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Journal articles on the topic 'Sub-Seasonal'

Author: Grafiati

Published: 25 May 2024

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1

Soret, A., V. Torralba, N. Cortesi, I. Christel, Ll Palma, A. Manrique-Suñén, Ll Lledó, N. González-Reviriego, and F. J. Doblas-Reyes. "Sub-seasonal to seasonal climate predictions for wind energy forecasting." Journal of Physics: Conference Series 1222 (May 2019): 012009. http://dx.doi.org/10.1088/1742-6596/1222/1/012009.

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2

King, Andrew D., Debra Hudson, Eun‐Pa Lim, Andrew G. Marshall, Harry H. Hendon, Todd P. Lane, and Oscar Alves. "Sub‐seasonal to seasonal prediction of rainfall extremes in Australia." Quarterly Journal of the Royal Meteorological Society 146, no. 730 (April 13, 2020): 2228–49. http://dx.doi.org/10.1002/qj.3789.

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3

Vitart, Frédéric, and Yuhei Takaya. "Lagged ensembles in sub‐seasonal predictions." Quarterly Journal of the Royal Meteorological Society 147, no. 739 (July 2021): 3227–42. http://dx.doi.org/10.1002/qj.4125.

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4

Levine, Matthew E. "Seasonal Symptoms in the Sub-Arctic." Military Medicine 160, no. 3 (March 1, 1995): 110–14. http://dx.doi.org/10.1093/milmed/160.3.110.

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5

Maddison, Eleanor J., Jennifer Pike, Amy Leventer, and Eugene W. Domack. "Deglacial seasonal and sub-seasonal diatom record from Palmer Deep, Antarctica." Journal of Quaternary Science 20, no. 5 (2005): 435–46. http://dx.doi.org/10.1002/jqs.947.

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6

Tuel, Alexandre, and Olivia Martius. "Weather persistence on sub-seasonal to seasonal timescales: a methodological review." Earth System Dynamics 14, no. 5 (September 13, 2023): 955–87. http://dx.doi.org/10.5194/esd-14-955-2023.

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Abstract. Persistence is an important concept in meteorology. It refers to surface weather or the atmospheric circulation either remaining in approximately the same state (quasi-stationarity) or repeatedly occupying the same state (recurrence) over some prolonged period of time. Persistence can be found at many different timescales; however, sub-seasonal to seasonal (S2S) timescales are especially relevant in terms of impacts and atmospheric predictability. For these reasons, S2S persistence has been attracting increasing attention from the scientific community. The dynamics responsible for persistence and their potential evolution under climate change are a notable focus of active research. However, one important challenge facing the community is how to define persistence from both a qualitative and quantitative perspective. Despite a general agreement on the concept, many different definitions and perspectives have been proposed over the years, among which it is not always easy to find one's way. The purpose of this review is to present and discuss existing concepts of weather persistence, associated methodologies and physical interpretations. In particular, we call attention to the fact that persistence can be defined as a global or as a local property of a system, with important implications in terms of methods and impacts. We also highlight the importance of timescale and similarity metric selection and illustrate some of the concepts using the example of summertime atmospheric circulation over western Europe.

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Seginer, Ido, and Ilyа Ioslovich. "Seasonal Sub-Optimal Environmental Control of Greenhouses." IFAC Proceedings Volumes 30, no. 26 (October 1997): 55–60. http://dx.doi.org/10.1016/s1474-6670(17)41245-6.

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8

Coelho, Caio A. S., Mári A. F. Firpo, and Felipe M. de Andrade. "A verification framework for South American sub-seasonal precipitation predictions." Meteorologische Zeitschrift 27, no. 6 (December 11, 2018): 503–20. http://dx.doi.org/10.1127/metz/2018/0898.

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9

Huang, Li Kun, and Guang Zhi Wang. "Study on Seasonal Characteristics of PM _2.5 in Harbin." Advanced Materials Research 183-185 (January 2011): 1246–49. http://dx.doi.org/10.4028/www.scientific.net/amr.183-185.1246.

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In order to investigate the seasonal characteristic of PM2.5, PM2.5 were collected in four seasons. This study investigates the elemental characteristics of PM2.5. The results show that the distribution trends of Ca, Na, and Mg are consistent and they are the highest in summer, lowest in winter. S is lower in summer and higher in autumn and winter, which is also caused by heating in autumn and winter. Si is higher in winter and lower in autumn, fly ash emissions from coal combustion is the main reason. Zn and K have a significant seasonal variation which is influenced by environmental factors in different seasons. Al and Fe mainly come from industrial emissions and natural emissions, Al is higher in summer and lower in winter, Fe is higher in winter lower in autumn. Cu, Cr, Cd, Ni, Mn, Sr, V, As, and Ba concentrations have lower content in four seasons, which indicates that emissions sources of these elements are more stable.

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10

林, 倩. "Performance of Sub-Seasonal to Seasonal (S2S) Products for Global Precipitation Forecasts." Journal of Water Resources Research 08, no. 06 (2019): 547–56. http://dx.doi.org/10.12677/jwrr.2019.86062.

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11

Moron, Vincent, Andrew W. Robertson, and D. S. Pai. "On the spatial coherence of sub-seasonal to seasonal Indian rainfall anomalies." Climate Dynamics 49, no. 9-10 (January 19, 2017): 3403–23. http://dx.doi.org/10.1007/s00382-017-3520-5.

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12

Kopp, Jérôme, Pauline Rivoire, S. Mubashshir Ali, Yannick Barton, and Olivia Martius. "A novel method to identify sub-seasonal clustering episodes of extreme precipitation events and their contributions to large accumulation periods." Hydrology and Earth System Sciences 25, no. 9 (September 23, 2021): 5153–74. http://dx.doi.org/10.5194/hess-25-5153-2021.

