International Journal of Climatology最新文献

{"title":"Comparison of indicators to evaluate the performance of climate models","authors":"Mario J. Gómez,&nbsp;Luis A. Barboza,&nbsp;Hugo G. Hidalgo,&nbsp;Eric J. Alfaro","doi":"10.1002/joc.8619","DOIUrl":"https://doi.org/10.1002/joc.8619","url":null,"abstract":"<p>The evaluation of climate models is a crucial step in climate studies. It consists of quantifying the resemblance of model outputs to reference data to identify models with superior capacity to replicate specific climate variables. Clearly, the choice of the evaluation indicator significantly impacts the results, underscoring the importance of selecting an indicator that properly captures the characteristics of a “good model”. This study examines the behaviour of six indicators, considering spatial correlation, distribution mean, variance and shape. Monthly data for precipitation, temperature and teleconnection patterns in Central America were utilized in the analysis. A new multicomponent measure was selected based on these criteria to assess the performance of 32 CMIP6 models in reproducing the annual seasonal cycle of these variables. The top six models were determined using multicriteria methods. It was found that even the best model reproduces one derived climatic variable poorly in this region. The proposed measure and selection method can contribute to enhancing the accuracy of climatological research based on climate models.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 13","pages":"4907-4924"},"PeriodicalIF":3.5,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Projected climatic exposure and velocities of precipitation extremes over India and its biogeographic zones","authors":"Disha Sachan,&nbsp;Amita Kumari,&nbsp;Pankaj Kumar","doi":"10.1002/joc.8629","DOIUrl":"https://doi.org/10.1002/joc.8629","url":null,"abstract":"<p>Climate change is leading to alterations in the dynamic and thermodynamic climate systems worldwide, including the Indian summer monsoon (ISM), which supports more than a billion population and drives the Indian economy. The anthropogenic climate change induces unprecedented transformations in the natural and ecological systems, such as the increased probability of precipitation extremes, changes in their frequency, duration and spatial variabilities. This current study aims to project the regional landscape-based metric, velocity of climate change (VoCC) and associated climatic exposure regarding precipitation extremes (PEs) for India and its different biogeographic zones. The climate velocities of mean precipitation, 95th, 99.5th and 99.9th percentiles of precipitation for the ISM season are presented for the historical and three projected time slices under the RCP8.5 scenario. ROM, a state-of-the-art regional earth system model over the CORDEX-South Asia domain, was used in the study. It was observed that the intense and very intense rainfall (95th, 99.5th and 99.9th percentiles) was enhanced over most of the study region in the near- and mid-future compared to the far-future. The intense rainfall exhibited higher climate velocity than the mean and very intense precipitation in the near-future. The southern part of the Indian subcontinent usually displayed positive VoCC values for the historical and near-future time slices compared to the northern part of the Indian peninsula, particularly the intense and very intense precipitation. The climatic exposure for all-India was also higher in the near- and mid-future compared to the far-future, especially for the intense rainfall followed by the mean and very intense rainfall. These results suggest the need for focusing the adaptation and mitigation measures towards managing the near-term impacts of PEs in relation to the long-term impacts, especially on the country's diverse flora.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 14","pages":"5156-5171"},"PeriodicalIF":3.5,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Century-Long Variations in Surface Incident Solar Radiation Over Japan——Characterized by Observations and Reanalyses","authors":"Qian Ma,&nbsp;Han Liu","doi":"10.1002/joc.8640","DOIUrl":"https://doi.org/10.1002/joc.8640","url":null,"abstract":"<div>\u0000 \u0000 <p>Surface incident solar radiation (<i>R</i>\u0000 _{\u0000 <i>s</i>\u0000}) is of great importance in determining the energy balance in the Earth system. In this study, we use century-long homogenized observations over Japan to constrain five 20th century reanalyses to