Search

The Madden–Julian Oscillation (MJO) provides an important source of global subseasonal-to-seasonal (S2S) predictability, while its prediction remains great challenges. Based on an atmosphere–ocean coupled model and the widely-used nudging method, suitable initialization and ensemble schemes are explored toward an improved MJO prediction. Results show that the ensemble strategy with perturbed atmospheric nudging coefficients facilitates adequate ensemble spread and hence improves the prediction skill, particularly for weak cases. Finally, an 18-member ensemble subseasonal prediction system called NUIST CFS1.1 is developed. Skill evaluation indicates that the NUIST CFS1.1 can extend the MJO prediction to 24 days lead, which reaches the world-average level but is far from the estimated potential predictability (~ 45 days). The limited skill at longer lead times corresponds to forecast errors exhibiting slower propagation and weaker intensity, which are jointly caused by the initial errors and model's imperfections. The latter is associated with unrealistic representations of MJO-related physical processes. The model underestimates the diabatic heating (mostly contributed by the latent heat release) of enhanced convection and fails to reproduce the suppressed convection within the MJO structure, collaboratively weakening the Kelvin/Rossby waves. This causes weaker horizontal winds and ultimately reduces the horizontal moisture advection on the two flanks of MJO convection. Furthermore, the underestimated Kelvin wave induces insufficient planetary boundary layer (PBL) convergence and thereby results in poor simulation of PBL premoistening ahead of MJO convection. Interestingly, the commonly dry bias is absent in our model, and the bias in horizontal moisture gradients may have a minor impact on the MJO propagation. Further efforts are urged to improve the model physics involving cumulus and PBL processes. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the 2018–2019 winter, an unprecedented pluvial event occurred in the Yangtze‐Huai River Basin (YHRB), which is one of the most developed areas in China. This was a record over the past 40 years. Other notable events during this period were the events of the 2002–2003, 1997–1998, and 1989–1990 winters. This study aims to examine the ability of the Scale Interaction Experiment‐Frontier (SINTEX‐F) to predict these extremely pluvial winters and its predictability source. Extreme precipitation events in the 2018–2019 and 1997–1998 winters were reasonably well predicted 1 month earlier by the ensemble seasonal prediction system from deterministic and probabilistic perspectives. In contrast, the predictions of extremely pluvial winters in 2002–2003 and 1989–1990 were less skillful in some parts of the YHRB. The analysis of co‐variability of inter‐member anomalies suggested that the SST anomalies in the tropical western Indian Ocean (IO) and the tropical Pacific Ocean were responsible for the predictions of the 2018–2019 and 1997–1998 winters through increased low‐level moisture convergences in the YHRB as well as wave trains along the upper westerly jet. Limited performance in predicting the 2002–2003 and 1989–1990 events might be due to less accurate prediction of low‐level circulation around the YHRB associated with the SST anomalies in the tropical eastern IO, western IO, and the Pacific, respectively. Besides, atmospheric internal variability as well as model biases may also limit the deterministic prediction skills. Plain Language Summary: In the 2018–2019 winter, an unprecedented pluvial event occurred in the Yangtze‐Huai River Basin (YHRB), known as one of the most developed and densely populated areas in China. Such a pluvial winter leads to regional flooding. There have been three additional extremely wet winters in the YHRB since 1979 and we noted the variation of the predictability of these extreme events. Warm anomalies in the tropical western Indian Ocean and Pacific played an important role in these unusually wet conditions over the YHRB during the 2018–2019 and 1997–1998 winters. Extremely pluvial winters during 2002–2003 (1989–1990) were also influenced by SST anomalies in the tropical eastern Indian Ocean (western Indian Ocean and Pacific), but prediction of the SST anomalies in these events was limited. Skillful predictions of the extreme winter conditions may help reduce their devastating impacts. Key Points: The Yangtze‐Huai River basin in China suffered from an extreme wet condition during the December–January–February 2018–2019SINTEX‐F successfully predicted extremely pluvial winters in 2018–2019 and 1997–1998, while being less skillful in 2002–2003 and 1989–1990 wintersTropical western Indian Ocean and Pacific SST anomalies played key roles in the seasonal predictability of the 2018–2019 and 1997–1998 events [ABSTRACT FROM AUTHOR]

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 (NUIST-CFS1.0). 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 Niño—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. [ABSTRACT FROM AUTHOR]