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Abstract. Temporal (serial) clustering of extreme precipitation events on sub-seasonal timescales is a type of compound event. It can cause large precipitation accumulations and lead to floods. We present a novel, count-based procedure to identify episodes of sub-seasonal clustering of extreme precipitation. We introduce two metrics to characterise the prevalence of sub-seasonal clustering episodes and their contribution to large precipitation accumulations. The procedure does not require the investigated variable (here precipitation) to satisfy any specific statistical properties. Applying this procedure to daily precipitation from the ERA5 reanalysis data set, we identify regions where sub-seasonal clustering occurs frequently and contributes substantially to large precipitation accumulations. The regions are the east and northeast of the Asian continent (northeast of China, North and South Korea, Siberia and east of Mongolia), central Canada and south of California, Afghanistan, Pakistan, the southwest of the Iberian Peninsula, and the north of Argentina and south of Bolivia. Our method is robust with respect to the parameters used to define the extreme events (the percentile threshold and the run length) and the length of the sub-seasonal time window (here 2–4 weeks). This procedure could also be used to identify temporal clustering of other variables (e.g. heat waves) and can be applied on different timescales (sub-seasonal to decadal). The code is available at the listed GitHub repository.

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13

Yang, C., P. Yang, S. Liang, and T. Wang. "The effects of illuminance and correlated colour temperature on daytime melatonin levels in undergraduates with sub-syndromal SAD." Lighting Research & Technology 52, no. 6 (November 5, 2019): 722–35. http://dx.doi.org/10.1177/1477153519884097.

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Under the pressure of ensuring student visual performance, this study investigated whether improving the classroom lighting is helpful in relieving the daytime serum melatonin concentration in undergraduates who suffer from sub-syndromal seasonal affective disorder in winter. Two negative control groups (Undergraduates without sub-SAD, lighting conditions 300 lx, 4000K), two positive control groups (undergraduates with sub-seasonal affective disorder, lighting conditions 300 lx, 4000K) and six positive intervention groups (undergraduates with sub-seasonal affective disorder, lighting conditions 1000, 2000, 3000 lx and 4000K and 5000K). There were eight participants in each group (four males and four females). A total of 80 participants took part in 15 successive days of study, and the melatonin was measured every seven days. After comparing participants’ melatonin levels before and after exposure, statistical analysis revealed: (1) Participants with sub-seasonal affective disorder had a higher daytime serum melatonin level than the normal; (2) Increasing the illuminant colour temperature or illuminance in daytime can effectively restrain sub-seasonal affective disorder participants’ daytime serum melatonin secretion; (3) Compared to the 4000K intervention, the 5000K light source affected the participants’ daytime serum melatonin suppression more significantly. These results provide suggestions for classroom lighting in sunless areas from the aspect of melatonin rhythm.

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14

Prodhomme, Chloé, Francisco Doblas-Reyes, Omar Bellprat, and Emanuel Dutra. "Impact of land-surface initialization on sub-seasonal to seasonal forecasts over Europe." Climate Dynamics 47, no. 3-4 (November 4, 2015): 919–35. http://dx.doi.org/10.1007/s00382-015-2879-4.

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15

Ray, Richard D. "Technical note: On seasonal variability of the M₂ tide." Ocean Science 18, no. 4 (July 19, 2022): 1073–79. http://dx.doi.org/10.5194/os-18-1073-2022.

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Abstract. Seasonal variability of the M2 ocean tide can be detected at many ports, perhaps most. Examination of the cluster of tidal constituents residing within the M2 tidal group can shed light on the physical mechanisms underlying seasonality. In the broadest terms these are astronomical, frictional–advective interactions, and climate processes; some induce annual modulations and some semiannual, in amplitude, phase, or both. This note reviews how this occurs and gives an example from each broad category. Phase conventions and their relationship with causal mechanisms, as well as nomenclature, are also addressed.

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16

Ismail, A., M. Riaz, S. Akhtar, S. H. Yoo, S. Park, M. Abid, M. Aziz, and Z. Ahmad. "Seasonal variation of aflatoxin B₁ content in dairy feed." Journal of Animal and Feed Sciences 26, no. 1 (March 21, 2017): 33–37. http://dx.doi.org/10.22358/jafs/69008/2017.

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17

Koh, Gary, and Rachel Jordan. "Sub-surface melting in a seasonal snow cover." Journal of Glaciology 41, no. 139 (1995): 474–82. http://dx.doi.org/10.1017/s002214300003481x.

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AbstractThe ability of solar radiation to penetrate into a snow cover combined with the low thermal conductivity of snow can lead to a sub-surface temperature maximum. This elevated sub-surface temperature allows a layer of wet snow to form below the surface even on days when the air temperature remains sub-freezing. A high-resolution frequency-modulated continuous wave (FMCW) radar has been used to detect the onset of sub-surface melting in a seasonal snow cover. The experimental observation of sub-surface melting is shown to be in good agreement with the predictions of a one-dimensional mass- and energy-balance model. The effects of varying snow characteristics and solar extinction parameters on the sub-surface melt characteristics are investigated using model simulations.

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18

Koh, Gary, and Rachel Jordan. "Sub-surface melting in a seasonal snow cover." Journal of Glaciology 41, no. 139 (1995): 474–82. http://dx.doi.org/10.3189/s002214300003481x.