explore their performance in reproducing <i>R</i>\u0000 _{\u0000 <i>s</i>\u0000} variation on different time scales (high-frequency components [HFCs], for signals with cycles less than 10 years; low-frequency components [LFCs], for signals with cycles more than 10 years) by ensemble empirical mode decomposition (EEMD) method and to quantify the impact factors on the <i>R</i>\u0000 _{\u0000 <i>s</i>\u0000} estimations by the sum of tree (SOT) model. It is found that ERA20C, ERA20CM and CERA20C overestimated <i>R</i>\u0000 _{\u0000 <i>s</i>\u0000} by 0.48–1.49 W/m⁻², while 20CRv2c and 20CRv3 underestimated <i>R</i>\u0000 _{\u0000 <i>s</i>\u0000} by −0.63 and −1.18 W/m⁻², respectively for 1931–2010. Poor correlation coefficient (<i>R</i>) was found to be 0.10 for ERA20CM and 0.22 for 20CRv2c. 20CRv2c failed for 1931–1960 but improved considerably for 1961–2010. 20CRv3 uses an upgraded model and assimilates more observations compared with its predecessor 20CRv2c; however, only the original components and HFCs in <i>R</i>\u0000 _{\u0000 <i>s</i>\u0000} were improved, with nearly no improvement in the LFCs. In general, CERA20C <i>R</i>\u0000 _{\u0000 <i>s</i>\u0000}, with small biases and higher <i>R</i> of 0.73 for original signals, 0.83 for HFCs and LFCs, is superior to other reanalyses. No obvious trend in clear sky <i>R</i>\u0000 _{\u0000 <i>s</i>\u0000} demonstrated that reanalysed <i>R</i>\u0000 _{\u0000 <i>s</i>\u0000} are insensitive to the aerosol forcings. Therefore, cloud cover and water vapour maybe the main factors influenced reanalysed <i>R</i>\u0000 _{\u0000 <i>s</i>\u0000}. Most of time, <i>R</i>\u0000 _{\u0000 <i>s</i>\u0000} is more sensitive to cloud cover than vapour pressure for all reanalyses except original signals (with contribution ratio of 0.29 for cloud cover and 0.71 for vapour pressure) and LFCs (with contribution ratio of 0.41 for cloud cover and 0.59 for vapour pressure) in CERA20C, and original signals (with contribution ratio of 0.37 for cloud cover and 0.63 for vapour pressure) in ERA20C. This work pointed out that aerosol related processes such as aerosol forcings or aerosol radiative effect in reanalyses should be improved in the future, which will ultimately improve the interaction between aerosol and cloud in <i>R</i>\u0000 _{\u0000 <i>s</i>\u0000} simulations.</p>\u0000 </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 15","pages":"5358-5370"},"PeriodicalIF":3.5,"publicationDate":"2024-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142762881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving drought monitoring using climate models with bias-corrected under Gaussian mixture probability models","authors":"Rubina Naz,&nbsp;Zulfiqar Ali,&nbsp;Veysi Kartal,&nbsp;Mohammed A. Alshahrani,&nbsp;Shreefa O. Hilali,&nbsp;Fathia Moh. Al Samman","doi":"10.1002/joc.8618","DOIUrl":"https://doi.org/10.1002/joc.8618","url":null,"abstract":"<p>Global climate models (GCMs) are extensively used to calculate standardized drought indices. However, inaccuracies in GCM simulations and uncertainties inherent in the standardization methodology limit the precision of drought evaluations. The objective of this research is to remove bias in GCMs for improving drought monitoring and assessment. Consequently, this article proposes a new framework for drought index under the ensemble of GCMs—Multi-Model Quantile Mapped Standardized Precipitation Index (MMQMSPI). In accordance of Standardized Precipitation Index (SPI), the second stage derives a new index by assessing the feasibility of parametric and nonparametric models during standardization. In the application, we used 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) data of precipitation across 32 grid points within the Tibetan Plateau region. The comparative findings reveal that the integration of KCGMD is the most suitable choice compared to other best-fitted univariate distributions in both features of the proposed framework. In this research, we assess the implications of evaluating future patterns of drought for the years 2015–2100 using seven different time periods and three different future scenarios. Temporal behavior clearly shows monthly variations in the pattern of MMQMSPI, and these variations differ on each time scale, but a drastic change can be seen over the long term, i.e., extreme dry and wet conditions, with a higher probability in all scenarios.