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 s...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.展开更多
Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives ...Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives of billions who depend on or are affected by monsoons, as it is essential for the water cycle, food security, ecology, disaster prevention, and the economy of monsoon regions. Given the extensive literature on Asian monsoon climate prediction, we limit our focus to reviewing the seasonal prediction and predictability of the Asian Summer Monsoon (ASM). However, much of this review is also relevant to monsoon predictions in other seasons and regions. Over the past two decades, considerable progress has been made in the seasonal forecasting of the ASM, driven by an enhanced understanding of the sources of predictability and the dynamics of seasonal variability, along with advanced development in sophisticated models and technologies. This review centers on advances in understanding the physical foundation for monsoon climate prediction (section 2), significant findings and insights into the primary and regional sources of predictability arising from feedback processes among various climate components (sections 3 and 4), the effects of global warming and external forcings on predictability (section 5), developments in seasonal prediction models and techniques (section 6), the challenges and limitations of monsoon climate prediction (section 7), and emerging research trends with suggestions for future directions (section 8). We hope this review will stimulate creative activities to enhance monsoon climate prediction.展开更多
For many years,oil and natural gas geochemistry has been key to hydrocarbon exploration and exploitation in offshore China.Onshore oil fields have been thoroughly explored and exploited and have now entered the later ...For many years,oil and natural gas geochemistry has been key to hydrocarbon exploration and exploitation in offshore China.Onshore oil fields have been thoroughly explored and exploited and have now entered the later stages of development.However,the oil and gas resources in the offshore basin of China are still abundant,and they have been relatively little explored,so they will be the main growth target for oil and gas production in the future.China has made many breakthroughs in offshore oil exploration in recent years.A large condensate fielddBozhong 19-6dhas been discovered in the Bozhong Depression.Its main source rock is associated with algal blooming in the third member of the Shahejie Formation.The Baodao 21-1 gas field in the Qiongdongnan Basin shows clear signs of receiving terrestrial organic matter from the Yacheng and Lingshui formations.The first ultra-deep water and ultra-shallow gas,found in the Lingshui 36-1 gas field in the Qiongdongnan Basin,has thermogenetic and biogenetic origins.Well WY-1 in the Weixinan Depression marked the initiation of offshore shale oil exploration.Its sweet interval consists of two stable oil shale beds developed at the base and the top of the second member of the Liushagang Formation.However,samples from offshore exploration are often severely contaminated,and the corresponding data can be seriously distorted,which yields fewer core and more cutting samples.Therefore,it is necessary to thoroughly screen test data and to apply decontamination treatment and data correction to contaminated samples.The success rate of offshore exploration usually relies on the accurate prediction of source kitchens.Reservoir geochemistry thus has great significance for the future,as source input geochemical information can be used to trace materials backwards,and source kitchens can be predicted,when geochemistry is combined with enrichment and development models of source rocks and geophysical data.展开更多
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-bas...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.展开更多
Based on a combination of six Chinese climate models and three international operational models,the China multimodel ensemble(CMME)prediction system has been upgraded into its version 2(CMMEv2.0)at the National Climat...Based on a combination of six Chinese climate models and three international operational models,the China multimodel ensemble(CMME)prediction system has been upgraded into its version 2(CMMEv2.0)at the National Climate Centre(NCC)of the China Meteorological Administration(CMA)by including new model members and expanding prediction products.A comprehensive assessment of the performance of the upgraded CMME during its hindcast(1993–2016)and real-time prediction(2021–present)periods is conducted in this study.The results demonstrate that CMMEv2.0 outperforms all the individual models by capturing more realistic equatorial sea surface temperature(SST)variability.It exhibits better prediction skills for precipitation and 2-m temperature anomalies,and the improvements in prediction skill of CMMEv2.0 are significant over East Asia.The superiority of CMMEv2.0 can be attributed to its better projection of El Niño–Southern Oscillation(ENSO;with the temporal correlation coefficient score for Niño3.4 index reaching 0.87 at 6-month lead)and ENSO-related teleconnections.As for the real-time prediction in recent three years,CMMEv2.0 has also yielded relatively stable skills;it successfully predicted the primary rainbelt over northern China in summers of 2021–2023 and the warm conditions in winters of 2022/2023.Beyond that,ensemble sampling experiments indicate that the CMMEv2.0 skills become saturated after the ensemble model number increased to 5–6,indicating that selection of only an optimal subgroup of ensemble models could benefit the prediction performance,especially over the extratropics,yet the underlying reasons await future investigation.展开更多
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 p...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.展开更多
基金jointly supported by the National Natural Science Foundation of China (Grant Nos.42192562 and 42030605)。
文摘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.
