This study assesses the suitability of convolutional neural networks(CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that co...This study assesses the suitability of convolutional neural networks(CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that compare different CNN configurations and deployed the best-performing architecture to downscale one-month lead seasonal forecasts of June–July–August–September(JJAS) precipitation from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0(NUIST-CFS1.0) for 1982–2020. We also perform hyper-parameter optimization and introduce predictors over a larger area to include information about the main large-scale circulations that drive precipitation over the East Africa region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results show that the CNN-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme precipitation spatial patterns. Besides, CNN-based downscaling yields a much more accurate forecast of extreme and spell indicators and reduces the significant relative biases exhibited by the raw model predictions. Moreover, our results show that CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of East Africa. The results demonstrate the potential usefulness of CNN in downscaling seasonal precipitation predictions over East Africa,particularly in providing improved forecast products which are essential for end users.展开更多
Prediction skill for the seasonal tropical cyclone(TC)activity in the Northern Hemisphere is investigated using the coupled climate forecast system(version 1.0)of Nanjing University of Information Science and Technolo...Prediction skill for the seasonal tropical cyclone(TC)activity in the Northern Hemisphere is investigated using the coupled climate forecast system(version 1.0)of Nanjing University of Information Science and Technology(NUISTCFS1.0).This assessment is based on the seven-month(May to November)hindcasts consisting of nine ensemble members during 1982–2019.The predictions are compared with the Japanese 55-year Reanalysis and observed tropical storms in the Northern Hemisphere.The results show that the overall distributions of the TC genesis and track densities in model hindcasts agree well with the observations,although the seasonal mean TC frequency and accumulated cyclone energy(ACE)are underestimated in all basins due to the low resolution(T106)of the atmospheric component in the model.NUIST-CFS1.0 closely predicts the interannual variations of TC frequency and ACE in the North Atlantic(NA)and eastern North Pacific(ENP),which have a good relationship with indexes based on the sea surface temperature.In the western North Pacific(WNP),NUIST-CFS1.0 can closely capture ACE,which is significantly correlated with the El Ni?o–Southern Oscillation(ENSO),while it has difficulty forecasting the interannual variation of TC frequency in this area.When the WNP is further divided into eastern and western subregions,the model displays improved TC activity forecasting ability.Additionally,it is found that biases in predicted TC genesis locations lead to inaccurately represented TC–environment relationships,which may affect the capability of the model in reproducing the interannual variations of TC activity.展开更多
Current dynamical models experience great difficulties providing reliable seasonal forecasts of regional/local rainfall in South China.This study evaluates seasonal forecast skill for precipitation in the first rainy ...Current dynamical models experience great difficulties providing reliable seasonal forecasts of regional/local rainfall in South China.This study evaluates seasonal forecast skill for precipitation in the first rainy season(FRS,i.e.,April–June)over South China from 1982 to 2020 based on the global real-time Climate Forecast System of Nanjing University of Information Science and Technology(NUIST-CFS1.0,previously known as SINTEX-F).The potential predictability and the practical forecast skill of NUIST-CFS1.0 for FRS precipitation remain low in general.But NUIST-CFS1.0 still performs better than the average of nine international models in terms of correlation coefficient skill in predicting the interannual precipitation anomaly and its related circulation index.NUIST-CFS1.0 captures the anomalous Philippines anticyclone,which transports moisture and heat northward to South China,favoring more precipitation in South China during the FRS.By examining the correlations between sea surface temperature(SST)and FRS precipitation and the Philippines anticyclone,we find that the model reasonably captures SST-associated precipitation and circulation anomalies,which partly explains the predictability of FRS precipitation.A dynamical downscaling model with 30-km resolution forced by the large-scale circulations of the NUIST-CFS1.0 predictions could improve forecasts of the climatological states and extreme precipitation events.Our results also reveal interesting interdecadal changes in the predictive skill for FRS precipitation in South China based on the NUIST-CFS1.0 hindcasts.These results help improve the understanding and forecasts for FRS precipitation in South China.展开更多