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AbstractThe ability of solar radiation to penetrate into a snow cover combined with the low thermal conductivity of snow can lead to a sub-surface temperature maximum. This elevated sub-surface temperature allows a layer of wet snow to form below the surface even on days when the air temperature remains sub-freezing. A high-resolution frequency-modulated continuous wave (FMCW) radar has been used to detect the onset of sub-surface melting in a seasonal snow cover. The experimental observation of sub-surface melting is shown to be in good agreement with the predictions of a one-dimensional mass- and energy-balance model. The effects of varying snow characteristics and solar extinction parameters on the sub-surface melt characteristics are investigated using model simulations.

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19

Idrissa, Nkurunziza Fabien, Chun Zhao, Qiuyan Du, Shengfu Lin, Kagabo Safari Abdou, Weichen Liu, and Xiaodong Wang. "Investigating the mechanisms driving the seasonal variations in surface PM<sub>2.5</sub> concentrations over East Africa with the WRF-Chem model." JUSTC 53, no. 5 (2023): 1. http://dx.doi.org/10.52396/justc-2022-0142.

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Most previous studies on surface PM<sub>2.5</sub> concentrations over East Africa focused on short-term in situ observations. In this study, the WRF-Chem model combined with in situ observations is used to investigate the seasonal variation in surface PM<sub>2.5</sub> concentrations over East Africa. WRF-Chem simulations are conducted from April to September 2017. Generally, the simulated AOD is consistent with satellite retrieval throughout the period, and the simulations depicted the seasonal variation in PM<sub>2.5</sub> concentrations from April to September but underestimated the concentrations throughout the period due to the uncertainties in local and regional emissions over the region. The composition analysis of surface PM<sub>2.5</sub> concentrations revealed that the dominant components were OIN and OC, accounting for 80% and 15% of the total concentrations, respectively, and drove the seasonal variation. The analysis of contributions from multiple physical and chemical processes indicated that the seasonal variation in surface PM<sub>2.5</sub> concentrations was controlled by the variation in transport processes, PBL mixing, and dry and wet deposition. The variation in PM<sub>2.5</sub> concentrations from May to July is due to wind direction changes that control the transported biomass burning aerosols from southern Africa, enhanced turbulent mixing of transported aerosols at the upper level to the surface and decreased wet deposition from decreased rainfall from May to July.

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20

Mote, Philip W., Timothy J. Dunkerton, and Hugh C. Pumphrey. "Sub-seasonal variations in lower stratospheric water vapor." Geophysical Research Letters 25, no. 13 (July 1, 1998): 2445–48. http://dx.doi.org/10.1029/98gl51847.

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21

Vitart, Frédéric. "Evolution of ECMWF sub-seasonal forecast skill scores." Quarterly Journal of the Royal Meteorological Society 140, no. 683 (January 9, 2014): 1889–99. http://dx.doi.org/10.1002/qj.2256.

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22

Tiwari, Yogesh K., Tania Guha, Vinu Valsala, Alfonso Saiz Lopez, Carlos Cuevas, Rafael P. Fernandez, and Anoop S. Mahajan. "Understanding atmospheric methane sub-seasonal variability over India." Atmospheric Environment 223 (February 2020): 117206. http://dx.doi.org/10.1016/j.atmosenv.2019.117206.

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23

Orsolini, Y. J., R. Senan, G. Balsamo, F. J. Doblas-Reyes, F. Vitart, A. Weisheimer, A. Carrasco, and R. E. Benestad. "Impact of snow initialization on sub-seasonal forecasts." Climate Dynamics 41, no. 7-8 (May 1, 2013): 1969–82. http://dx.doi.org/10.1007/s00382-013-1782-0.

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24

Oguntunji, A. O., O. A. Oladejo, and K. L. Ayorinde. "Seasonal variation in egg production and mortality of Muscovy ducks (Cairina Moschata)." Biotehnologija u stocarstvu 31, no. 2 (2015): 181–92. http://dx.doi.org/10.2298/bah1502181o.

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Seasonal variation is one of the principal non-genetic factors influencing performance of poultry in tropical environment. This study was conducted to investigate influence of seasonal variation on egg production and incidence of mortality in intensively-reared non-descript Muscovy ducks in Nigeria. Egg production and incidence of mortality in sixty two (62) female Muscovy ducks was studied in a 12-month trial divided into two major seasons: wet (April - September) and dry (October - March) and four sub-seasons: early rainy season (April - June), late rainy season (July - September), early dry season (October - December) and late dry season (January - March). Student?s t-test and Completely Randomized Design was used to analyse seasonal and sub-seasonal effect on performance, respectively. Season and sub-season significantly (P < 0.05) affected egg production; higher egg production was recorded in wet season compared with dry season (16.18% vs. 1.32%). Among sub-seasons, highest egg production was recorded in late rainy season (20.92%) while the least (0.00%) was obtained in late dry season. Conversely, there was no significant (P > 0.05) effect of season and sub-season on mortality. It is evident that seasonal variation had no effect on incidence of mortality but significantly affected egg production of Muscovy duck and the adverse effect is more pronounced in dry season most especially in late dry season.

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25

Pineda-Metz, Santiago E. A., and Américo Montiel. "Seasonal dynamics of meroplankton in a sub-Antarctic fjord (Southern Patagonia, Chile)." Polar Biology 44, no. 5 (March 30, 2021): 875–86. http://dx.doi.org/10.1007/s00300-021-02823-6.

