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Journal articles on the topic 'Sub-Seasonal'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Keerthi, Madhavan Girijakumari, Olivier Aumont, Lester Kwiatkowski, and Marina Levy. "Inadequacies in the representation of sub-seasonal phytoplankton dynamics in Earth system models." Biogeosciences 22, no. 9 (2025): 2163–80. https://doi.org/10.5194/bg-22-2163-2025.

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Abstract. Sub-seasonal phytoplankton dynamics on timescales between 8 d and 3 months significantly contribute to annual fluctuations, making it essential to accurately represent this variability in ocean models to avoid distorting long-term trends. This study assesses the capability of Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) to reproduce sub-seasonal surface ocean phytoplankton variations observed in ocean colour satellite data. Our findings reveal that, unlike sea surface temperature, all models struggle to accurately reproduce the

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2

Soret, A., V. Torralba, N. Cortesi, et al. "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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3

King, Andrew D., Debra Hudson, Eun‐Pa Lim, et al. "Sub‐seasonal to seasonal prediction of rainfall extremes in Australia." Quarterly Journal of the Royal Meteorological Society 146, no. 730 (2020): 2228–49. http://dx.doi.org/10.1002/qj.3789.

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4

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

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5

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

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6

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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7

Tuel, Alexandre, and Olivia Martius. "Weather persistence on sub-seasonal to seasonal timescales: a methodological review." Earth System Dynamics 14, no. 5 (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 pe

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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 (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 th

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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 (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 condition

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10

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 (2018): 503–20. http://dx.doi.org/10.1127/metz/2018/0898.

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11

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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12

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 (2017): 3403–23. http://dx.doi.org/10.1007/s00382-017-3520-5.

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13

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

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14

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

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15

Idrissa, Nkurunziza Fabien, Chun Zhao, Qiuyan Du, et al. "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 Septe

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16

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

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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/10.3189/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

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18

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

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19

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 (2015): 919–35. http://dx.doi.org/10.1007/s00382-015-2879-4.

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20

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 (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 biv

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21

Ray, Richard D. "Technical note: On seasonal variability of the M₂ tide." Ocean Science 18, no. 4 (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 nomenclatu

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22

Ismail, A., M. Riaz, S. Akhtar, et al. "Seasonal variation of aflatoxin B₁ content in dairy feed." Journal of Animal and Feed Sciences 26, no. 1 (2017): 33–37. http://dx.doi.org/10.22358/jafs/69008/2017.

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23

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 (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

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24

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

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25

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

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26

Tiwari, Yogesh K., Tania Guha, Vinu Valsala, et al. "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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27

Orsolini, Y. J., R. Senan, G. Balsamo, et al. "Impact of snow initialization on sub-seasonal forecasts." Climate Dynamics 41, no. 7-8 (2013): 1969–82. http://dx.doi.org/10.1007/s00382-013-1782-0.

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28

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 (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,

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29

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 (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.

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30

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 (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 oscillati

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31

Lainela, Silvie, Erik Jacobs, Stella-Theresa Luik, Gregor Rehder, and Urmas Lips. "Seasonal dynamics and regional distribution patterns of CO₂ and CH₄ in the north-eastern Baltic Sea." Biogeosciences 21, no. 20 (2024): 4495–519. http://dx.doi.org/10.5194/bg-21-4495-2024.

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Abstract. Significant research has been carried out in the last decade to describe the CO2 system dynamics in the Baltic Sea. However, there is a lack of knowledge in this field in the NE Baltic Sea, which is the main focus of the present study. We analysed the physical forcing and hydrographic background in the study year (2018) and tried to elucidate the observed patterns of surface water CO2 partial pressure (pCO2) and methane concentrations (cCH4). Surface water pCO2 and cCH4 were continuously measured during six monitoring cruises onboard R/V Salme, covering the Northern Baltic Proper (NB

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32

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 (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 zonall

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33

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 (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 zonall

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34

SEETHARAM, K. "Impact of Madden-Julian oscillations on the Indian summer monsoon sub-divisional rainfalls." MAUSAM 59, no. 2 (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 M

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35

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

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36

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 (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 pe

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Lindqvist, H., C. W. O'Dell, S. Basu, et al. "Does GOSAT capture the true seasonal cycle of XCO<sub>2</sub>?" Atmospheric Chemistry and Physics Discussions 15, no. 12 (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 o

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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 MgCl<sub>2</sub> hydrates and its application for seasonal heat storage. This study, indicate the validity of the ReaxFF approach for studying MgCl<sub>2</sub> hydrates and provide important atomistic-scale insight of reaction kinetics and H<sub>2</sub>O transport.

