scholarly journals Imprint of the convective parameterization and sea‐surface temperature on large‐scale convective self‐aggregation

2017 ◽  
Vol 9 (2) ◽  
pp. 1488-1505 ◽  
Author(s):  
Tobias Becker ◽  
Bjorn Stevens ◽  
Cathy Hohenegger
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Sea Surface ◽  
Convective Parameterization ◽  
Self Aggregation

scholarly journals Statistical Downscaling Forecasts for Winter Monsoon Precipitation in Malaysia Using Multimodel Output Variables

2010 ◽  
Vol 23 (1) ◽  
pp. 17-27 ◽  
Author(s):  
Liew Juneng ◽  
Fredolin T. Tangang ◽  
Hongwen Kang ◽  
Woo-Jin Lee ◽  
Yap Kok Seng
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Winter Season ◽  
Peninsular Malaysia ◽  
Sea Surface ◽  
Asia Pacific ◽  
Forecast Data ◽  
Multimodel Ensembles

Abstract This paper compares the skills of four different forecasting approaches in predicting the 1-month lead time of the Malaysian winter season precipitation. Two of the approaches are based on statistical downscaling techniques of multimodel ensembles (MME). The third one is the ensemble of raw GCM forecast without any downscaling, whereas the fourth approach, which provides a baseline comparison, is a purely statistical forecast based solely on the preceding sea surface temperature anomaly. The first multimodel statistical downscaling method was developed by the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) team, whereas the second is based on the canonical correlation analysis (CCA) technique using the same predictor variables. For the multimodel downscaling ensemble, eight variables from seven operational GCMs are used as predictors with the hindcast forecast data spanning a period of 21 yr from 1983/84 to 2003/04. The raw GCM forecast ensemble tends to have higher skills than the baseline skills of the purely statistical forecast that relates the dominant modes of observed sea surface temperature variability to precipitation. However, the downscaled MME forecasts have higher skills than the raw GCM products. In particular, the model developed by APCC showed significant improvement over the peninsular Malaysia region. This is attributed to the model’s ability to capture regional and large-scale predictor signatures from which the additional skills originated. Overall, the results showed that the appropriate downscaling technique and ensemble of various GCM forecasts could result in some skill enhancement, particularly over peninsular Malaysia, where other models tend to have lower or no skills.

scholarly journals Adjusting for Desert-Dust-Related Biases in a Climate Data Record of Sea Surface Temperature

2020 ◽  
Vol 12 (16) ◽  
pp. 2554
Author(s):  
Christopher J. Merchant ◽  
Owen Embury
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Dust Aerosol ◽  
Sea Surface ◽  
Climate Data ◽  
Desert Dust ◽  
Data Record ◽  
Climate Data Record

Atmospheric desert-dust aerosol, primarily from north Africa, causes negative biases in remotely sensed climate data records of sea surface temperature (SST). Here, large-scale bias adjustments are deduced and applied to the v2 climate data record of SST from the European Space Agency Climate Change Initiative (CCI). Unlike SST from infrared sensors, SST measured in situ is not prone to desert-dust bias. An in-situ-based SST analysis is combined with column dust mass from the Modern-Era Retrospective analysis for Research and Applications, Version 2 to deduce a monthly, large-scale adjustment to CCI analysis SSTs. Having reduced the dust-related biases, a further correction for some periods of anomalous satellite calibration is also derived. The corrections will increase the usability of the v2 CCI SST record for oceanographic and climate applications, such as understanding the role of Arabian Sea SSTs in the Indian monsoon. The corrections will also pave the way for a v3 climate data record with improved error characteristics with respect to atmospheric dust aerosol.

scholarly journals Forage fish of the Pacific Rim as revealed by diet of a piscivorous seabird: synchrony and relationships with sea surface temperature

2008 ◽  
Vol 65 (8) ◽  
pp. 1610-1622 ◽  
Author(s):  
Julie A. Thayer ◽  
Douglas F. Bertram ◽  
Scott A. Hatch ◽  
Mark J. Hipfner ◽  
Leslie Slater ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Fish Communities ◽  
Japan Sea ◽  
Pacific Rim ◽  
Sea Surface ◽  
Eastern Pacific ◽  
Forage Fish ◽  
Forage Fishes

We tested the hypothesis of synchronous interannual changes in forage fish dynamics around the North Pacific Rim. To do this, we sampled forage fish communities using a seabird predator, the rhinoceros auklet ( Cerorhinca monocerata ), at six coastal study sites from Japan to California. We investigated whether take of forage fishes was related to local marine conditions as indexed by sea surface temperature (SST). SST was concordant across sites in the eastern Pacific, but inversely correlated between east and west. Forage fish communities consisted of anchovy ( Engraulis spp.), sandlance ( Ammodytes spp.), capelin ( Mallotus spp.), and juvenile rockfish ( Sebastes spp.), among others, and take of forage fish varied in response to interannual and possibly lower-frequency oceanographic variability. Take of primary forage species were significantly related to changes in SST only at the eastern sites. We found synchrony in interannual variation of primary forage fishes across several regions in the eastern Pacific, but no significant east–west correlations. Specifically in the Japan Sea, factors other than local SST or interannual variability may more strongly influence forage fishes. Predator diet sampling offers a fishery-independent, large-scale perspective on forage fish dynamics that may be difficult to obtain using conventional means of study.

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