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AbstractKnowledge of seasonal dynamics and composition of meroplankton (larvae of benthic invertebrates) is rather limited for sub-Antarctic regions. We studied the seasonal dynamics of meroplankton in a sub-Antarctic proglacial basin (Gallegos Sound, Chile), by examining changes in the meroplankton community in relation to hydrographic variables along four sampling cruises between early winter 2010 and late winter 2011. The local meroplankton community was composed of 39 larval morphotypes distributed among 11 major taxa, being polychaetes the best represented (15 larvae morphotypes), and bivalve the most abundant. We found distinct seasonal differences in terms of meroplanktonic composition and abundance, with higher abundance and larval morphotype number during austral spring and late winter, and lower in summer and early winter. The pattern observed for meroplankton was directly related to seasonal variations of fluorescence of chlorophyll a and temperature. We found meroplankton abundances lower than those of other sub- and Polar environments. However, meroplanktonic temporal dynamics showed a common pattern for sub- and Polar fjords, suggesting a strong link between benthic spawning and the occurrence of phytoplankton blooms.

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26

Liu, Xiangwen, Tongwen Wu, Song Yang, Tim Li, Weihua Jie, Li Zhang, Zaizhi Wang, et al. "MJO prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center." Climate Dynamics 48, no. 9-10 (August 22, 2016): 3283–307. http://dx.doi.org/10.1007/s00382-016-3264-7.

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27

Giunta, Giuseppe, Alessandro Ceppi, and Raffaele Salerno. "An Extended Analysis of Temperature Prediction in Italy: From Sub-Seasonal to Seasonal Timescales." Forecasting 5, no. 4 (October 13, 2023): 600–615. http://dx.doi.org/10.3390/forecast5040033.

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Earth system predictions, from sub-seasonal to seasonal timescales, remain a challenging task, and the representation of predictability sources on seasonal timescales is a complex work. Nonetheless, advances in technology and science have been making continuous progress in seasonal forecasting. In a previous paper, a performance for temperature prediction by a modelling system named e-kmf® was carried out in comparison with observations and climatology for a year of data; a low level of predictability in the sub-seasonal range, particularly in the second month, was observed over the Italian peninsula. Therefore, in this study, we focus our investigations specifically on the performance between the fifth and the eighth week of temperature forecasts over six years of simulations (2012–2018) to investigate the capability of the weather model to better reproduce the behavior of temperatures in the second month of the forecast. Although some differences in seasons are present, results have globally shown how temperature predictions have the potential to be quite skillful, with an average skill score of about 68%, with climatology used as reference; additionally, an overall anomaly correlation coefficient equal to 0.51 was shown, providing useful information for applications in planning, sales, and supply of natural energy resources.

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KAKISHIMA, Hayato, Yoshitaka EBIE, Akito MURANO, and Hiroshi YAMAZAKI. "EMISSION CHARACTERISTICS OF GREENHOUSE GASES (CH₄·N₂O) CONSIDERING SEASONAL FLUCTUATIONS FROM JOHKASOU." Journal of Japan Society of Civil Engineers, Ser. G (Environmental Research) 74, no. 7 (2018): III_391—III_398. http://dx.doi.org/10.2208/jscejer.74.iii_391.

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OTANI, Sosuke, Junpei MARIKAWA, Taiki KAWASAKI, and Koichi TANAKA. "SEASONAL DYNAMICS OF THE CO₂ FLUX IN BRACKISH SALT MARSH." Journal of Japan Society of Civil Engineers, Ser. B2 (Coastal Engineering) 72, no. 2 (2016): I_1441—I_1446. http://dx.doi.org/10.2208/kaigan.72.i_1441.

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30

Smith, Jacob W., Peter H. Haynes, Amanda C. Maycock, Neal Butchart, and Andrew C. Bushell. "Sensitivity of stratospheric water vapour to variability in tropical tropopause temperatures and large-scale transport." Atmospheric Chemistry and Physics 21, no. 4 (February 18, 2021): 2469–89. http://dx.doi.org/10.5194/acp-21-2469-2021.

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Abstract. Concentrations of water vapour entering the tropical lower stratosphere are primarily determined by conditions that air parcels encounter as they are transported through the tropical tropopause layer (TTL). Here we quantify the relative roles of variations in TTL temperatures and transport in determining seasonal and interannual variations of stratospheric water vapour. Following previous studies, we use trajectory calculations with the water vapour concentration set by the Lagrangian dry point (LDP) along trajectories. To assess the separate roles of transport and temperatures, the LDP calculations are modified by replacing either the winds or the temperatures with those from different years to investigate the wind or temperature sensitivity of water vapour to interannual variations and, correspondingly, with those from different months to investigate the wind or temperature sensitivity to seasonal variations. Both ERA-Interim reanalysis data for the 1999–2009 period and data generated by a chemistry–climate model (UM-UKCA) are investigated. Variations in temperatures, rather than transport, dominate interannual variability, typically explaining more than 70 % of variability, including individual events such as the 2000 stratospheric water vapour drop. Similarly seasonal variation of temperatures, rather than transport, is shown to be the dominant driver of the annual cycle in lower stratospheric water vapour concentrations in both the model and reanalysis, but it is also shown that seasonal variation of transport plays an important role in reducing the seasonal cycle maximum (reducing the annual range by about 30 %). The quantitative role in dehydration of sub-seasonal and sub-monthly Eulerian temperature variability is also examined by using time-filtered temperature fields in the trajectory calculations. Sub-monthly temperature variability reduces annual mean water vapour concentrations by 40 % in the reanalysis calculation and 30 % in the model calculation. As with other aspects of dehydration, simple Eulerian measures of variability are not sufficient to quantify the implications for dehydration, and the Lagrangian sampling of the variability must be taken into account. These results indicate that, whilst capturing seasonal and interannual variation of temperature is a major factor in modelling realistic stratospheric water vapour concentrations, simulation of seasonal variation of transport and of sub-seasonal and sub-monthly temperature variability are also important and cannot be ignored.

