The Forecast Skills and Predictability Sources of Marine Heatwaves in the NUIST-CFS1.0 Hindcasts

Using monthly observations and ensemble hindcasts of the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS1.0) for the period 1983–2020, this study investigates the forecast skill of marine heatwaves(MHWs) over the globe and the predictability sources of the MHWs over the tropical oceans. The MHW forecasts are demonstrated to be skillful on seasonal-annual time scales, particularly in tropical oceans. The forecast skill of the MHWs over the tropical Pacific Ocean(TPO) remains high at lead times of 1–24 months, indicating a forecast better than random chance for up to two years. The forecast skill is subject to the spring predictability barrier of El Nino-Southern Oscillation(ENSO). The forecast skills for the MHWs over the tropical Indian Ocean(TIO), tropical Atlantic Ocean(TAO), and tropical Northwest Pacific(NWP) are lower than that in the TPO. A reliable forecast at lead times of up to two years is shown over the TIO, while a shorter reliable forecast window(less than 17 months) occurs for the TAO and NWP.Additionally, the forecast skills for the TIO, TAO, and NWP are seasonally dependent. Higher skills for the TIO and TAO appear in boreal spring, while a greater skill for the NWP emerges in late summer-early autumn. Further analyses suggest that ENSO serves as a critical source of predictability for MHWs over the TIO and TAO in spring and MHWs over the NWP in summer.

Recognition and Prediction of Source Rocks of the Madingo Formation in the Lower Congo Basin

We investigated the petrological and seismic properties of Madingo Formation, the highquality source rocks in the Madingo Formation in the Lower Congo Basin are highly heterogeneous. Due to little drilling and oil-based mud pollution, samples that are able to be used to measure the TOC(total organic carbon) content of source rock in the Madingo Formation are few and unevenly distributed;hence, it is difficult to carry out their quantitative evaluation. We investigated the petrological and seismic properties of Madingo Formation between TOC and well logging parameters including density, natural gamma, and acoustic time difference via multiple regression analysis. The TOC data volume is calculated using a neural network model between the predicted TOC content and seismic attributes of the sidetrack. The results of TOC three-dimensional quantitative prediction in the study area show that the source rocks in the Madingo Formation have a strong heterogeneity in the vertical direction, and the plane distribution is low in the northeast and high in the southwest. This study provides suitable tools to predict the complex heterogeneous distribution of source rocks and has great significance for oil exploration in the Lower Congo Basin.

Why Are Arctic Sea Ice Concentration in September and Its Interannual Variability Well Predicted over the Barents–East Siberian Seas by CFSv2?

To further understand the prediction skill for the interannual variability of the sea ice concentration(SIC)in specific regions of the Arctic,this paper evaluates the NCEP Climate Forecast System version 2(CFSv2),in predicting the autumn SIC and its interannual variability over the Barents–East Siberian Seas(BES).It is found that CFSv2 presents much better prediction skill for the September SIC over BES than the Arctic as a whole at 1–6-month leads,and high prediction skill for the interannual variability of the SIC over BES is displayed at 1–2-month leads after removing the linear trend.CFSv2 can reasonably reproduce the relationship between the SIC over BES in September and such factors as the surface air temperature(SAT),200-hPa geopotential height,sea surface temperature(SST),and North Atlantic Oscillation.In addition,it is found that the prescribed SIC initial condition in August as an input to CFSv2 is also essential.Therefore,the above atmospheric and oceanic factors,as well as an accurate initial condition of SIC,all contribute to a high prediction skill for SIC over BES in September.Based on a statistical prediction method,the contributions from individual predictability sources are further identified.The high prediction skill of CFSv2 for the interannual variability of SIC over BES is largely attributable to its accurate predictions of the SAT and SST,as well as a better initial condition of SIC.