scholarly journals Variations in Cloud Cover and Cloud Types over the Ocean from Surface Observations, 1954–2008

2011 ◽  
Vol 24 (22) ◽  
pp. 5914-5934 ◽  
Author(s):  
Ryan Eastman ◽  
Stephen G. Warren ◽  
Carole J. Hahn
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Negative Correlation ◽  
Cloud Cover ◽  
Sea Surface ◽  
Interannual Variations ◽  
Total Cloud Cover ◽  
Marine Stratus ◽  
Long Term Variations

Abstract Synoptic weather observations from ships throughout the World Ocean have been analyzed to produce a climatology of total cloud cover and the amounts of nine cloud types. About 54 million observations contributed to the climatology, which now covers 55 years from 1954 to 2008. In this work, interannual variations of seasonal cloud amounts are analyzed in 10° grid boxes. Long-term variations O(5–10 yr), coherent across multiple latitude bands, remain present in the updated cloud data. A comparison to coincident data on islands indicates that the coherent variations are probably spurious. An exact cause for this behavior remains elusive. The globally coherent variations are removed from the gridbox time series using a Butterworth filter before further analysis. Before removing the spurious variation, the global average time series of total cloud cover over the ocean shows low-amplitude, long-term variations O(2%) over the 55-yr span. High-frequency, year-to-year variation is seen O(1%–2%). Among the cloud types, the most widespread and consistent relationship is found for the extensive marine stratus and stratocumulus clouds (MSC) over the eastern parts of the subtropical oceans. Substantiating and expanding upon previous work, strong negative correlation is found between MSC and sea surface temperature (SST) in the eastern North Pacific, eastern South Pacific, eastern South Atlantic, eastern North Atlantic, and the Indian Ocean west of Australia. By contrast, a positive correlation between cloud cover and SST is seen in the central Pacific. High clouds show a consistent low-magnitude positive correlation with SST over the equatorial ocean. In regions of persistent MSC, time series show decreasing MSC amount. This decrease could be due to further spurious variation within the data. However, the decrease combined with observed increases in SST and the negative correlation between marine stratus and sea surface temperature suggests a positive cloud feedback to the warming sea surface. The observed decrease of MSC has been partly but not completely offset by increasing cumuliform clouds in these regions; a similar decrease in stratiform and increase in cumuliform clouds had previously been seen over land. Interannual variations of cloud cover in the tropics show strong correlation with an ENSO index.

scholarly journals ANALISIS KORELASI CURAH HUJAN DAN SUHU PERMUKAAN LAUT WILAYAH INDONESIA, SERTA IMPLIKASINYA UNTUK PRAKIRAAN CURAH HUJAN (STUDI KASUS KABUPATEN CILACAP) (CORRELATION ANALYSIS OF RAINFALL AND INDONESIA SEA SURFACE TEMPERATURE, AND ITS ...

Agromet ◽  
2007 ◽  
Vol 21 (2) ◽  
pp. 46 ◽  
Author(s):  
W. Estiningtyas ◽  
F. Ramadhani ◽  
E. Aldrian
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Negative Correlation ◽  
Prediction Models ◽  
Time Series Data ◽  
Sea Surface ◽  
Rainfall Pattern ◽  
Series Data ◽  
Rainfall Prediction