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Lindqvist, H., C. W. O'Dell, S. Basu, H. Boesch, F. Chevallier, N. Deutscher, L. Feng, et al. "Does GOSAT capture the true seasonal cycle of XCO<sub>2</sub>?" Atmospheric Chemistry and Physics Discussions 15, no. 12 (June 17, 2015): 16461–503. http://dx.doi.org/10.5194/acpd-15-16461-2015.

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Abstract. The seasonal cycle accounts for a dominant mode of total column CO2 (XCO2) annual variability and is connected to CO2 uptake and release; it thus represents an important variable to accurately measure from space. We quantitatively evaluate the XCO2 seasonal cycle of the Greenhouse Gases Observing Satellite (GOSAT) observations from the Atmospheric CO2 Observations from Space (ACOS) retrieval system, and compare average regional seasonal cycle features to those directly measured by the Total Carbon Column Observing Network (TCCON). We analyze the mean seasonal cycle amplitude, dates of maximum and minimum XCO2, as well as the regional growth rates in XCO2 through the fitted trend over several years. We find that GOSAT generally captures the seasonal cycle amplitude within 1.0 ppm accuracy compared to TCCON, except in Europe, where the difference exceeds 1.0 ppm at two sites, and the amplitude captured by GOSAT is generally shallower compared to TCCON. This bias over Europe is not as large for the other GOSAT retrieval algorithms (NIES v02.21, RemoTeC v2.35, UoL v5.1, and NIES PPDF-S v.02.11), although they have significant biases at other sites. The ACOS bias correction was found to partially explain the shallow amplitude over Europe. The impact of the TCCON retrieval version, co-location method, and aerosol changes in the ACOS algorithm were also tested, and found to be few tenths-of-a-ppm and mostly non-systematic. We find generally good agreement in the date of minimum XCO2 between ACOS and TCCON, but ACOS generally infers a date of maximum XCO2 2–3 weeks later than TCCON. We further analyze the latitudinal dependence of the seasonal cycle amplitude throughout the Northern Hemisphere, and compare the dependence to that predicted by current optimized models that assimilate in-situ measurements of CO2. In the zonal averages, GOSAT agrees with the models to within 1.4 ppm, depending on the model and latitude. We also show that the seasonal cycle of XCO2 depends on longitude especially at the mid-latitudes: the amplitude of GOSAT XCO2 doubles from West US to East Asia at 45–50° N, which is only partially shown by the models. In general, we find that model-to-model differences can be larger than GOSAT-to-model differences. These results suggest that GOSAT retrievals of the XCO2 seasonal cycle may be sufficiently accurate to evaluate land surface models in regions with significant discrepancies between the models.

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DASTIDAR, AVIK GHOSH, SARBARI GHOSH, U. K. DE, and S. K. GHOSH. "Statistical analysis of monsoon rainfall distribution over West Bengal, India." MAUSAM 61, no. 4 (November 27, 2021): 487–98. http://dx.doi.org/10.54302/mausam.v61i4.884.

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Seasonal, monthly and daily rainfall characteristics of meteorological sub-divisions of Sub Himalayan West Bengal (SHWB) and Gangetic West Bengal (GWB) have been studied using rainfall data of 23 stations of India Meteorological Department (IMD) over the state of West Bengal. The two subdivisions have distinctive characteristics, though two stations lying in the plain region of SHWB have behaviour more alike the stations of GWB. Krishnagar is a station with least seasonal rainfall in the entire state. Kurtosis and Skewness of the seasonal rainfall distribution have been studied and found that, for most of the stations they lie within reasonable limits. From the time series analysis, it is found that the seasonal rainfall has no trend.

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Paeth, Heiko, and Andreas Hense. "Seasonal forecast of sub-sahelian rainfall using cross validated model output statistics." Meteorologische Zeitschrift 12, no. 3 (June 27, 2003): 157–73. http://dx.doi.org/10.1127/0941-2948/2003/0012-0157.

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34

He, Sijie, Xinyan Li, Timothy DelSole, Pradeep Ravikumar, and Arindam Banerjee. "Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 169–77. http://dx.doi.org/10.1609/aaai.v35i1.16090.

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Sub-seasonal forecasting (SSF) focuses on predicting key variables such as temperature and precipitation on the 2-week to 2-month time scale. Skillful SSF would have immense societal value in such areas as agricultural productivity, water resource management, and emergency planning for extreme weather events. However, SSF is considered more challenging than either weather prediction or even seasonal prediction, and is still a largely understudied problem. In this paper, we carefully investigate 10 Machine Learning (ML) approaches to sub-seasonal temperature forecasting over the contiguous U.S. on the SSF dataset we collect, including a variety of climate variables from the atmosphere, ocean, and land. Because of the complicated atmosphere-land-ocean couplings and the limited amount of good quality observational data, SSF imposes a great challenge for ML despite the recent advances in various domains. Our results indicate that suitable ML models, e.g., XGBoost, to some extent, capture the predictability on sub-seasonal time scales and can outperform the climatological baselines, while Deep Learning (DL) models barely manage to match the best results with carefully designed architecture. Besides, our analysis and exploration provide insights on important aspects to improve the quality of sub-seasonal forecasts, e.g., feature representation and model architecture. The SSF dataset and code are released with this paper for use by the broader research community.

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Jesswein, Markus, Rafael P. Fernandez, Lucas Berná, Alfonso Saiz-Lopez, Jens-Uwe Grooß, Ryan Hossaini, Eric C. Apel, et al. "Global seasonal distribution of CH₂Br₂ and CHBr₃ in the upper troposphere and lower stratosphere." Atmospheric Chemistry and Physics 22, no. 22 (November 25, 2022): 15049–70. http://dx.doi.org/10.5194/acp-22-15049-2022.

