Sea surface temperature variations partitioned through multiple seasonal cycles
by Rameshan Kallummal
Low-frequency changes in the tropical Indian Ocean surface temperature have previously been investigated in the context of the Indian Ocean basin-wide (IOBM) and dipole (IOD) modes. The IOBM and IOD are the leading eigenmodes estimated from a traditional anomaly of SST. This approach ignores the possibility of multiple seasonal cycles (SCs) having different geographic patterns and interannually modulating amplitudes. The analyses presented here are anchored on the four sets of multivariate seasonal cycles independently extracted from the monthly observations of sea surface temperature (SST), surface wind, and surface pressure variations. We show that the secular warming, encapsulated by the monotonic variations of the first SC of SST (SST–SC1), differs from the previous linear trend patterns and has the most significant variance in the Indian Ocean Warm Pool (IOWP). Hence, these warming tendencies quantify the monotonic expansion rates of IOWP. The most significant interannual responses of Indian Ocean SST to remote forces (such as El Niño and La Niña) are also captured by SST–SC1. Unlike the traditional IOBM but similar to SST–SC1’s secular warming, these remotely forced interannual signals also have considerable variances in IOWP. The interannual variations in SST’s third seasonal cycle (i.e., SST–SC3) inherit SST–SC3’s dipole pattern but diverge from classical IOD in many aspects and are predominantly controlled by local processes. However, they are insufficient to account for the total interannual signals on their own. The collective interannual variations of four seasonal cycles—with significant variances off Africa’s eastern shores—demonstrate basin-wide unipolar patterns. Hence, SST interannual signals in the north-western Indian Ocean and the constantly growing warming in the IOWP influence climate and weather over countries surrounding the Indian Ocean. Thus, this study offers a simple way to separate three types of climate signals: secular, internal, and remotely induced climate fluctuations.
Weather integrated malaria prediction system using Bayesian structural time series model for northeast states of India
by Hariprasad Vavilala, Nikhila Yaladanda, Phani Krishna Kondeti, Rafiq Unissa, Rajasekhar Mopuri, Krushna Chandra Gouda, Kantha Rao Bhimala, Madhusudhan Rao Kadiri, Suryanaryana Murty Upadhyayula & Srinivasa Rao Mutheneni
Malaria is an endemic disease in India and targeted to eliminate by the year 2030. The present study is aimed at understanding the epidemiological patterns of malaria transmission dynamics in Assam and Arunachal Pradesh followed by the development of a malaria prediction model using monthly climate factors. A total of 144,055 cases in Assam during 2011–2018 and 42,970 cases in Arunachal Pradesh were reported during the 2011–2019 period observed, and Plasmodium falciparum (74.5%) was the most predominant parasite in Assam, whereas Plasmodium vivax (66%) in Arunachal Pradesh. Malaria transmission showed a strong seasonal variation where most of the cases were reported during the monsoon period (Assam, 51.9%, and Arunachal Pradesh, 53.6%). Similarly, the malaria incidence was highest in the male population in both states (Asam, 55.75%, and Arunachal Pradesh, 51.43%), and the disease risk is also higher among the > 15 years age group (Assam, 61.7%, and Arunachal Pradesh, 67.9%). To predict the malaria incidence, Bayesian structural time series (BSTS) and Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMAX) models were implemented. A statistically significant association between malaria cases and climate variables was observed. The most influencing climate factors are found to be maximum and mean temperature with a 6-month lag, and it showed a negative association with malaria incidence. The BSTS model has shown superior performance on the optimal auto-correlated dataset (OAD) which contains auto-correlated malaria cases, cross-correlated climate variables besides malaria cases in both Assam (RMSE, 0.106; MAE, 0.089; and SMAPE, 19.2%) and Arunachal Pradesh (RMSE, 0.128; MAE, 0.122; and SMAPE, 22.6%) than the SARIMAX model. The findings suggest that the predictive performance of the BSTS model is outperformed, and it may be helpful for ongoing intervention strategies by governmental and nongovernmental agencies in the northeast region to combat the disease effectively.
Source: https://doi.org/10.1007/s11356-022-20642-y
Sensor orientation and noise analysis of the Kashmir-Zanskar seismic network: an appraisal from 2014 to 2020
by Ramees R. Mir, Imtiyaz A. Parvez, Gabi Laske & Vinod K. Gaur
This paper presents estimated misorientation angles of broadband seismic sensors of the Kashmir-Zanskar network and their effects on anisotropy determinations and great-circle-path deviations. The misorientations were calculated from the difference between backazimuths of Rayleigh waves and those of the great-circle-arcs connecting the source and receiver. Waveforms of global Rayleigh waves extracted from the records of 13 broadband seismographs in the Kashmir-Zanskar region of Northwestern Himalaya, and 3 others around the region, were used to evaluate misorientation errors in each of these sensor installations. Three of the 16 were found to have orientation errors between ± 5 and 10° with respect to the geographic north, 4 between 10 and 16° and the remainder with <5°. These misalignments had resulted in leakage of a substantial amount of energy in the transverse component receiver functions which, after correction, led to sharper amplitudes and polarities. Indeed, the SKS-derived azimuths of the fast component were found to be quite sensitive to instrument misalignment, suffering ~ 16° shift from a ~ 15.5° error in orientation. A notable observation revealed by misalignment corrections was the substantial, up to 20°, off-great-circle arc deviations even along shorter path arrivals from regional events, offering a qualitative ordination of the region’s heterogeneities. The paper also presents probability distribution functions of the estimated power spectral density of ambient noise at each station compared with global high and low-noise models and near-source earthquake models. The results provide a first-order assessment of small earthquake detection capability of this network, down to M1.0, also confirmed by some of the smallest events located.
Source: https://doi.org/10.1007/s10950-022-10090-z
Projected engulfment of tropical Indian Ocean by anthropogenical warmpool
by Rameshan Kallummal
The spatio-temporal characteristics of the observed warming of the Indian Ocean (IO) surface temperature and its statistical link to the IO Warm Pool (IOWP) are still unclear. This study discriminates the basin-wide monotonic warming mode of the IO surface from the internally and remotely forced variability. The trend pattern reported in this study reveals a radically different warming signal. It shows that the monotonic warming in the observed sea surface temperature (SST) has the spatial pattern of summer-mean SST and the Warm Pool as its most conspicuous feature. The highest warming (0.17∘C per decade) is in IOWP and not in the other IO regions identified in the previous studies. By 2070, IOWP will cover about 80% of tropical IO at the current latitudinal expansion rates. The mean states of equatorial SST, wind, and surface pressure are shifting towards an endless summer. Irrespective of the season, SST near Indonesia would remain above 31∘C by 2080 and beyond (i.e., more than 2.2∘C rise since 1950). This would substantially increase local rainfall intensity and frequency. Thus, IO is poised to play a more significant role in climate change. We also argue that the basin-wide warming is due to anthropogenic forces’ rectification by coupled processes in ocean-atmosphere mixed layers.
Source: https://doi.org/10.1007/s00382-022-06181-9
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