Pacific Meridional Mode (PMM) is known to be significantly correlated with tropical cyclone (TC) genesis over the western North Pacific (WNP), while the stability of their relationship remains unknown. Here we found that their relationship is nonstationary, which depends on the phase of Pacific Decadal Oscillation (PDO). During the PDO warm phases, the PMM‐emanated cyclonic circulation and ascending motion can propagate to the entire WNP due to the enhanced background convection. In contrast, during the PDO cold phases, the PMM‐resulted cyclonic circulation and ascending motion are confined to the eastern WNP, while the compensated descending motion prevails in the western WNP. Accordingly, the PMM‐induced consistent (inconsistent) changes in large‐scale conditions across the western and eastern WNP act to strengthen (weaken) the relationship between the PMM and WNP TC genesis during the PDO warm (cold) phases. The result provides further guidance for improving seasonal prediction of TC genesis. Plain Language Summary: Billions of people in the Pacific islands and Asian coastal regions are subject to enormous tropical cyclone (TC) induced disasters. The Pacific Meridional Mode (PMM), a seasonally evolving mode of coupled climate variability, has a prominent impact on TC genesis in the western North Pacific (WNP) and is usually used as an important predictor for seasonal forecasting of TC genesis. However, stability in the relationship between PMM and TC genesis remains unclear. Here we found that their relationship is nonstationary and depends on the phase of the Pacific Decadal Oscillation (PDO), a decadal fluctuation of the Pacific Ocean. The result highlights the crucial role of PDO in modulating the relationship between the PMM and WNP TC genesis and thus provides further guidance for seasonal forecasting scheme of TC genesis. Key Points: The relationship between the Pacific Meridional Mode (PMM) and tropical cyclone genesis over the western North Pacific is nonstationaryThe nonstationary relationship stems from the diverse atmospheric responses to PMM that depend on the phase of Pacific Decadal Oscillation [ABSTRACT FROM AUTHOR]

Seasonal forecasting models have a harder time accurately predicting regional precipitation anomalies in East Asia than the large‐scale drivers of these precipitation anomalies. These large‐scale drivers include both sea surface temperature (SST) anomalies and conditions in the polar stratosphere. A maximum covariance analysis is used to isolate the SST and stratospheric patterns most associated with anomalous East Asian precipitation, which are employed to correct seasonal forecasts by projecting the forecasted stratospheric circulation and SST onto the observed combined modes. The nonuniform predictability of East Asian rainfall in seasonal forecast models is improved after error correction with the observed linkage between the stratosphere–tropical ocean modes and East Asian rainfall. This method should be helpful for improving rainfall forecasts in other regions as well. Plain Language Summary: Seasonal predictability of East Asian rainfall in summer is mainly associated with sea surface temperatures (SSTs) across the tropical Pacific and Indian Oceans. In contrast, East Asian rainfall in spring can be affected by both the state of tropical oceans and downward propagation of extratropical stratospheric variability. This study reports the most common combinations between the extratropical stratosphere and tropical SST in early spring, which in turn are associated with East Asian rainfall variations in spring. Rainfall in some regions like Japan and near the China‐Russia border is poorly forecasted in most seasonal models. After error correction based on the physical linkage between the most frequent stratosphere–tropical SST combinations and East Asian rainfall, an improvement especially in less predictable regions is identified in seasonal forecast models. Key Points: A novel method is proposed to improve (i.e., error correct) seasonal forecasts of East Asian spring precipitationMaximum covariance analysis is used to identify large‐scale drivers associated with rainfallThese drivers are better predicted by models than rainfall, and can be utilized to improve upon the nonuniform predictability of East Asian spring rainfall [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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 (NUIST-CFS1.0) 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. [ABSTRACT FROM AUTHOR]