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Abstract. Bromine released from the decomposition of short-lived brominated source gases contributes as a sink of ozone in the lower stratosphere. The two major contributors are CH2Br2 and CHBr3. In this study, we investigate the global seasonal distribution of these two substances, based on four High Altitude and Long Range Research Aircraft (HALO) missions, the HIAPER Pole-to-Pole Observations (HIPPO) mission, and the Atmospheric Tomography (ATom) mission. Observations of CH2Br2 in the free and upper troposphere indicate a pronounced seasonality in both hemispheres, with slightly larger mixing ratios in the Northern Hemisphere (NH). Compared to CH2Br2, CHBr3 in these regions shows larger variability and less clear seasonality, presenting larger mixing ratios in winter and autumn in NH midlatitudes to high latitudes. The lowermost stratosphere of SH and NH shows a very similar distribution of CH2Br2 in hemispheric spring with differences well below 0.1 ppt, while the differences in hemispheric autumn are much larger with substantially smaller values in the SH than in the NH. This suggests that transport processes may be different in both hemispheric autumn seasons, which implies that the influx of tropospheric air (“flushing”) into the NH lowermost stratosphere is more efficient than in the SH. The observations of CHBr3 support the suggestion, with a steeper vertical gradient in the upper troposphere and lower stratosphere in SH autumn than in NH autumn. However, the SH database is insufficient to quantify this difference. We further compare the observations to model estimates of TOMCAT (Toulouse Off-line Model of Chemistry And Transport) and CAM-Chem (Community Atmosphere Model with Chemistry, version 4), both using the same emission inventory of Ordóñez et al. (2012). The pronounced tropospheric seasonality of CH2Br2 in the SH is not reproduced by the models, presumably due to erroneous seasonal emissions or atmospheric photochemical decomposition efficiencies. In contrast, model simulations of CHBr3 show a pronounced seasonality in both hemispheres, which is not confirmed by observations. The distributions of both species in the lowermost stratosphere of the Northern and Southern hemispheres are overall well captured by the models with the exception of southern hemispheric autumn, where both models present a bias that maximizes in the lowest 40 K above the tropopause, with considerably lower mixing ratios in the observations. Thus, both models reproduce equivalent flushing in both hemispheres, which is not confirmed by the limited available observations. Our study emphasizes the need for more extensive observations in the SH to fully understand the impact of CH2Br2 and CHBr3 on lowermost-stratospheric ozone loss and to help constrain emissions.

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Xie, Xiao, Ping Liang, and Qiwen Qian. "Sub-Seasonal Prediction of Sea-Gale Processes in the Yangtze River Estuary of China." Atmosphere 14, no. 4 (April 5, 2023): 682. http://dx.doi.org/10.3390/atmos14040682.

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The sea-gale process (SGP) is a significant and disastrous weather event for the marine industry. However, the sub-seasonal predictability of SGP remains unclear. In this study, we investigate the influence of low-frequency oscillation on SGP in the Yangtze River estuary from November to April, and its implications for sub-seasonal prediction. We noted that SGPs have a close relationship with the 10~30 day low-frequency component of the 10-m wind speed in the Yangtze River estuary, and typically occur during the peak phase of the low-frequency oscillation. The 10~30 day low-frequency oscillation of 10-m wind was found to be linked to the eastward propagation of extratropical Rossby waves from the North Atlantic across Europe to East Asia. This Rossby wave leads to the low-frequency oscillation of the Siberian high pressure and Japan Sea low pressure, which is indicative of the 10~30 day low-frequency oscillations of the 10-m wind speed in the Yangtze River Estuary. A sea-gale process index (SGPI) was constructed based on the low-frequency oscillation of the Siberian high and the Japan Sea low in order to predict SGPs at the sub-seasonal time scale. Hindcast and real-time forecasts showed that 2/3 of SGPs can be predicted with a leading time of 10~30 days, and that good sub-seasonal predictions of SGPs are connected with strong low-frequency oscillations at the initial forecast time. Therefore, SGPI can be adopted for the sub-seasonal prediction of SGPs in the Yangtze River Estuary.

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37

Atkinson, C. P., H. L. Bryden, J. J.-M. Hirschi, and T. Kanzow. "On the seasonal cycles and variability of Florida Straits, Ekman and Sverdrup transports at 26° N in the Atlantic Ocean." Ocean Science 6, no. 4 (October 1, 2010): 837–59. http://dx.doi.org/10.5194/os-6-837-2010.

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Abstract. Since April 2004 the RAPID array has made continuous measurements of the Atlantic Meridional Overturning Circulation (AMOC) at 26° N. Two key components of this system are Ekman transport zonally integrated across 26° N and western boundary current transport in the Florida Straits. Whilst measurements of the AMOC as a whole are somewhat in their infancy, this study investigates what useful information can be extracted on the variability of the Ekman and Florida Straits transports using the decadal timeseries already available. Analysis is also presented for Sverdrup transports zonally integrated across 26° N. The seasonal cycles of Florida Straits, Ekman and Sverdrup transports are quantified at 26° N using harmonic analysis of annual and semi-annual constituents. Whilst Sverdrup transport shows clear semi-annual periodicity, calculations of seasonal Florida Straits and Ekman transports show substantial interannual variability due to contamination by variability at non-seasonal frequencies; the mean seasonal cycle for these transports only emerges from decadal length observations. The Florida Straits and Ekman mean seasonal cycles project on the AMOC with a combined peak-to-peak seasonal range of 3.5 Sv. The combined seasonal range for heat transport is 0.40 PW. The Florida Straits seasonal cycle possesses a smooth annual periodicity in contrast with previous studies suggesting a more asymmetric structure. No clear evidence is found to support significant changes in the Florida Straits seasonal cycle at sub-decadal periods. Whilst evidence of wind driven Florida Straits transport variability is seen at sub-seasonal and annual periods, a model run from the 1/4° eddy-permitting ocean model NEMO is used to identify an important contribution from internal oceanic variability at sub-annual and interannual periods. The Ekman transport seasonal cycle possesses less symmetric structure, due in part to different seasonal transport regimes east and west of 50 to 60° W. Around 60% of non-seasonal Ekman transport variability occurs in phase section-wide at 26° N and is related to the NAO, whilst Sverdrup transport variability is more difficult to decompose.