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 14","pages":"4984-5008"},"PeriodicalIF":3.5,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the Changes in Moisture Budget of Extreme Wet Indian Summer Monsoon Precipitation in CMIP6","authors":"Pookkandy Byju,&nbsp;Santosh Kumar Muruki,&nbsp;Milan Mathew,&nbsp;Kaagita Venkatramana,&nbsp;K. S. Krishnamohan","doi":"10.1002/joc.8636","DOIUrl":"https://doi.org/10.1002/joc.8636","url":null,"abstract":"<div>\u0000 \u0000 <p>Climate change is expected to have a considerable impact on precipitation leading to more intense and frequent extreme events. Considering the different driving mechanisms of precipitation extreme is essential to understand the changes in response to climate change. In this study, we decompose the intensity of extreme wet month precipitation (EWMP) during the Indian summer monsoon (ISM) into atmospheric dynamic, thermodynamic and non-linear components by using moisture budget estimation. The data from 19 Coupled Model Intercomparison Project phase-6 (CMIP6) models are used for historical, intermediate (SSP2-4.5), and high-emission (SSP5-8.5) scenarios and the changes are estimated for near (2021–2040), mid (2041–2060), and far-future (2081–2100) relative to the historical (1995–2014) period for different monsoon sub-domains. The findings reveal a significant increase in the intensity of EWMP in the ISM, projecting 2%–12% in SSP2-4.5 and 8%–25% in SSP5-8.5 for the far-future. The enhanced vertical ascent of moisture (V-Dyn) is found to be a dominant factor contributing more than 70% to EWMP in most sub-domains. However, regardless of enhancement in intensity of precipitation, the models simulate a reduction in impact of the V-Dyn by 10%–35% from the near to far-future period, particularly in high emission scenarios. Vertical thermodynamic and non-linear moisture advection components also play minor roles (&lt;5% in historical), with their influence gradually increasing with future warming (&gt;15% in SSP5-8.5). The responses also vary regionally for components such as horizontal dynamic term, where it leads to precipitation offset in the northern regions, but causes enhanced precipitation in southern regions. The study highlights the spatial and temporal variability of moisture budgets of extreme wet Indian summer monsoon precipitation in a warming environment.</p>\u0000 </div>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 14","pages":"5266-5279"},"PeriodicalIF":3.5,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High Resolution Köppen-Geiger Climate Zones of Türkiye","authors":"Enes Taşoğlu,&nbsp;Muhammed Zeynel Öztürk,&nbsp;Öznur Yazıcı","doi":"10.1002/joc.8635","DOIUrl":"https://doi.org/10.1002/joc.8635","url":null,"abstract":"<p>The Köppen-Geiger (K-G) climate classification is the most commonly used climate classification method in the world, and there are many K-G climate classification studies focusing on Türkiye using different datasets. However, the differences in the datasets used in these studies lead to substantial differences and errors in K-G climate zone maps. The differences and disagreements in these maps also cause significant discrepancies in climate studies. In this respect, accurate identification of climate classes and types is very important for understanding the distribution of climate types and for many climate-based studies to achieve accurate results. In this study, the K-G climate types of Türkiye and the regime characteristics of these climate types were determined using the CHELSA dataset corrected based on the measurements of 337 meteorological stations. According to the results that were obtained, 14 climate types were identified in Türkiye. Since the CHELSA dataset reflected topographic conditions well, many microclimates were identified within broad areas of climate types. The distribution of the microclimate types was compared to the distribution of the vegetation, and the accuracy of the results was evaluated. Apart from microclimates, other prominent features of this study were the co-occurrence of multiple climate types in a limited area in the Eastern Black Sea Region and the detection of the EF climate type for the first time at the summit of Mount Ararat. Climate types vary according to altitude conditions, and temperature changes due to altitude are an important factor in the formation of climate sub-types within the same main climate type in Türkiye.