VARIABILITY OF INDIAN SUMMER MONSOON : RELATIONSHIP WITH GLOBAL SST ANOMALIES

(iloh,tlcurr("!,1111111 ll111p .o; uflhe SlIllll1lt" r mUn!\4 lOn prt>cipil:lliull anomalies a nti St"a SU l f.IC(" Tt"mpt"1 a1lln"' (SST) a nUllul!it'.'i are pTt'St'IlI("d . n lir1 ) -)l' /u (1950..1479) rime St' 1; ("S uf 11l0n...nnn ind("x b co rn' l a h~d ....; Ih Iht"SST tillll' Sl'riCS al t"nch 1° ;<2° latitLIl1co! u nl:iluJt" box uf th t" \\(1(111 (>I,:eans usi ng COADS (Comprehensive O..:eanAllno...pl1("fl." Dala S('1)dn ta ttl "3riOUS timc IJgs o f lUonth s (i.t' .. l1luI11 h,'S of year s p recedi ng 'lI1.1 conc urrcnI lu Ihe1ll011Stllll1-)l'at) , Ctl lTclal ion..mups :'I ll' pn.-pUTl,.f 111111 Illaly"I'" 1(1 i.lclltify Il' IN."Oll lle ct it ln "-' Ilf llln ll....oon pTt'I,:ipil<ttiunwilh glubal S ~Ts.It is I'olin.! th ai tlll' lag,orrelatiuns .... ilh SST (Will 1:('01(31 and t'ilstt"m t'lluHt orial Padlic (Ninu-rl'l!inniliresuggl·..liw of IWlI I>p t' S o f inlt"raetjuns .....ith Ihe munsoun. The first on e, .... h il.:h sho ws pmili\'c <:orre lnlio n of summermonsoon pf('\.-ipililtion anoll1alit"s ",i lh Ihe ct"nlral and l":Jsl ..-mequaturial P<lciftc SST nnoUlal ies aboul a yea r be forelilt" 1l10 nsollfl. sUggl°.!>1Sthat lhe monsoon which follo ws abmlt a )'t'lir la ll'r ur tX'currence ofwaml t"pisode of EI..Nin~Suut hem Oscillatiun (ENSO) is generally ....-eltc It is also suggestetJ Ihal this inleract io n might be taki ng placelhruugh Ihe in llue nce or nOr1h em hemisp here int er tempera tures. Th e seco nd I)-PC of inleraclion of equ alorialPaci fic SST ....i lh mon soon is revealed through the strung n~al ive co rrela tio ns bqinning befo re lh e summer monsoon an d continuing ....; lh g~ a l er magnitud e an d o~ r ....i der extent. suuest ing th ai a .....arm SST anomaly j ust precedineanll concurrent to monsoon ~aso n weaken s th e monsson.AiNt"li intcf<n'lions bctween Ihe Indian Ocean and monsoon are also emph a si ~d in the anal ysis. Two key~ginns are ide nt ified. Th e cen tra l Indian Ocun south o f th e equalor shoW!strong positive corre la tions during (helalt' no n hl'm ",inler a nd spring. Th e other key Tq!'ion is in the north Ind ian Geran. Th e correlations are significanllynt'ga li\'e. Some teleconnections with th e Atlantic basin are also revealed which are ralhe rdifficuh to explain but ma yfind usefu l ap plications in monitoring and long-range forecas line of the monsoon.

Conservation Genomics Reveals Low Connectivity Among Populations of Threatened Roseate Terns in the Atlantic Basin

While the effects of barriers to dispersal such as population declines, habitat fragmentation, and geographic distance have been well-documented in terrestrial wildlife, factors impeding the dispersal of highly vagile taxa such as seabirds are less well understood. The roseate tern (Sterna dougallii) is a globally distributed seabird species, but populations tend to be both fragmented and small, and the species is declining across most of its range. Within the Atlantic Basin, past work has shown differentiation among roseate terns breeding on different continents, but these results were generated with a limited number of microsatellite markers. Relationships between breeding populations in the Northwestern Atlantic and the Caribbean have never been analyzed. We evaluated population structuring of roseate tern populations in North America and the Azores using both microsatellite markers and single-nucleotide polymorphisms generated through targeted sequencing of Ultra-conserved Elements. For both marker types, we found significant genetic differentiation among all 3 populations and evidence for moderate contemporary unidirectional gene flow from the Caribbean to the Azores, but not among other populations. Within the Caribbean metapopulation, we found high rates of unidirectional migration from the Virgin Islands to Florida, potentially indicative of movement from source population to sink or an artifact of dispersal among other unsampled populations in the Caribbean region. These observations have significance for species persistence in the Atlantic, as our results suggest that loss of genetic diversity within populations is unlikely to be buffered by inflow of new alleles from other breeding populations.