<p>Significant decrease in rainfall caused extreme climate has significant impact on agriculture sector, especialy food crops production. It is one of reason and push developing of rainfall prediction models as anticipate from extreme climate events. Rainfall prediction models develop base on time series data, and then it has been included anomaly aspect, like rainfall prediction model with Kalman filtering method. One of global parameter that has been used as climate anomaly indicator is sea surface temperature. Some of research indicate, there are relationship between sea surface temperature and rainfall. Relationship between Indonesian rainfall and global sea surface temperature has been known, but its relationship with Indonesian’s sea surface temperature not know yet, especialy for rainfall in smaller area like district. So, therefore the research about relationship between rainfall in distric area and Indonesian’s sea surface temperature and it application for rainfall prediction is needed. Based on Indonesian’s sea surface temperature time series data Januari 1982 until Mei 2006 show there are zona of Indonesian’s sea surface temperature (with temperature more than 27,6 0C) dominan in Januari-Mei and moved with specific pattern. Highest value of spasial correlation beetwen Cilacap’s rainfall and Indonesian’s sea surface temperature is 0,30 until 0,50 with different zona of Indonesian’s sea surface temperature. Highest positive correlation happened in March and July. Negative correlation is -0,30 until -0,70 with highest negative correlation in May and June. Model validation resulted correlation coeffcient 85,73%, fits model 20,74%, r2 73,49%, RMSE 20,5% and standart deviation 37,96. Rainfall prediction Januari-Desember 2007 period indicated rainfall pattern is near same with average rainfall pattern, rainfall less than 100/month. The result of this research indicate Indonesian’s sea surface temperature can be used as indicator rainfall condition in distric area, that means rainfall in district area can be predicted based on Indonesian’s sea surface temperature in zona with highest correlation in every month.</p><p>------------------------------------------------------------------</p><p>Penurunan curah hujan yang cukup signifikan akibat iklim ekstrim telah membawa dampak yang cukup signifikan pula pada sektor pertanian, terutama produksi tanaman pangan. Hal ini menjadi salah satu alasan yang mendorong semakin berkembangnya model-model prakiraan hujan sebagai upaya antipasi terhadap kejadian iklim ekstrim. Model prakiraan hujan yang pada awalnya hanya berbasis pada data time series, kini telah berkembang dengan memperhitungkan aspek anomali iklim, seperti model prakiraan hujan dengan metode filter Kalman. Salah satu indikator global yang dapat digunakan sebagai indikator anomali iklim adalah suhu permukaan laut. Dari berbagai hasil penelitian diketahui bahwa suhu permukaan laut ini memiliki keterkaitan dengan kejadian curah hujan. Hubungan curah hujan Indonesia dengan suhu permukaan laut global sudah banyak diketahui, tetapi keterkaitannya dengan suhu permukaan laut wilayah Indonesia belum banyak mendapat perhatian, terutama untuk curah hujan pada cakupan yang lebih sempit seperti kabupaten. Oleh karena itu perlu dilakukan penelitian yang mengkaji hubungan kedua parameter tersebut serta mengaplikasikannya untuk prakiraan curah hujan pada wilayah Kabupaten. Hasil penelitian berdasarkan data suhu permukaan laut wilayah Indonesia rata-rata Januari 1982 hingga Mei 2006 menunjukkan zona dengan suhu lebih dari 27,6 0C yang dominan pada bulan Januari-Mei dan bergerak dengan pola yang cukup jelas. Korelasi spasial antara curah hujan kabupaten Cilacap dengan SPL wilayah Indonesia rata-rata bulan Januari-Desember menunjukkan korelasi positip tertinggi antara 0,30 hingga 0,50 dengan zona SPL yang beragam. Korelasi tertinggi terjadi pada bulan Maret dan Juli. Sedangkan korelasi negatip berkisar antara -0,30 hingga -0,70 dengan korelasi negatip tertinggi pada bulan Mei dan Juni. Validasi model prakiraan hujan menghasilkan nilai koefisien korelasi 85,73%, fits model 20,74%, r2 sebesar 73,49%, RMSE 20,5% dan standar deviasi 37,96. Hasil prakiraan hujan bulanan periode Januari-Desember 2007 mengindikasikan pola curah hujan yang tidak jauh berbeda dengan rata-rata selama 19 tahun (1988-2006) dengan jeluk hujan kurang dari 100 mm/bulan. Hasil penelitian mengindikasikan bahwa SPL wilayah Indonesia dapat digunakan sebagai indikator untuk menunjukkan kondisi curah hujan di suatu wilayah (kabupaten), artinya curah hujan dapat diprediksi berdasarkan perubahan SPL pada zona-zona dengan korelasi yang tertinggi pada setiap bulannya.</p>

scholarly journals Comparison of in situ time-series of temperature with gridded sea surface temperature datasets in the North Atlantic

