scholarly journals Satellite Perspectives of Sea Surface Temperature Diurnal Warming on Atmospheric Moistening and Radiative Heating During MJO

2020 ◽  
pp. 1-56
Author(s):  
Kyle Itterly ◽  
Patrick Taylor ◽  
J. Brent Roberts
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Active Phase ◽  
Radiative Heating ◽  
Sea Surface ◽  
Integrated Analysis ◽  
Moist Static Energy ◽  
Diurnal Warming ◽  
Static Energy

AbstractDiurnal air-sea coupling affects climate modes such as the Madden-Julian Oscillation (MJO) via the regional moist static energy budget. Prior to MJO initiation, large-scale subsidence increases (decreases) surface shortwave insolation (winds). These act in concert to significantly warm the uppermost layer of the ocean over the course of a single day and the ocean mixed layer over the course of 1-2 weeks. Here, we provide an integrated analysis of multiple surface, top-of-atmosphere, and atmospheric column observations to assess the covariability related to regions of strong diurnal sea surface temperature (dSST) warming over 44 MJO events between 2000-2018 to assess their role in MJO initiation. Combining satellite observations of evaporation and precipitation with reanalysis moisture budget terms, we find 30-50% enhanced moistening over high dSST regions during late afternoon using either ERA5 or MERRA-2 despite large model biases. Diurnally developing moisture convergence, only modestly weaker evaporation, and diurnal minimum precipitation act to locally enhance moistening over broad regions of enhanced diurnal warming, which rectifies onto the larger scale. Field campaign ship and sounding data corroborate that strong dSST periods are associated with reduced middle tropospheric humidity and larger diurnal amplitudes of surface warming, evaporation, instability, and column moistening. Further, we find greater daytime increases in low cloud cover and evidence of enhanced radiative destabilization for the top 50th dSST percentile. Together, these results support that dSST warming acts in concert with large-scale dynamics to enhance moist static energy during the suppressed to active phase transition of the MJO.

Optimal Error Analysis of MJO Prediction Associated with Uncertainties in Sea Surface Temperature over Indian Ocean

2020 ◽  
Author(s):  
Xiaojing Li ◽  
Youmin Tang
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Computational Time ◽  
Initial Time ◽  
Ensemble Prediction ◽  
Moist Static Energy ◽  
Vertical Advection ◽  
Static Energy

<p>In this study, the predictability of the Madden-Julian Oscillation (MJO) is investigated using the coupled Community Earth System Model (CESM) and the climatically relevant singular vector (CSV) method. The CSV method is an ensemble-based strategy to calculate the optimal growth of the initial error on the climate scale. We focus on the CSV analysis of MJO initialized at phase II, facilitating the investigation of the effect of the initial errors of the sea surface temperature (SST) in the Indian Ocean on it. Six different MJO events are chosen as the study cases to ensure the robustness of the results.</p><p>The results indicate that for all the study cases, the optimal perturbation structure of the SST, denoted by the leading mode of the singular vectors (SVs), is a meridional dipole-like pattern between the Bay of Bengal and the southern central Indian Ocean. The MJO signal tends to be more converged and significant in the Eastern Hemisphere while the model is perturbed by leading SV. The moist static energy analysis results indicate that the eastward propagation is much more evident in the terms of vertical advection and radiation flux than others. Therefore, the SV perturbation can strengthen and converge the MJO signal mostly by increasing the vertical advection of the moist static energy.</p><p>Further, the sensitivity studies indicate that the structure of the leading SV is not sensitive to the initial states, which suggests that we might not need to calculate SVs for each initial time in constructing the ensemble prediction, significantly saving computational time in the operational forecast systems.</p>

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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