基金supported by the National Natural Science Foundation of China(Grant No.U2342208)support from NSF/Climate Dynamics Award#2025057。
文摘Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives of billions who depend on or are affected by monsoons, as it is essential for the water cycle, food security, ecology, disaster prevention, and the economy of monsoon regions. Given the extensive literature on Asian monsoon climate prediction, we limit our focus to reviewing the seasonal prediction and predictability of the Asian Summer Monsoon (ASM). However, much of this review is also relevant to monsoon predictions in other seasons and regions. Over the past two decades, considerable progress has been made in the seasonal forecasting of the ASM, driven by an enhanced understanding of the sources of predictability and the dynamics of seasonal variability, along with advanced development in sophisticated models and technologies. This review centers on advances in understanding the physical foundation for monsoon climate prediction (section 2), significant findings and insights into the primary and regional sources of predictability arising from feedback processes among various climate components (sections 3 and 4), the effects of global warming and external forcings on predictability (section 5), developments in seasonal prediction models and techniques (section 6), the challenges and limitations of monsoon climate prediction (section 7), and emerging research trends with suggestions for future directions (section 8). We hope this review will stimulate creative activities to enhance monsoon climate prediction.
基金financially supported by the Natural Science Foundation of China(Grant Nos:41472108,41872131,42302189).
文摘For many years,oil and natural gas geochemistry has been key to hydrocarbon exploration and exploitation in offshore China.Onshore oil fields have been thoroughly explored and exploited and have now entered the later stages of development.However,the oil and gas resources in the offshore basin of China are still abundant,and they have been relatively little explored,so they will be the main growth target for oil and gas production in the future.China has made many breakthroughs in offshore oil exploration in recent years.A large condensate fielddBozhong 19-6dhas been discovered in the Bozhong Depression.Its main source rock is associated with algal blooming in the third member of the Shahejie Formation.The Baodao 21-1 gas field in the Qiongdongnan Basin shows clear signs of receiving terrestrial organic matter from the Yacheng and Lingshui formations.The first ultra-deep water and ultra-shallow gas,found in the Lingshui 36-1 gas field in the Qiongdongnan Basin,has thermogenetic and biogenetic origins.Well WY-1 in the Weixinan Depression marked the initiation of offshore shale oil exploration.Its sweet interval consists of two stable oil shale beds developed at the base and the top of the second member of the Liushagang Formation.However,samples from offshore exploration are often severely contaminated,and the corresponding data can be seriously distorted,which yields fewer core and more cutting samples.Therefore,it is necessary to thoroughly screen test data and to apply decontamination treatment and data correction to contaminated samples.The success rate of offshore exploration usually relies on the accurate prediction of source kitchens.Reservoir geochemistry thus has great significance for the future,as source input geochemical information can be used to trace materials backwards,and source kitchens can be predicted,when geochemistry is combined with enrichment and development models of source rocks and geophysical data.
基金supported by the China National Offshore Oil Corporation Research Institute of Program (No. CRI2015FW001)China National Research and Development Project (No. 2017ZX05032)+1 种基金Major National Science and Technology Programs in the “Thirteenth Five-Year” plan period (No. 2016ZX05048)Study on identification and prediction technology of source rocks in key area of west Africa-South America sea national oil and gas projects in the “Thirteenth Five-Year” plan period (No. 2017ZX05032-001-002)。
文摘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.
基金Supported by the National Natural Science Foundation of China (U2242206 and 42175052)National Key Research and Development Program of China (2021YFA071800 and 2023YFC3007700)+3 种基金Innovative Development Special Project of China Meteorological Administration (CXFZ2023J002 and CXFZ2023J050)China Meteorological Administration (CMA) Joint Research Project for Meteorological Capacity Improvement (23NLTSZ003)Special Operating Expenses of Scientific Research Institutions for “Key Technology Development of Numerical Forecasting” of Chinese Academy of Meteorological SciencesCMA Youth Innovation Team(CMA2024QN06)。
文摘Based on a combination of six Chinese climate models and three international operational models,the China multimodel ensemble(CMME)prediction system has been upgraded into its version 2(CMMEv2.0)at the National Climate Centre(NCC)of the China Meteorological Administration(CMA)by including new model members and expanding prediction products.A comprehensive assessment of the performance of the upgraded CMME during its hindcast(1993–2016)and real-time prediction(2021–present)periods is conducted in this study.The results demonstrate that CMMEv2.0 outperforms all the individual models by capturing more realistic equatorial sea surface temperature(SST)variability.It exhibits better prediction skills for precipitation and 2-m temperature anomalies,and the improvements in prediction skill of CMMEv2.0 are significant over East Asia.The superiority of CMMEv2.0 can be attributed to its better projection of El Niño–Southern Oscillation(ENSO;with the temporal correlation coefficient score for Niño3.4 index reaching 0.87 at 6-month lead)and ENSO-related teleconnections.As for the real-time prediction in recent three years,CMMEv2.0 has also yielded relatively stable skills;it successfully predicted the primary rainbelt over northern China in summers of 2021–2023 and the warm conditions in winters of 2022/2023.Beyond that,ensemble sampling experiments indicate that the CMMEv2.0 skills become saturated after the ensemble model number increased to 5–6,indicating that selection of only an optimal subgroup of ensemble models could benefit the prediction performance,especially over the extratropics,yet the underlying reasons await future investigation.
基金Supported by the National Key Research and Development Program of China(2022YFE0106800)National Natural Science Foundation of China(42230603)Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(311021001)。
文摘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.