2009 ◽  
Vol 66 (7) ◽  
pp. 1467-1479 ◽  
Author(s):  
Sarah L. Hughes ◽  
N. Penny Holliday ◽  
Eugene Colbourne ◽  
Vladimir Ozhigin ◽  
Hedinn Valdimarsson ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Atlantic ◽  
Sea Surface ◽  
The North ◽  
Spatial Coverage ◽  
The North Atlantic

Abstract Hughes, S. L., Holliday, N. P., Colbourne, E., Ozhigin, V., Valdimarsson, H., Østerhus, S., and Wiltshire, K. 2009. Comparison of in situ time-series of temperature with gridded sea surface temperature datasets in the North Atlantic. – ICES Journal of Marine Science, 66: 1467–1479. Analysis of the effects of climate variability and climate change on the marine ecosystem is difficult in regions where long-term observations of ocean temperature are sparse or unavailable. Gridded sea surface temperature (SST) products, based on a combination of satellite and in situ observations, can be used to examine variability and long-term trends because they provide better spatial coverage than the limited sets of long in situ time-series. SST data from three gridded products (Reynolds/NCEP OISST.v2., Reynolds ERSST.v3, and the Hadley Centre HadISST1) are compared with long time-series of in situ measurements from ICES standard sections in the North Atlantic and Nordic Seas. The variability and trends derived from the two data sources are examined, and the usefulness of the products as a proxy for subsurface conditions is discussed.

Long-Term Variations of the Sea Surface Temperature in the East Coast of Korea

2014 ◽  
Vol 20 (6) ◽  
pp. 601-608 ◽  
Author(s):  
Sang-Woo Kim ◽  
◽  
Jin-Wook Im ◽  
Byung-Sun Yoon ◽  
Hee-Dong Jeong ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
East Coast ◽  
Sea Surface ◽  
Long Term Variations

scholarly journals Recent changes (2013-2017) in scyphomedusan fauna in the Boka Kotorska Bay, Montenegro (Southeast Adriatic)

2019 ◽  
Vol 60 (1) ◽  
pp. 25-39
Author(s):  
Ivana Violić ◽  
Davor Lučić ◽  
Ivona Milić Beran ◽  
Vesna Mačić ◽  
Branka Pestorić ◽  
...  
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Water Column ◽  
Sea Surface ◽  
Long Term Trends ◽  
Sst Anomalies ◽  
Chrysaora Hysoscella

A semi- quantitative time series (2013-2017) was used to present the recent events of scyphomedusae appearance and abundance in the Boka Kotorska Bay, Montenegro, Southeast Adriatic. Six meroplanktonic species were recorded: Aurelia spp, Chrysaora hysoscella, Cotylorhiza tuberculata ̧ Discomedusa lobata, Drymonema dalmatinum and Rhizostoma pulmo. Among them, C. hysoscella and D. lobata dominated in the water column during winter and spring, forming dense aggregations in March and May, and February to May, respectively. Our description of the D. lobata blooms are actually the first known records of blooms for this species. C. tuberculata was observed in the Bay principally in August and September. The bloom was occurred only in 2017, being the first information of C. tuberculata mass appearance in this area. We hypothesized that global warming phenomena could trigger the observed changes, and in this respect, long-term trends of sea surface temperature (SST) fluctuations were analysed. The scyphomedusae blooms coincided with high positive SST anomalies, noted in the last seven years for this area. To better understand the mechanisms underlying changes in their phenology and abundance, detailed studies on benthic stages in the Bay are essential.