Comparison of ARCHER MPERC to NHC Analysis

Eyewall replacement cycles (ERCs) are events that occur in intense tropical cyclones (TCs) and are difficult to predict. An ERC event involves a secondary outer eyewall that surrounds the inner eyewall. The outer eyewall slowly moves towards the eye and weakens the inner eyewall, eventually replacing the inner eyewall. During this process, wind speeds lower and the structure of a TC becomes disorganized, further weakening the storm. TCs often restrengthen after an ERC. Little is known about the process and as such, poses an obstacle to forecasters. The Automated Rotational Center Hurricane Eye Retrieval (ARCHER) Microwave-based Probability of Eyewall Replacement Cycle (MPERC) is an algorithm that uses 89-95 GHz passive microwave imagery and intensity estimates from the National Hurricane Center (NHC), Central Pacific Hurricane Center (CPHC), or the Joint Typhoon Warning Center (JTWC) to predict the possibility of an ERC. The effectiveness and ability of ARCHER MPERC was analyzed and compared to the NHC’s official reports on all Atlantic Basin tropical cyclones from 2017 to 2019. MPERC ultimately predicted seventeen ERCs in nine tropical cyclones. Of those, seven were valid ERCs. The algorithm works well, predicting approximately 41% of the total number of predictions correctly. However, MPERC did not predict five ERCs that were cited by the NHC. It was further found that it was true that MPERC produces incorrect results in sheared and dry environments.

A Comparison of Arctic and Atlantic Cyclone Predictability

Arctic cyclones (ACs) are synoptic scale features that can be associated with strong, intense winds over the Arctic region for long periods of time, potentially leading to rapid declines of sea ice during the summer. As a consequence, sea ice predictions may rely on the predictability of cyclone-related wind speed and direction, which critically depends on the cyclone track and intensity. Despite this, there are relatively few studies that have documented the predictability of ACs during the summer, beyond a few case studies, nor has there been an extensive comparison of whether these cyclones are more or less predictable relative to comparable midlatitude cyclones, which have been studied in greater detail. The goal of this study is to document the practical predictability of AC position and intensity forecasts over 100 cases and compare it to 89 Atlantic basin midlatitude cyclones using the Global Ensemble Forecast System (GEFS) Reforecast V2. This dataset contains 11-member ensemble forecasts initialized daily from 1985-present using a fixed model. In this study, 1 and 3 day forecast hours are compared, where predictability is defined as the ensemble mean root mean square error and ensemble standard deviation (SD). Although Atlantic basin cyclone tracks are characterized by higher predictability relative to comparable ACs, intensity predictability is higher for ACs. In addition, storms characterized by low ensemble SD and predictability are found in regions of higher baroclinic instability than storms characterized by high predictability. There appears to be little, if any, relationship between latent heat release and precipitable water and predictability.

Deep Learning Experiments for Tropical Cyclone Intensity Forecasts

Reducing tropical cyclone (TC) intensity forecast errors is a challenging task that has interested the operational forecasting and research community for decades. To address this, we developed a deep learning (DL)-based Multilayer Perceptron (MLP) TC intensity prediction model. The model was trained using the global Statistical Hurricane Intensity Prediction Scheme (SHIPS) predictors to forecast the change in TC maximum wind speed for the Atlantic Basin. In the first experiment, a 24-hour forecast period was considered. To overcome sample size limitations, we adopted a Leave One Year Out (LOYO) testing scheme, where a model is trained using data from all years except one and then evaluated on the year that is left out. When tested on 2010–2018 operational data using the LOYO scheme, the MLP outperformed other statistical-dynamical models by 9-20%. Additional independent tests in 2019 and 2020 were conducted to simulate real-time operational forecasts, where the MLP model again outperformed the statistical-dynamical models by 5-22% and achieved comparable results as HWFI. The MLP model also correctly predicted more rapid intensification events than all the four operational TC intensity models compared. In the second experiment, we developed a lightweight MLP for 6-hour intensity predictions. When coupled with a synthetic TC track model, the lightweight MLP generated realistic TC intensity distribution in the Atlantic Basin. Therefore, the MLP-based approach has the potential to improve operational TC intensity forecasts, and will also be a viable option for generating synthetic TCs for climate studies.