The extreme floods in the Middle/Lower Yangtze River Valley(MLYRV)during June−July 2020 caused more than 170 billion Chinese Yuan direct economic losses.Here,we examine the key features related to this extreme event a...The extreme floods in the Middle/Lower Yangtze River Valley(MLYRV)during June−July 2020 caused more than 170 billion Chinese Yuan direct economic losses.Here,we examine the key features related to this extreme event and explore relative contributions of SST anomalies in different tropical oceans.Our results reveal that the extreme floods over the MLYRV were tightly related to a strong anomalous anticyclone persisting over the western North Pacific,which brought tropical warm moisture northward that converged over the MLYRV.In addition,despite the absence of a strong El Niño in 2019/2020 winter,the mean SST anomaly in the tropical Indian Ocean during June−July 2020 reached its highest value over the last 40 years,and 43%(57%)of it is attributed to the multi-decadal warming trend(interannual variability).Based on the NUIST CFS1.0 model that successfully predicted the wet conditions over the MLYRV in summer 2020 initiated from 1 March 2020(albeit the magnitude of the predicted precipitation was only about one-seventh of the observed),sensitivity experiment results suggest that the warm SST condition in the Indian Ocean played a dominant role in generating the extreme floods,compared to the contributions of SST anomalies in the Maritime Continent,central and eastern equatorial Pacific,and North Atlantic.Furthermore,both the multi-decadal warming trend and the interannual variability of the Indian Ocean SSTs had positive impacts on the extreme floods.Our results imply that the strong multi-decadal warming trend in the Indian Ocean needs to be taken into consideration for the prediction/projection of summer extreme floods over the MLYRV in the future.展开更多
East Africa is particularly vulnerable to precipitation variability, as the livelihood of much of the population depends on rainfed agriculture. Seasonal forecasts of the precipitation anomalies, when skillful, can th...East Africa is particularly vulnerable to precipitation variability, as the livelihood of much of the population depends on rainfed agriculture. Seasonal forecasts of the precipitation anomalies, when skillful, can therefore improve implementation of coping mechanisms with respect to food security and water management. This study assesses the performance of Nanjing University of Information Science and Technology Climate Forecast System version 1.0(NUISTCFS1.0) on forecasting June–September(JJAS) seasonal precipitation anomalies over East Africa. The skill in predicting the JJAS mean precipitation initiated from 1 May for the period of 1982–2019 is evaluated using both deterministic and probabilistic verification metrics on grid cell and over six distinct clusters. The results show that NUIST-CFS1.0 captures the spatial pattern of observed seasonal precipitation climatology, albeit with dry and wet biases in a few parts of the region. The model has positive skill across a majority of Ethiopia, Kenya, Uganda, and Tanzania, whereas it doesn’t exceed the skill of climatological forecasts in parts of Sudan and southeastern Ethiopia. Positive forecast skill is found over regions where the model shows better performance in reproducing teleconnections related to oceanic SST. The prediction performance of NUIST-CFS1.0 is found to be on a level that is potentially useful over a majority of East Africa.展开更多
To assess the performances of state-of-the-art global climate models on simulating the Arctic clouds and surface radiation balance,the 2001–2014 Arctic Basin surface radiation budget,clouds,and the cloud radiative ef...To assess the performances of state-of-the-art global climate models on simulating the Arctic clouds and surface radiation balance,the 2001–2014 Arctic Basin surface radiation budget,clouds,and the cloud radiative effects(CREs)in 22 coupled model intercomparison project 6(CMIP6)models are evaluated against satellite observations.For the results from CMIP6 multi-model mean,cloud fraction(CF)peaks in autumn and is lowest in winter and spring,consistent with that from three satellite observation products(Cloud Sat-CALIPSO,CERESMODIS,and APP-x).Simulated CF also shows consistent spatial patterns with those in observations.However,almost all models overestimate the CF amount throughout the year when compared to CERES-MODIS and APP-x.On average,clouds warm the surface of the Arctic Basin mainly via the longwave(LW)radiation cloud warming effect in winter.Simulated surface energy loss of LW is less than that in CERES-EBAF observation,while the net surface shortwave(SW)flux is underestimated.The biases may result from the stronger cloud LW warming effect and SW cooling effect from the overestimated CF by the models.These two biases compensate each other,yielding similar net surface radiation flux between model output(3.0 W/m2)and CERES-EBAF observation(6.1 W/m2).During 2001–2014,significant increasing trend of spring CF is found in the multi-model mean,consistent with previous studies based on surface and satellite observations.Although most of the 22 CMIP6 models show common seasonal cycles of CF and liquid water path/ice water path(LWP/IWP),large inter-model spreads exist in the amounts of CF and LWP/IWP throughout the year,indicating the influences of different cloud parameterization schemes used in different models.Cloud Feedback Model Intercomparison Project(CFMIP)observation simulator package(COSP)is a great tool to accurately assess the performance of climate models on simulating clouds.More intuitive and credible evaluation results can be obtained based on the COSP model output.In the future,with the release of more COSP output of CMIP6 models,it is expected that those inter-model spreads and the model-observation biases can be substantially reduced.Longer term active satellite observations are also necessary to evaluate models’cloud simulations and to further explore the role of clouds in the rapid Arctic climate changes.展开更多