A process-oriented approach for mining marine heatwaves with a time series of raster formatted products

2020 ◽  
Author(s):  
Cunjin Xue ◽  
Changfeng Jing
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Spatial Structure ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temporal Evolution ◽  
Sea Surface ◽  
The Third ◽  
Oriented Approach

&lt;p&gt;A marine heatwave (MHW) is defined as a coherent area of extreme warm sea surface temperature that persists for days to months, which has a property of evolution from production through development to death in space and time. MHWs usually relates to climatic extremes that can have devastating and long-term impacts on ecosystems, with subsequent socioeconomic consequences. Long term remote sensing products make it possible for mining successive MHWs at global scale. However, more literatures focus on a spatial distribution at a fixed time snapshot or a temporal statistic at a fixed grid cell of MWHs. As few considering the temporal evolution of MWHs, it is greater challenge to mining their dynamic changes of spatial structure. Thus, this manuscript proposes a process-oriented approach for identifying and tracking MWHs, named as PoAITM. The PoAITM considers a dynamic evolution of a MWH, which consists of three steps. The first step uses the threshold-based algorithm to identifying the time series of grid pixels which meets the MWH definition, called as MWH pixels; the second adopts the spatial proximities to connect the MWH pixels at the snapshots, and transforms them spatial objects, called as MWH objects; the third combines the dynamic characteristics and spatiotemporal topologies of MWH objects between the previous and next snapshots to identify and track them belonging to the same ones. The final extract MWH with a property from production through development to death is defined as a MWH process. Comparison with the prevail methods of tracking MHWs, The PoAITM has three advantages. Firstly, PoAITM combines the spatial distribution and temporal evolution of MWH to identify and track the MWH objects. The second considers not only the spatial structure of MWH at current snapshot, also the previous and next ones, to track the MWH process, which ensures the MWH completeness in a temporal domain. The third is the dynamic behaviors of MWH, e.g. developing, merging, splitting, are also found between the successive MWH objects. Finally, we address the global MWHs exploring from the sea surface temperature products during the period of January 1982 to December 2018. The results not only show well-known knowledge, but also some new findings about evolution characteristics of MWHs, which may provide new references for further study on global climate change.&lt;/p&gt;

scholarly journals Future temperature and salinity in Puget Sound, Washington State, under CMIP6 climate change scenarios

2021 ◽  
Author(s):  
Stevie Walker ◽  
Hem Nalini Morzaria-Luna ◽  
Isaac Kaplan ◽  
David Petatán-Ramírez
Keyword(s):  
Climate Change ◽  
Time Series ◽  
High Resolution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Puget Sound ◽  
Washington State ◽  
Trophic Levels ◽  
Sea Surface

Abstract In Washington State, climate change will reshape the Puget Sound marine ecosystem through bottom-up and top-down processes, directly affecting species at all trophic levels. To better understand future climate change effects on sea surface temperature and salinity in Puget Sound, we used empirical downscaling to derive high-resolution time series of future sea surface temperature and salinity. Downscaling was based on scenario outputs of two coarse-resolution General Circulation Models, GFDL-CM4 and CNRM-CM6-1-HR, developed as part of the Coupled Model Intercomparison Project Phase 6 (CMIP6). We calculated 30-year climatologies for historical and future simulations, calculated the anomalies between historical and future projections, interpolated to a high resolution, and applied the resulting downscaled anomalies to a Regional Ocean Modeling System (ROMS) time series, yielding short-term (2020–2050) and long-term (2070–2100) delta-downscaled forecasts. Downscaled output for Puget Sound showed temperature and salinity variability between scenarios and models, but overall, there was strong model agreement. Model variability and uncertainty was higher for long-term projections. Spatially, we found regional differences for both temperature and salinity, including higher temperatures in the South Basin of Puget Sound and higher salinity in the North Basin. This study is a first step to translating CMIP6 outputs to higher resolution predictions of future conditions in Puget Sound. Interpreting downscaled projections of temperature and salinity in Puget Sound will help inform future ecosystem-based management decisions, such as supporting end-to-end ecosystem modeling simulations and assessing local-scale exposure risk to climate change.

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