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 14","pages":"5248-5265"},"PeriodicalIF":3.5,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/joc.8635","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of subseasonal precipitation forecasts in the Uruguay River basin","authors":"Juan Badagian,&nbsp;Marcelo Barreiro,&nbsp;Ramiro I. Saurral","doi":"10.1002/joc.8634","DOIUrl":"https://doi.org/10.1002/joc.8634","url":null,"abstract":"<p>The development of subseasonal forecasts has seen significant advancements, transforming our ability to predict weather patterns and climate variability on intermediate timescales ranging from 2 weeks to 2 months. Motivated by the need to enhance our understanding of subseasonal precipitation forecasts and their applicability to the hydrology forecast, this study retrospectively analysed precipitation ensemble forecasts from subseasonal prediction models in the Uruguay River basin nearby Salto Grande dam. Three models were considered: two from the S2S project (ECMWF and CNRM) and one from the SubX project (GEFS). Model forecasts were analysed on a weekly time scale using both deterministic and probabilistic approaches. Multimodel probabilistic forecasts combining the three different models were built to increase forecast skill. Individual models have a skill larger than or equal to the climatological forecast until 2 weeks in advance. Particularly, ECMWF shows better skill in both ensemble mean and probabilistic forecast. Multimodel probabilistic forecast improves the skill of the forecast throughout the year, with the skill even surpassing the climatological forecast by up to 4 weeks in advance during the summer. In addition, model skill was analysed considering the state of the El Niño–Southern Oscillation (ENSO) on a weekly and monthly basis. On weekly time scales the ENSO state modifies model skill differently depending on the sub-basin and season considered. However, the influence of ENSO on forecast skill is more clearly observed on monthly time scales, with largest improvement in the lower basin during springtime. The results of this work suggest that subseasonal models are a promising tool to bridge the gap between weather and climate forecast in the Uruguay River basin and have the potential to be utilized for hydrological forecasting in the study region.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 14","pages":"5233-5247"},"PeriodicalIF":3.5,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dissecting changes in evapotranspiration and its components across the Losses Plateau of China during 2001–2020","authors":"Shanlei Sun,&nbsp;Aoge Ma,&nbsp;Yibo Liu,&nbsp;Menyuan Mu,&nbsp;Yi Liu,&nbsp;Yang Zhou,&nbsp;Jinjian Li","doi":"10.1002/joc.8633","DOIUrl":"https://doi.org/10.1002/joc.8633","url":null,"abstract":"<p>China's Losses Plateau (LP) is one of the ecologically vulnerable and the most severe soil erosion regions. Thus, knowing spatiotemporal changes in evapotranspiration (ET) and its components (soil evaporation, E; transpiration, T; and vegetation interception evaporation, EI) and revealing the underlying mechanisms are vital for ecosystem and water resources sustainability for this region. Here, we investigate the spatiotemporal changes in ET and its components and then quantify the impacts of climate variables (i.e., precipitation, radiation, temperature, and relative humidity) and vegetation dynamics (e.g., land use/cover changes [LUCC] and changes in leaf area index [LAI]) on their annual trends, by using a process-based terrestrial ecosystem model and a joint-solution method with multiple sensitivity numerical experiments. Results show that over 67% of the study region experienced significant (<i>p</i> &lt; 0.05) increases in annual ET, T, and EI, with regional average rises of 4.05, 3.67, and 0.74 mm·year⁻¹, respectively. However, there are significant (<i>p</i> &lt; 0.05) decreases in regional mean E of 0.38 mm·year⁻¹, and the negative trend covers 35.8% of the study area. E, T, and EI changes dominate the annual ET trends over 11.8%, 87.3%, and 0.9% of the study area, respectively. Attribution analyses highlight the increased LAI as the critical factor governing these trends across most of the LP (&gt;58%). At the same time, precipitation and LUCC play a more dominant role in the remaining areas. This study emphasizes the spatial heterogeneity in the drivers of changes in ET and its components and highlights the critical role of vegetation dynamics. These findings provide valuable insights for understanding the ET processes and guiding sustainable water resource management in the LP.