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38

Atkinson, C. P., H. L. Bryden, J. J. M. Hirschi, and T. Kanzow. "On the variability of Florida Straits and wind driven transports at 26° N in the Atlantic Ocean." Ocean Science Discussions 7, no. 2 (April 29, 2010): 919–71. http://dx.doi.org/10.5194/osd-7-919-2010.

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Abstract. Since April 2004 the RAPID array has made continuous measurements of the Atlantic Meridional Overturning Circulation (AMOC) at 26° N. Two key components of this system are Ekman transport zonally integrated across 26° N and western boundary current transport in the Florida Straits. Whilst measurements of the AMOC as a whole are somewhat in their infancy, this study investigates what useful information can be extracted on the variability of the Ekman and Florida Straits transports using the decadal timeseries already available. Analysis is also presented for Sverdrup transports zonally integrated across 26° N. The seasonal cycles of Florida Straits, Ekman and Sverdrup transports are quantified at 26° N using harmonic analysis of annual and semi-annual constituents. Whilst Sverdrup transport shows clear semi-annual periodicity, calculations of seasonal Florida Straits and Ekman transports show substantial interannual variability due to variability at non-seasonal frequencies; the mean seasonal cycle for these transports only emerges from decadal length observations. The Florida Straits and Ekman mean seasonal cycles project on the AMOC with a combined peak-to-peak seasonal range of 3.5 Sv. The combined seasonal range for heat transport is 0.40 PW. The Florida Straits seasonal cycle possesses a smooth annual periodicity in contrast with previous studies suggesting a more asymmetric structure. No clear evidence is found to support significant changes in the Florida Straits seasonal cycle at sub-decadal periods. Whilst evidence of wind driven Florida Straits transport variability is seen at sub-seasonal and annual periods, model runs from the 1/4° eddy-permitting ocean model NEMO are used to identify an important contribution from internal oceanic variability at sub-annual and interannual periods. The Ekman transport seasonal cycle possesses less symmetric structure, due in part to different seasonal transport regimes east and west of 50 to 60° W. Around 60% of non-seasonal Ekman transport variability occurs in phase section-wide at 26° N and is related to the NAO, whilst Sverdrup transport variability is more difficult to decompose.

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39

Song, Jinming, Xuegang Li, Lifeng Niu, Huamao Yuan, Ning Li, and Xuelu Gao. "Role of the Jiaozhou Bay as a source/sink of CO₂ over a seasonal cycle." Scientia Marina 71, no. 3 (July 30, 2007): 441–50. http://dx.doi.org/10.3989/scimar.2007.71n3441.

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SEETHARAM, K. "Impact of Madden-Julian oscillations on the Indian summer monsoon sub-divisional rainfalls." MAUSAM 59, no. 2 (November 27, 2021): 195–210. http://dx.doi.org/10.54302/mausam.v59i2.1251.

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Indian summer monsoon rainfall exhibits inter-seasonal variations in the time scales of 2-7 years which are linked to quasi-biennial oscillations and El nino-Southern Oscillation phenomenon and also intra-seasonal variations in the time-scale of 30-60 days which are linked to activity of MJO which emerged as a dominant mode of intra-seasonal oscillations of Indian summer monsoon rainfall in addition to the other modes of low frequency oscillations. In this scenario, the inter and intra seasonal variability of 29 meteorological sub-divisional rainfalls has been investigated by correlating the MJO indices at 10 different longitudes covering Indian, Pacific and Atlantic Oceans with cumulative sub-divisional summer monsoon rainfall (1979 – 2000). The results were discussed.

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41

Huss, Matthias, Leo Sold, Martin Hoelzle, Mazzal Stokvis, Nadine Salzmann, Daniel Farinotti, and Michael Zemp. "Towards remote monitoring of sub-seasonal glacier mass balance." Annals of Glaciology 54, no. 63 (2013): 75–83. http://dx.doi.org/10.3189/2013aog63a427.

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AbstractThis study presents a method that allows continuous monitoring of mass balance for remote or inaccessible glaciers, based on repeated oblique photography. Hourly to daily pictures from two automatic cameras overlooking two large valley glaciers in the Swiss Alps are available for eight ablation seasons (2004–11) in total. We determine the fraction of snow-covered glacier surface from orthorectified and georeferenced images and combine this information with simple accumulation and melt modelling using meteorological data. By applying this approach, the evolution of glacier-wide mass balance throughout the ablation period can be directly calculated, based on terrestrial remote-sensing data. Validation against independent in situ mass-balance observations indicates good agreement. Our methodology has considerable potential for the remote determination of mountain glacier mass balance at high temporal resolution and could be applied using both repeated terrestrial and air-/spaceborne observations.

