large-scale environmental variables between OT (HA-OT) and non-OT (LA-OT) cases were examined 12 hours prior to the OT occurrence. Statistical analysis showed that environmental differences did exist between the OT and non-OT cases. The OTs were more skewed towards the early stage of the TC life cycle, and mostly concentrated in low latitudes. Meanwhile, a sufficiently deep warm-water layer, large temperature difference between the upper- and lower-level troposphere, large humidity at the middle and upper levels, and large atmospheric instability, were favorable for OT occurrence. The differences in large-scale environmental characteristics between HA-OTs and LA-OTs were not as significant as those between OTs and non-OTs, but the HA-OT samples tended to occur when the vertical shear was weak and the TC intensity was low. Finally, statistical models were designed to predict the OT and HA-OT. When at least three OT (HA-OT) predictor thresholds were satisfied, the Peirce skill score reached a maximum value of 0.49 (0.30).

Tropical cyclones in the GISS ModelE2

The authors describe the characteristics of tropical cyclone (TC) activity in the GISS general circulation ModelE2 with a horizontal resolution 1°×1°. Four model simulations are analysed. In the first, the model is forced with sea surface temperature (SST) from the recent historical climatology. The other three have different idealised climate change simulations, namely (1) a uniform increase of SST by 2 degrees, (2) doubling of the CO2 concentration and (3) a combination of the two. These simulations were performed as part of the US Climate Variability and Predictability Program Hurricane Working Group. Diagnostics of standard measures of TC activity are computed from the recent historical climatological SST simulation and compared with the same measures computed from observations. The changes in TC activity in the three idealised climate change simulations, by comparison with that in the historical climatological SST simulation, are also described. Similar to previous results in the literature, the changes in TC frequency in the simulation with a doubling CO2 and an increase in SST are approximately the linear sum of the TC frequency in the other two simulations. However, in contrast with previous results, in these simulations the effects of CO2 and SST on TC frequency oppose each other. Large-scale environmental variables associated with TC activity are then analysed for the present and future simulations. Model biases in the large-scale fields are identified through a comparison with ERA-Interim reanalysis. Changes in the environmental fields in the future climate simulations are shown and their association with changes in TC activity discussed.

Macrophytes in boreal streams: Characterizing and predicting native occurrence and abundance to assess human impact

Macrophytes are a structurally and functionally essential element of stream ecosystems and therefore indispensable in assessment, protection and restoration of streams. Modelling based on continuous environmental gradients offers a potential approach to predict natural variability of communities and thereby improve detection of anthropogenic community change. Using data from minimally disturbed streams, we described natural macrophyte assemblages in pool and riffle habitats separately and in combination, and explored their variation across large scale environmental gradients. Specifically, we developed RIVPACS-type models to predict the presence and abundance of macrophyte taxa at stream sites in the absence of human influence and, used data from impacted streams to explore the responses of three biotic indices to anthropogenic stress. The indices used, taxonomic completeness (O/E-taxa), a measure of compositional dissimilarity (BC-index) and an index taking into account the abundance of species (AB-index), are based on predicted and observed macrophyte communities. We found that size of the catchment area, altitude, latitude and percentage of lakes in the catchment were the large scale environmental variables that best predicted the natural variation of assemblages. The RIVPACS approach substantially improved both the precision and accuracy to predict the natural communities and the sensitivity to human disturbance. O/E-taxa performed best in relation to the null model decreasing the variation by 20% in pools, 29% in riffles and 32% in combined data. In general, models based on the riffle assemblages performed better than models based on pool assemblages, but including both habitats and predicting abundances instead of only presence/absence yielded the greatest accuracy and sensitivity. Our results support the use of multivariate modelling techniques in predicting reference condition to assess status of stream macrophyte communities.

On the decreasing trend of the number of monsoon depressions in the Bay of Bengal

This study unravels the physical link between the weakening of the monsoon circulation and the decreasing trend in the frequency of monsoon depressions over the Bay of Bengal. Based on the analysis of the terms of Genesis Potential Index, an empirical index to quantify the relative contribution of large scale environmental variables responsible for the modulation of storms, it is shown here that the reduction in the mid-tropospheric relative humidity is the most important reason for the decrease in the number of monsoon depressions. The net reduction of relative humidity over the Bay of Bengal is primarily due to the decrease in the moisture flux convergence, which is attributed to the weakening of the low level jet, a characteristic feature of monsoon circulation. Further, the anomalous moisture convergence over the western equatorial Indian Ocean associated with the rapid warming of the sea surface, reduces the moisture advection into the Bay of Bengal and hence adversely affect the genesis/intensification of monsoon depressions. Hence, the reduction in the number of monsoon depression over the Bay of Bengal could be one of the manifestations of the differential rates in the observed warming trend of the Indian Ocean basin.

Changes in Tropical Cyclone Activity over Northwest Western Australia in the Past 50 Years and a View of the Future 50 Years

Abstract In the first half of this research, this study examines the trend in tropical cyclone (TC) activity over the economically important northwest Western Australia (NWA) TC basin (equator–40°S, 80°–140°E) based on statistical analyses of the International Best Track Archive for Climate Stewardship (IBTrACS) and large-scale environmental variables, which are known to be closely linked to the formation and longevity of TCs, from NCEP–NCAR reanalyses. In the second half, changes in TC activity from climate model projections for 2000–60 are compared for (i) no scenario change (CNTRL) and (ii) the moderate IPCC Special Report on Emission Scenarios (SRES) A1B scenario (EGHG). The aims are to (i) determine differences in mean annual TC frequency and intensity trends, (ii) test for differences between genesis and decay positions of CNTRL and EGHG projections using a nonparametric permutation test, and (iii) use kernel density estimation (KDE) for a cluster analysis of CNTRL and EGHG genesis and decay positions and generate their probability distribution functions. The main findings are there is little difference in the mean TC number over the period, but there is a difference in mean intensity; CNTRL and EGHG projections differ in mean genesis and decay positions in both latitude and longitude; and the KDE reveals just one cluster in both CNTRL and EGHG mean genesis and decay positions. The EGHG KDE is possibly disjoint, with a wider longitudinal spread. The results can be explained in terms of physical, meteorological, and sea surface temperature (SST) conditions, which provide natural limits to the spread of the genesis and decay points.