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 14","pages":"5207-5232"},"PeriodicalIF":3.5,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142665006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variations and comparisons in hourly and daily precipitation extremes over eastern China in recent warming periods","authors":"Yimeng Qi,&nbsp;Danqing Huang,&nbsp;Jiao Chen,&nbsp;Jinwen Zeng,&nbsp;Anqi Liu","doi":"10.1002/joc.8632","DOIUrl":"https://doi.org/10.1002/joc.8632","url":null,"abstract":"<p>Under global warming, a notable intensification of precipitation extremes has posed significant threats to both natural ecosystems and human societies. However, there is still a lack of systematic studies on the variability of hourly precipitation extremes and a comparison with daily precipitation extremes. This study introduces a set of indices for hourly and daily precipitation extremes to systematically examine the changes across eastern China during the summers from 1980 to 2020. The results show that southern China is the sensitive regions with a notable large value of climatology in frequency and intensity for both of hourly and daily precipitation extremes. For the trends, the frequency of hourly precipitation extremes shows a rising trend in southern China, and intensity increased in northern China. Conversely, for daily precipitation extremes, the enhanced intensity and frequency are observed only in southeastern China. Additionally, the eastern coastal area experiences prolonged hourly rainfall with increasing persistence, while the region with an increased duration of daily precipitation is situated further south. Comparing the periods 1980–1999 and 2000–2020, precipitation extremes shift toward more intense and more frequent, with more pronounced variations at the hourly scale. Notably, northeastern China tend to be more intense and shorter-lived hourly precipitation extremes after 1999, while south China experiences more frequent and longer-lasting extremes. These changes are partly attributed to the increased unstable conditions, enhanced convective available potential energy and weakened convective inhibition.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 14","pages":"5192-5206"},"PeriodicalIF":3.5,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensemble forecasting of Indian Ocean Dipole events generated by conditional nonlinear optimal perturbation method","authors":"Rong Feng,&nbsp;Wansuo Duan,&nbsp;Lei Hu,&nbsp;Ting Liu","doi":"10.1002/joc.8627","DOIUrl":"https://doi.org/10.1002/joc.8627","url":null,"abstract":"<p>In this study, we applied the conditional nonlinear optimal perturbation (CNOP) method to generate nonlinear fast-growing initial perturbations for ensemble forecasting, aiming to assess the effectiveness of the CNOP method in improving the forecast skill of climate events. Our findings reveal a significant improvement in the forecast skill of the Indian Ocean Dipole (IOD) within the CNOP ensemble forecast, particularly at long lead times, thereby extending the skilful forecast lead times. Notably, this improvement is more prominent for strong IOD events, with skilful forecast lead times exceeding 12 months, outperforming many current state-of-the-art coupled models. The high forecast skill of the CNOP method is primarily attributed to its ability to capture the uncertainties in the wind anomaly field in the eastern Indian Ocean (EIO) closely associated with IOD evolution. Consequently, CNOP ensemble members exhibit significant deviations from the control forecast, resulting in a large ensemble spread encompassing IOD evolution. Furthermore, a comparison with the climate-relevant singular vectors (CSV) method in terms of IOD and El Niño–Southern Oscillation (ENSO) predictions reveals the superior performance of the CNOP ensemble forecast. Despite the initial perturbations for ensemble forecasting being generated aimed at improving IOD forecast skill, the CNOP method significantly improves the forecast skill of both IOD and ENSO events, with a greater improvement for ENSO. Additionally, the CNOP ensemble forecast system provides more reliable estimates of forecast uncertainties and exhibits higher reliability with increasing lead times. In conclusion, the CNOP method effectively captures the nonlinear physical processes of climate events and improve the forecast skill.</p>","PeriodicalId":13779,"journal":{"name":"International Journal of Climatology","volume":"44 14","pages":"5119-5135"},"PeriodicalIF":3.5,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}