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42

Bhat, G. S. "The Indian drought of 2002—a sub-seasonal phenomenon?" Quarterly Journal of the Royal Meteorological Society 132, no. 621 (October 2006): 2583–602. http://dx.doi.org/10.1256/qj.05.13.

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43

Schmitt Quedi, Erik, and Fernando Mainardi Fan. "Sub seasonal streamflow forecast assessment at large-scale basins." Journal of Hydrology 584 (May 2020): 124635. http://dx.doi.org/10.1016/j.jhydrol.2020.124635.

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44

Bekele-Biratu, Endalkachew, Wassila M. Thiaw, and Diriba Korecha. "Sub-seasonal variability of the Belg rains in Ethiopia." International Journal of Climatology 38, no. 7 (March 14, 2018): 2940–53. http://dx.doi.org/10.1002/joc.5474.

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45

Li, Yuan, Zhiyong Wu, Hai He, and Guihua Lu. "Deterministic and Probabilistic Evaluation of Sub-Seasonal Precipitation Forecasts at Various Spatiotemporal Scales over China during the Boreal Summer Monsoon." Atmosphere 12, no. 8 (August 15, 2021): 1049. http://dx.doi.org/10.3390/atmos12081049.

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Skillful sub-seasonal precipitation forecasts can provide valuable information for both flood and drought disaster mitigations. This study evaluates both deterministic and probabilistic sub-seasonal precipitation forecasts of ECMWF, ECCC, and UKMO models derived from the Sub-seasonal to Seasonal (S2S) Database at various spatiotemporal scales over China during the boreal summer monsoon. The Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2), is used as the reference dataset to evaluate the forecast skills of the models. The results suggest that skillful deterministic sub-seasonal precipitation forecasts are found when the lead time is within 2 weeks. The deterministic forecast skills reduce quickly when the lead time is beyond 2 weeks. Positive ranked probability skill scores (RPSS) are only found when the lead time is within 2 weeks for probabilistic forecasts as well. Multimodel ensembling helps to improve forecast skills by removing large negative skill scores in northwestern China. The forecast skills are also improved at larger spatial scales or longer temporal scales. However, the improvement is only observed for certain regions where the predictable low frequency signals remain at longer lead times. The composite analysis suggests that both the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO) have an impact on weekly precipitation variability over China. The forecast skills are found to be enhanced during active ENSO and MJO phases. In particular, the forecast skills are found to be enhanced during active MJO phases.

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46

Pathak, Amar Deep, Silvia Nedea, Adri C. T. van Duin, Herbert Zondag, Camilo Rindt, and David Smeulders. "Reactive force field development for magnesium chloride hydrates and its application for seasonal heat storage." Physical Chemistry Chemical Physics 18, no. 23 (2016): 15838–47. http://dx.doi.org/10.1039/c6cp02762h.

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We present the development of the ReaxFF of MgCl₂ hydrates and its application for seasonal heat storage. This study, indicate the validity of the ReaxFF approach for studying MgCl₂ hydrates and provide important atomistic-scale insight of reaction kinetics and H₂O transport.

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Zhang, Qian, Zhenxing Shen, Yali Lei, Tian Zhang, Yaling Zeng, Zhi Ning, Jian Sun, et al. "Optical properties and source identification of black carbon and brown carbon: comparison of winter and summer haze episodes in Xi'an, Northwest China." Environmental Science: Processes & Impacts 21, no. 12 (2019): 2058–69. http://dx.doi.org/10.1039/c9em00320g.

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Summer and winter fine particulate matter (PM_2.5) samples were collected to provide insight into the seasonal variations of the optical properties and source profiles of PM_2.5 black carbon (BC) and brown carbon (BrC) in Xi'an, China.

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Zhao, Tongtiegang, Andrew Schepen, and Q. J. Wang. "Ensemble forecasting of sub-seasonal to seasonal streamflow by a Bayesian joint probability modelling approach." Journal of Hydrology 541 (October 2016): 839–49. http://dx.doi.org/10.1016/j.jhydrol.2016.07.040.

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49

Hethke, Manja, Franz T. Fürsich, Baoyu Jiang, and Yanhong Pan. "Seasonal to sub-seasonal palaeoenvironmental changes in Lake Sihetun (Lower Cretaceous Yixian Formation, NE China)." International Journal of Earth Sciences 102, no. 1 (July 11, 2012): 351–78. http://dx.doi.org/10.1007/s00531-012-0799-7.

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Lin, Y. C., and J. J. Kao. "Effects of seasonal variation in precipitation on estimation of non-point source pollution." Water Science and Technology 47, no. 7-8 (April 1, 2003): 299–304. http://dx.doi.org/10.2166/wst.2003.0702.

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The extent of nonpoint source pollution (NPSP) generated from upstream catchment areas of a reservoir is normally estimated based on a design rainfall. However, rainfall generally varies in different seasons. This seasonal change may significantly influence the estimation of runoff volume and associated NPSP arising within a reservoir watershed. The NPSP management, reservoir operation, and water treatment operation strategies developed based on the estimation will also be altered. This study analyzes the effects of monthly and seasonal variation in precipitation on the estimation of pollution levels from non-point sources in a reservoir watershed. The area studied is the Derchi reservoir watershed located in central Taiwan. This watershed is subdivided into twenty-six sub-watersheds, and the Thiessen method is used to determine the rainfall intensity in each sub-watershed. Runoff pattern and NPSP contributions for each sub-watershed are estimated using the AGNPS model. Results show significant seasonal variation in precipitation; estimated NPSP loads likewise vary significantly over time. Seven- to one hundred-fold differences among monthly and seasonal estimations of phosphorus and sediment loads are observed.

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