The 2015/16 El Niño displayed a distinct feature in the SST anomalies over the far eastern Pacific(FEP)compared to the 1997/98 extreme case.In contrast to the strong warm SST anomalies in the FEP in the 1997/98 e...The 2015/16 El Niño displayed a distinct feature in the SST anomalies over the far eastern Pacific(FEP)compared to the 1997/98 extreme case.In contrast to the strong warm SST anomalies in the FEP in the 1997/98 event,the FEP warm SST anomalies in the 2015/16 El Niño were modest and accompanied by strong southeasterly wind anomalies in the southeastern Pacific.Exploring possible underlying causes of this distinct difference in the FEP may improve understanding of the diversity of extreme El Niños.Here,we employ observational analyses and numerical model experiments to tackle this issue.Mixed-layer heat budget analysis suggests that compared to the 1997/98 event,the modest FEP SST warming in the 2015/16 event was closely related to strong vertical upwelling,strong westward current,and enhanced surface evaporation,which were caused by the strong southeasterly wind anomalies in the southeastern Pacific.The strong southeasterly wind anomalies were initially triggered by the combined effects of warm SST anomalies in the equatorial central and eastern Pacific(CEP)and cold SST anomalies in the southeastern subtropical Pacific in the antecedent winter,and then sustained by the warm SST anomalies over the northeastern subtropical Pacific and CEP.In contrast,southeasterly wind anomalies in the 1997/98 El Niño were partly restrained by strong anomalously negative sea level pressure and northwesterlies in the northeast flank of the related anomalous cyclone in the subtropical South Pacific.In addition,the strong southeasterly wind and modest SST anomalies in the 2015/16 El Niño may also have been partly related to decadal climate variability.展开更多
Accurate prediction of the summer precipitation over the middle and lower reaches of the Yangtze River(MLYR)is of urgent demand for the local economic and societal development.This study assesses the seasonal forecast...Accurate prediction of the summer precipitation over the middle and lower reaches of the Yangtze River(MLYR)is of urgent demand for the local economic and societal development.This study assesses the seasonal forecast skill in predicting summer precipitation over the MLYR region based on the global Climate Forecast System of Nanjing University of Information Science and Technology(NUIST-CFS1.0,previously SINTEX-F).The results show that the model can provide moderate skill in predicting the interannual variations of the MLYR rainbands,initialized from 1 March.In addition,the nine-member ensemble mean can realistically reproduce the links between the MLYR precipitation and tropical sea surface temperature(SST)anomalies,but the individual members show great discrepancies,indicating large uncertainty in the forecasts.Furthermore,the NUIST-CFS1.0 can predict five of the seven extreme summer precipitation anomalies over the MLYR during 1982-2020,albeit with underestimated magnitudes.The Weather Forecast and Research(WRF)downscaling hindcast experiments with a finer resolution of 30 km,which are forced by the large-scale information of the NUIST-CFS1.0 predictions with a spectral nudging method,display improved predictions of the extreme summer precipitation anomalies to some extent.However,the performance of the downscaling predictions is highly dependent on the global model forecast skill,suggesting that further improvements on both the global and regional climate models are needed.展开更多
Recent studies have shown that deep learning(DL)models can skillfully forecast El Niño–Southern Oscillation(ENSO)events more than 1.5 years in advance.However,concerns regarding the reliability of predictions ma...Recent studies have shown that deep learning(DL)models can skillfully forecast El Niño–Southern Oscillation(ENSO)events more than 1.5 years in advance.However,concerns regarding the reliability of predictions made by DL methods persist,including potential overfitting issues and lack of interpretability.Here,we propose ResoNet,a DL model that combines CNN(convolutional neural network)and transformer architectures.This hybrid architecture enables our model to adequately capture local sea surface temperature anomalies as well as long-range inter-basin interactions across oceans.We show that ResoNet can robustly predict ENSO at lead times of 19 months,thus outperforming existing approaches in terms of the forecast horizon.According to an explainability method applied to ResoNet predictions of El Niño and La Niña from 1-to 18-month leads,we find that it predicts the Niño-3.4 index based on multiple physically reasonable mechanisms,such as the recharge oscillator concept,seasonal footprint mechanism,and Indian Ocean capacitor effect.Moreover,we demonstrate for the first time that the asymmetry between El Niño and La Niña development can be captured by ResoNet.Our results could help to alleviate skepticism about applying DL models for ENSO prediction and encourage more attempts to discover and predict climate phenomena using AI methods.展开更多