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Zhang, Qian, Zhenxing Shen, Yali Lei, 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<sub>2.5</sub>) samples were collected to provide insight into the seasonal variations of the optical properties and source profiles of PM<sub>2.5</sub> black carbon (BC) and brown carbon (BrC) in Xi'an, China.

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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 (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-season

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41

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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42

Bradley, Alexander C., Barbara Dix, Fergus Mackenzie, J. Pepijn Veefkind, and Joost A. de Gouw. "Deep transfer learning method for seasonal TROPOMI XCH₄ albedo correction." Atmospheric Measurement Techniques 18, no. 7 (2025): 1675–87. https://doi.org/10.5194/amt-18-1675-2025.

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Abstract. The retrieval of methane from satellite measurements is sensitive to the reflectance of the surface, and in many regions, especially those with agriculture, surface reflectance depends on the season. Existing corrections for this effect do not take into account a changing relationship between reflectance and the methane correction value over time. It is an important issue to consider, as agricultural emissions of methane are significant and other sources, like oil and gas production, are also often located in agricultural lands. In this work, we use a set of 12 monthly machine learni

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43

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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44

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 (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

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45

Permyakova, Anastasia, Sujing Wang, Emilie Courbon, et al. "Design of salt–metal organic framework composites for seasonal heat storage applications." Journal of Materials Chemistry A 5, no. 25 (2017): 12889–98. http://dx.doi.org/10.1039/c7ta03069j.

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Li, Yunyun, Yi Huang, Jingjing Fan, et al. "Meteorological and Hydrological Drought Risks under Future Climate and Land-Use-Change Scenarios in the Yellow River Basin." Atmosphere 14, no. 11 (2023): 1599. http://dx.doi.org/10.3390/atmos14111599.

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The primary innovation of this study lies in the development of an integrated modeling framework that combines downscaled climate projections, land-use-change simulations, and copula-based risk analysis. This framework allows for the assessment of localized sub-seasonal and seasonal drought hazards under future scenarios. The BCC-CSM1-1 climate model projections from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset are utilized to represent the future climate for 2025–2060 under RCP 4.5 and 8.5 scenarios. The CA-Markov model is employed to predict future land-use-

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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 (2003): 157–73. http://dx.doi.org/10.1127/0941-2948/2003/0012-0157.

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Yang, Baoshan, Hui Wang, Yingkui Jiang, Fang Dong, Xinhua He та Xiaoshuang Lai. "Combing δ15N and δ18O to identify the distribution and the potential sources of nitrate in human-impacted watersheds, Shandong, China". RSC Advances 8, № 41 (2018): 23199–205. http://dx.doi.org/10.1039/c8ra04364g.

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NO<sub>3</sub><sup>−</sup> showed seasonal and spatial patterns in two human-impacted watersheds. NO<sub>3</sub><sup>−</sup> is primarily from manure/sewage according to δ<sup>15</sup>N and δ<sup>18</sup>O. Microbial nitrification took place in the NO<sub>3</sub><sup>−</sup> of manure/sewage and soil nitrate.

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Jesswein, Markus, Rafael P. Fernandez, Lucas Berná, et al. "Global seasonal distribution of CH₂Br₂ and CHBr₃ in the upper troposphere and lower stratosphere." Atmospheric Chemistry and Physics 22, no. 22 (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 mixi

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Boas, Theresa, Heye Reemt Bogena, Dongryeol Ryu, Harry Vereecken, Andrew Western, and Harrie-Jan Hendricks Franssen. "Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach." Hydrology and Earth System Sciences 27, no. 16 (2023): 3143–67. http://dx.doi.org/10.5194/hess-27-3143-2023.

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Abstract. Long-range weather forecasts provide predictions of atmospheric, ocean and land surface conditions that can potentially be used in land surface and hydrological models to predict the water and energy status of the land surface or in crop growth models to predict yield for water resources or agricultural planning. However, the coarse spatial and temporal resolutions of available forecast products have hindered their widespread use in such modelling applications, which usually require high-resolution input data. In this study, we applied sub-seasonal (up to 4 months) and seasonal (7 mo

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