Remarkable progress has been made in observations,theories,and simulations of the ocean–atmosphere system,laying a solid foundation for the improvement of short-term climate prediction,among which Chinese scientists ...Remarkable progress has been made in observations,theories,and simulations of the ocean–atmosphere system,laying a solid foundation for the improvement of short-term climate prediction,among which Chinese scientists have made important contributions.This paper reviews Chinese research on tropical air–sea interaction,ENSO dynamics,and ENSO prediction in the past 70 years.Review of the tropical air–sea interaction mainly focuses on four aspects:characteristics of the tropical Pacific climate system and ENSO;main modes of tropical Indian Ocean SSTs and their interactions with the tropical Pacific;main modes of tropical Atlantic SSTs and inter-basin interactions;and influences of the mid–high-latitude air–sea system on ENSO.Review of the ENSO dynamics involves seven aspects:fundamental theories of ENSO;diagnosis and simulation of ENSO;the two types of ENSO;mechanisms of ENSO initiation;the interactions between ENSO and other phenomena;external forcings and teleconnections;and climate change and the ENSO response.The ENSO prediction part briefly summarizes the dynamical–statistical methods used in ENSO prediction,as well as the operational ENSO prediction systems and their applications.Lastly,we discuss some of the issues in these areas that are in need of further study.展开更多
Northeastern China has experienced a significant increase in summer compound hot and dry events(CHDEs),posing a threat to local agricultural production and sustainable development.This study investigates the detectabl...Northeastern China has experienced a significant increase in summer compound hot and dry events(CHDEs),posing a threat to local agricultural production and sustainable development.This study investigates the detectable anthropogenic signal in the long-term trend of CHDE and quantifies the contribution of different external forcings.A probability-based index(PI)is constructed through the joint probability distribution to measure the severity of CHDE,with lower values representing more severe cases.Response of CHDE to external forcing was assessed with simulations from the Coupled Model Intercomparison Project phase 6(CMIP6).The results show a significant increase in the severity of CHDE over northeastern China during the past decades.The trend of regional averaged PI is-0.28(90%confidence interval:-0.43 to-0.13)per 54 yr and it is well reproduced in the historical forcing simulations.The attribution method of optimal fingerprinting was firstly applied to a two-signal configuration with anthropogenic forcing and natural forcing;the anthropogenic impact was robustly detected and it explains most of the observed trend of PI.Similarly,three-signal analysis further demonstrated that the anthropogenic greenhouse gases dominantly contribute to the observed change,while the anthropogenic aerosol and natural forcing have almost no contribution to the observed changes.For a compound event concurrently exceeding the 95 th percentile of surface air temperature and precipitation reversal in the current period,its likelihood exhibits little change at 1.5℃global warming,but almost doubled at 2.0℃global warming.展开更多
基金supported by the National Key Research and Development Program of China (Grant No.2020YFA0608000)the National Natural Science Foundation of China (Grant No. 42030605)the High-Performance Computing of Nanjing University of Information Science&Technology for their support of this work。
文摘This study assesses the suitability of convolutional neural networks(CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that compare different CNN configurations and deployed the best-performing architecture to downscale one-month lead seasonal forecasts of June–July–August–September(JJAS) precipitation from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0(NUIST-CFS1.0) for 1982–2020. We also perform hyper-parameter optimization and introduce predictors over a larger area to include information about the main large-scale circulations that drive precipitation over the East Africa region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results show that the CNN-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme precipitation spatial patterns. Besides, CNN-based downscaling yields a much more accurate forecast of extreme and spell indicators and reduces the significant relative biases exhibited by the raw model predictions. Moreover, our results show that CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of East Africa. The results demonstrate the potential usefulness of CNN in downscaling seasonal precipitation predictions over East Africa,particularly in providing improved forecast products which are essential for end users.
基金supported by the National Natural Science Foundation of China[grant Nos.42125503 and 42075137]the National Key Research and Development Program of China[grant Nos.2020YFA0608000 and 2020YFA0607900].
基金supported by the National Key Research and Development Program of China[grant number 2020YFA0608000]the National Natural Science Foundation of China[grant number 42030605].
基金supported in part by the National Key Research and Development Program of China(Grant No.2020YFA0608000)the Nature Science Foundation of China(Grant Nos.42005002,42030605,and 42105003)。
文摘Prediction skill for the seasonal tropical cyclone(TC)activity in the Northern Hemisphere is investigated using the coupled climate forecast system(version 1.0)of Nanjing University of Information Science and Technology(NUISTCFS1.0).This assessment is based on the seven-month(May to November)hindcasts consisting of nine ensemble members during 1982–2019.The predictions are compared with the Japanese 55-year Reanalysis and observed tropical storms in the Northern Hemisphere.The results show that the overall distributions of the TC genesis and track densities in model hindcasts agree well with the observations,although the seasonal mean TC frequency and accumulated cyclone energy(ACE)are underestimated in all basins due to the low resolution(T106)of the atmospheric component in the model.NUIST-CFS1.0 closely predicts the interannual variations of TC frequency and ACE in the North Atlantic(NA)and eastern North Pacific(ENP),which have a good relationship with indexes based on the sea surface temperature.In the western North Pacific(WNP),NUIST-CFS1.0 can closely capture ACE,which is significantly correlated with the El Ni?o–Southern Oscillation(ENSO),while it has difficulty forecasting the interannual variation of TC frequency in this area.When the WNP is further divided into eastern and western subregions,the model displays improved TC activity forecasting ability.Additionally,it is found that biases in predicted TC genesis locations lead to inaccurately represented TC–environment relationships,which may affect the capability of the model in reproducing the interannual variations of TC activity.
基金supported by National Natural Science Foundation of China(Grant Nos.42088101 and 42030605)National Key R&D Program of China(Grant No.2020YFA0608000)。
文摘Current dynamical models experience great difficulties providing reliable seasonal forecasts of regional/local rainfall in South China.This study evaluates seasonal forecast skill for precipitation in the first rainy season(FRS,i.e.,April–June)over South China from 1982 to 2020 based on the global real-time Climate Forecast System of Nanjing University of Information Science and Technology(NUIST-CFS1.0,previously known as SINTEX-F).The potential predictability and the practical forecast skill of NUIST-CFS1.0 for FRS precipitation remain low in general.But NUIST-CFS1.0 still performs better than the average of nine international models in terms of correlation coefficient skill in predicting the interannual precipitation anomaly and its related circulation index.NUIST-CFS1.0 captures the anomalous Philippines anticyclone,which transports moisture and heat northward to South China,favoring more precipitation in South China during the FRS.By examining the correlations between sea surface temperature(SST)and FRS precipitation and the Philippines anticyclone,we find that the model reasonably captures SST-associated precipitation and circulation anomalies,which partly explains the predictability of FRS precipitation.A dynamical downscaling model with 30-km resolution forced by the large-scale circulations of the NUIST-CFS1.0 predictions could improve forecasts of the climatological states and extreme precipitation events.Our results also reveal interesting interdecadal changes in the predictive skill for FRS precipitation in South China based on the NUIST-CFS1.0 hindcasts.These results help improve the understanding and forecasts for FRS precipitation in South China.
基金This work is supported by National Natural Science Foundation of China(Grant No.42030605 and 42088101)National Key R&D Program of China(Grant No.2020YFA0608004).
文摘The extreme floods in the Middle/Lower Yangtze River Valley(MLYRV)during June−July 2020 caused more than 170 billion Chinese Yuan direct economic losses.Here,we examine the key features related to this extreme event and explore relative contributions of SST anomalies in different tropical oceans.Our results reveal that the extreme floods over the MLYRV were tightly related to a strong anomalous anticyclone persisting over the western North Pacific,which brought tropical warm moisture northward that converged over the MLYRV.In addition,despite the absence of a strong El Niño in 2019/2020 winter,the mean SST anomaly in the tropical Indian Ocean during June−July 2020 reached its highest value over the last 40 years,and 43%(57%)of it is attributed to the multi-decadal warming trend(interannual variability).Based on the NUIST CFS1.0 model that successfully predicted the wet conditions over the MLYRV in summer 2020 initiated from 1 March 2020(albeit the magnitude of the predicted precipitation was only about one-seventh of the observed),sensitivity experiment results suggest that the warm SST condition in the Indian Ocean played a dominant role in generating the extreme floods,compared to the contributions of SST anomalies in the Maritime Continent,central and eastern equatorial Pacific,and North Atlantic.Furthermore,both the multi-decadal warming trend and the interannual variability of the Indian Ocean SSTs had positive impacts on the extreme floods.Our results imply that the strong multi-decadal warming trend in the Indian Ocean needs to be taken into consideration for the prediction/projection of summer extreme floods over the MLYRV in the future.
基金supported by National Natural Science Foundation of China(Grant Nos.42030605 and42088101)National Key R&D Program of China(Grant No.2020YFA0608004)。
文摘East Africa is particularly vulnerable to precipitation variability, as the livelihood of much of the population depends on rainfed agriculture. Seasonal forecasts of the precipitation anomalies, when skillful, can therefore improve implementation of coping mechanisms with respect to food security and water management. This study assesses the performance of Nanjing University of Information Science and Technology Climate Forecast System version 1.0(NUISTCFS1.0) on forecasting June–September(JJAS) seasonal precipitation anomalies over East Africa. The skill in predicting the JJAS mean precipitation initiated from 1 May for the period of 1982–2019 is evaluated using both deterministic and probabilistic verification metrics on grid cell and over six distinct clusters. The results show that NUIST-CFS1.0 captures the spatial pattern of observed seasonal precipitation climatology, albeit with dry and wet biases in a few parts of the region. The model has positive skill across a majority of Ethiopia, Kenya, Uganda, and Tanzania, whereas it doesn’t exceed the skill of climatological forecasts in parts of Sudan and southeastern Ethiopia. Positive forecast skill is found over regions where the model shows better performance in reproducing teleconnections related to oceanic SST. The prediction performance of NUIST-CFS1.0 is found to be on a level that is potentially useful over a majority of East Africa.
基金The Major State Basic Research Development Program of China under contract No.2016YFA0601804the Global Change Research Program of China under contract No.2015CB953900+1 种基金the National Natural Science Foundation of China under contract Nos 41941007 and 41876220the China Postdoctoral Science Foundation under contract No.2020M681661
文摘To assess the performances of state-of-the-art global climate models on simulating the Arctic clouds and surface radiation balance,the 2001–2014 Arctic Basin surface radiation budget,clouds,and the cloud radiative effects(CREs)in 22 coupled model intercomparison project 6(CMIP6)models are evaluated against satellite observations.For the results from CMIP6 multi-model mean,cloud fraction(CF)peaks in autumn and is lowest in winter and spring,consistent with that from three satellite observation products(Cloud Sat-CALIPSO,CERESMODIS,and APP-x).Simulated CF also shows consistent spatial patterns with those in observations.However,almost all models overestimate the CF amount throughout the year when compared to CERES-MODIS and APP-x.On average,clouds warm the surface of the Arctic Basin mainly via the longwave(LW)radiation cloud warming effect in winter.Simulated surface energy loss of LW is less than that in CERES-EBAF observation,while the net surface shortwave(SW)flux is underestimated.The biases may result from the stronger cloud LW warming effect and SW cooling effect from the overestimated CF by the models.These two biases compensate each other,yielding similar net surface radiation flux between model output(3.0 W/m2)and CERES-EBAF observation(6.1 W/m2).During 2001–2014,significant increasing trend of spring CF is found in the multi-model mean,consistent with previous studies based on surface and satellite observations.Although most of the 22 CMIP6 models show common seasonal cycles of CF and liquid water path/ice water path(LWP/IWP),large inter-model spreads exist in the amounts of CF and LWP/IWP throughout the year,indicating the influences of different cloud parameterization schemes used in different models.Cloud Feedback Model Intercomparison Project(CFMIP)observation simulator package(COSP)is a great tool to accurately assess the performance of climate models on simulating clouds.More intuitive and credible evaluation results can be obtained based on the COSP model output.In the future,with the release of more COSP output of CMIP6 models,it is expected that those inter-model spreads and the model-observation biases can be substantially reduced.Longer term active satellite observations are also necessary to evaluate models’cloud simulations and to further explore the role of clouds in the rapid Arctic climate changes.
基金supported by National Natural Science Foundation of China(Grant No.42030605)National Key Research and Development Program of China(Grant No.2019YFC1510004)+2 种基金National Natural Science Foundation of China(Grant Nos.42088101 and 42005020)the General Program of Natural Science Research of Jiangsu Higher Education Institutions(19KJB170019)the Natural Science Foundation of Jiangsu Province(Grant No.BK20190781).
文摘The 2015/16 El Niño displayed a distinct feature in the SST anomalies over the far eastern Pacific(FEP)compared to the 1997/98 extreme case.In contrast to the strong warm SST anomalies in the FEP in the 1997/98 event,the FEP warm SST anomalies in the 2015/16 El Niño were modest and accompanied by strong southeasterly wind anomalies in the southeastern Pacific.Exploring possible underlying causes of this distinct difference in the FEP may improve understanding of the diversity of extreme El Niños.Here,we employ observational analyses and numerical model experiments to tackle this issue.Mixed-layer heat budget analysis suggests that compared to the 1997/98 event,the modest FEP SST warming in the 2015/16 event was closely related to strong vertical upwelling,strong westward current,and enhanced surface evaporation,which were caused by the strong southeasterly wind anomalies in the southeastern Pacific.The strong southeasterly wind anomalies were initially triggered by the combined effects of warm SST anomalies in the equatorial central and eastern Pacific(CEP)and cold SST anomalies in the southeastern subtropical Pacific in the antecedent winter,and then sustained by the warm SST anomalies over the northeastern subtropical Pacific and CEP.In contrast,southeasterly wind anomalies in the 1997/98 El Niño were partly restrained by strong anomalously negative sea level pressure and northwesterlies in the northeast flank of the related anomalous cyclone in the subtropical South Pacific.In addition,the strong southeasterly wind and modest SST anomalies in the 2015/16 El Niño may also have been partly related to decadal climate variability.
基金National Natu-ral Science Foundation of China(Grant Nos.42030605 and 42088101)National Key R&D Program of China(Grant No.2020YFA0608004).
文摘Accurate prediction of the summer precipitation over the middle and lower reaches of the Yangtze River(MLYR)is of urgent demand for the local economic and societal development.This study assesses the seasonal forecast skill in predicting summer precipitation over the MLYR region based on the global Climate Forecast System of Nanjing University of Information Science and Technology(NUIST-CFS1.0,previously SINTEX-F).The results show that the model can provide moderate skill in predicting the interannual variations of the MLYR rainbands,initialized from 1 March.In addition,the nine-member ensemble mean can realistically reproduce the links between the MLYR precipitation and tropical sea surface temperature(SST)anomalies,but the individual members show great discrepancies,indicating large uncertainty in the forecasts.Furthermore,the NUIST-CFS1.0 can predict five of the seven extreme summer precipitation anomalies over the MLYR during 1982-2020,albeit with underestimated magnitudes.The Weather Forecast and Research(WRF)downscaling hindcast experiments with a finer resolution of 30 km,which are forced by the large-scale information of the NUIST-CFS1.0 predictions with a spectral nudging method,display improved predictions of the extreme summer precipitation anomalies to some extent.However,the performance of the downscaling predictions is highly dependent on the global model forecast skill,suggesting that further improvements on both the global and regional climate models are needed.
基金supported by the Shanghai Artificial Intelligence Laboratory and National Natural Science Foundation of China(Grant No.42088101 and 42030605).
文摘Recent studies have shown that deep learning(DL)models can skillfully forecast El Niño–Southern Oscillation(ENSO)events more than 1.5 years in advance.However,concerns regarding the reliability of predictions made by DL methods persist,including potential overfitting issues and lack of interpretability.Here,we propose ResoNet,a DL model that combines CNN(convolutional neural network)and transformer architectures.This hybrid architecture enables our model to adequately capture local sea surface temperature anomalies as well as long-range inter-basin interactions across oceans.We show that ResoNet can robustly predict ENSO at lead times of 19 months,thus outperforming existing approaches in terms of the forecast horizon.According to an explainability method applied to ResoNet predictions of El Niño and La Niña from 1-to 18-month leads,we find that it predicts the Niño-3.4 index based on multiple physically reasonable mechanisms,such as the recharge oscillator concept,seasonal footprint mechanism,and Indian Ocean capacitor effect.Moreover,we demonstrate for the first time that the asymmetry between El Niño and La Niña development can be captured by ResoNet.Our results could help to alleviate skepticism about applying DL models for ENSO prediction and encourage more attempts to discover and predict climate phenomena using AI methods.
基金Supported by the National Key Research and Development Program of China(2018YFC1506000)National Natural Science Foundation of China(41975094 and 41275073).
文摘Remarkable progress has been made in observations,theories,and simulations of the ocean–atmosphere system,laying a solid foundation for the improvement of short-term climate prediction,among which Chinese scientists have made important contributions.This paper reviews Chinese research on tropical air–sea interaction,ENSO dynamics,and ENSO prediction in the past 70 years.Review of the tropical air–sea interaction mainly focuses on four aspects:characteristics of the tropical Pacific climate system and ENSO;main modes of tropical Indian Ocean SSTs and their interactions with the tropical Pacific;main modes of tropical Atlantic SSTs and inter-basin interactions;and influences of the mid–high-latitude air–sea system on ENSO.Review of the ENSO dynamics involves seven aspects:fundamental theories of ENSO;diagnosis and simulation of ENSO;the two types of ENSO;mechanisms of ENSO initiation;the interactions between ENSO and other phenomena;external forcings and teleconnections;and climate change and the ENSO response.The ENSO prediction part briefly summarizes the dynamical–statistical methods used in ENSO prediction,as well as the operational ENSO prediction systems and their applications.Lastly,we discuss some of the issues in these areas that are in need of further study.
基金Supported by the National Key Research and Development Program of China(2018YFC1507704,2017YFA0603804)National Natural Science Foundation of China(41905078)。
文摘Northeastern China has experienced a significant increase in summer compound hot and dry events(CHDEs),posing a threat to local agricultural production and sustainable development.This study investigates the detectable anthropogenic signal in the long-term trend of CHDE and quantifies the contribution of different external forcings.A probability-based index(PI)is constructed through the joint probability distribution to measure the severity of CHDE,with lower values representing more severe cases.Response of CHDE to external forcing was assessed with simulations from the Coupled Model Intercomparison Project phase 6(CMIP6).The results show a significant increase in the severity of CHDE over northeastern China during the past decades.The trend of regional averaged PI is-0.28(90%confidence interval:-0.43 to-0.13)per 54 yr and it is well reproduced in the historical forcing simulations.The attribution method of optimal fingerprinting was firstly applied to a two-signal configuration with anthropogenic forcing and natural forcing;the anthropogenic impact was robustly detected and it explains most of the observed trend of PI.Similarly,three-signal analysis further demonstrated that the anthropogenic greenhouse gases dominantly contribute to the observed change,while the anthropogenic aerosol and natural forcing have almost no contribution to the observed changes.For a compound event concurrently exceeding the 95 th percentile of surface air temperature and precipitation reversal in the current period,its likelihood exhibits little change at 1.5℃global warming,but almost doubled at 2.0℃global warming.
基金supported by the National Natural Science Foundation of China(42088101 and 42030605)support from the research project:Towards an Operational Fire Early Warning System for Indonesia(TOFEWSI)+1 种基金The TOFEWSI project was funded from October 2017-October 2021 through the UK’s National Environment Research Council/Newton Fund on behalf of the UK Research&Innovation(NE/P014801/1)(UK Principal InvestigatorAllan Spessa)(https//tofewsi.github.io/)financial support from the Natural Science Foundation of Qinghai(2021-HZ-811)。