Accurate prediction of extreme weather and climate is crucial for their devastating impacts on our society and environment. Here, predictability of frequency of summer (June–August) extreme hot days (SEHDs) in China has been assessed in the SINTEX‐F2 seasonal forecast system during the period of 1983–2015. The hindcast had 12 ensemble members and was initialized on the first day of March, April and May, respectively. Results show that overall, the SINTEX‐F2 predicts more regions with good prediction skills at shorter lead time due to its better capture of the linear trends. Whereas, in Southwest China and eastern Tibetan Plateau, the prediction skills are consistently increased at shorter lead time even without the impacts of linear trends; the correlation coefficients between the region‐mean anomalies in the observed and predicted SEHD frequencies are 0.74, 0.68 and 0.61 at 1–3 month lead, respectively, and they remain as high as 0.56, 0.49 and 0.40 when the linear trends are removed. This is because the SINTEX‐F2 can reproduce the observed influences of Indian Ocean Basin Mode (IOBM) on the SEHD frequency. The warm IOBM can cause anomalous ascending and strong divergence in the upper troposphere over the tropical Indian Ocean and Indian subcontinent. The strong divergence causes direct convergence and descending over Southwestern China and eastern Tibetan Plateau. It also causes substantial descending over the western North Pacific that can introduce anomalous cyclonic circulation over northeastern Asia via the Pacific‐Japan or East‐Asia pattern. The latter serves as a source of wave energies, excites the Rossby waves meandering westward in the mid‐latitude, and enhances the anticyclonic circulation anomalies over Southwestern China and eastern Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Regional climate projections are challenging because of large uncertainty particularly stemming from unpredictable, internal variability of the climate system. Here, we examine the internal variability-induced uncertainty in precipitation and surface air temperature (SAT) trends during 2005–2055 over East Asia based on 40 member ensemble projections of the Community Climate System Model Version 3 (CCSM3). The model ensembles are generated from a suite of different atmospheric initial conditions using the same SRES A1B greenhouse gas scenario. We find that projected precipitation trends are subject to considerably larger internal uncertainty and hence have lower confidence, compared to the projected SAT trends in both the boreal winter and summer. Projected SAT trends in winter have relatively higher uncertainty than those in summer. Besides, the lower-level atmospheric circulation has larger uncertainty than that in the mid-level. Based on k-means cluster analysis, we demonstrate that a substantial portion of internally-induced precipitation and SAT trends arises from internal large-scale atmospheric circulation variability. These results highlight the importance of internal climate variability in affecting regional climate projections on multi-decadal timescales. [ABSTRACT FROM AUTHOR]

seasonal forecast system based on a global, fully coupled ocean-atmosphere general circulation model is used to (1) evaluate the interannual predictability of the Northwest Pacific climate during June-August following El Niño [JJA(1)], and (2) examine the contribution from the tropical Indian Ocean (TIO) variability. The model retrospective forecast for 1983-2006 captures major modes of atmospheric variability over the Northwest Pacific during JJA(1), including a rise in sea level pressure (SLP), an anomalous anticyclone at the surface, and a reduction in subtropical rainfall, and increased rainfall to the northeast over East Asia. The anomaly correlation coefficient (ACC) for the leading principal components (PCs) of SLP and rainfall stays above 0.5 for lead time up to 3-4 months. The predictability for zonal wind is slightly better. An additional experiment is performed by prescribing the SST climatology over the TIO. In this run, designated as NoTIO, the Northwest Pacific anticyclone during JJA(1) weakens considerably and reduces its westward extension. Without an interactive TIO, the ACC for PC prediction drops significantly. To diagnose the TIO effect on the circulation, the differences between the two runs (Control minus NoTIO) are analyzed. The diagnosis shows that El Nino causes the TIO SST to rise and to remain high until JJA(1). In response to the higher than usual SST, precipitation increases over the TIO and excites a warm atmospheric Kelvin wave, which propagates into the western Pacific along the equator. The decrease in equatorial SLP drives northeasterly wind anomalies, induces surface wind divergence, and suppresses convection over the subtropical Northwest Pacific. An anomalous anticyclone forms in the Northwest Pacific, and the intensified moisture transport on its northwest flank causes rainfall to increase over East Asia. In the NoTIO experiment, the Northwest Pacific anticyclone weakens but does not disappear. Other mechanisms for maintaining this anomalous circulation are discussed. [ABSTRACT FROM AUTHOR]

The middle and lower reaches of the Yangtze River valley (YRV), which are among the most densely populated regions in China, are subject to frequent flooding. In this study, the predictor importance analysis model was used to sort and select predictors, and five methods (multiple linear regression (MLR), decision tree (DT), random forest (RF), backpropagation neural network (BPNN), and convolutional neural network (CNN)) were used to predict the interannual variation of summer precipitation over the middle and lower reaches of the YRV. Predictions from eight climate models were used for comparison. Of the five tested methods, RF demonstrated the best predictive skill. Starting the RF prediction in December, when its prediction skill was highest, the 70-year correlation coefficient from cross validation of average predictions was 0.473. Using the same five predictors in December 2019, the RF model successfully predicted the YRV wet anomaly in summer 2020, although it had weaker amplitude. It was found that the enhanced warm pool area in the Indian Ocean was the most important causal factor. The BPNN and CNN methods demonstrated the poorest performance. The RF, DT, and climate models all showed higher prediction skills when the predictions start in winter than in early spring, and the RF, DT, and MLR methods all showed better prediction skills than the numerical climate models. Lack of training data was a factor that limited the performance of the machine learning methods. Future studies should use deep learning methods to take full advantage of the potential of ocean, land, sea ice, and other factors for more accurate climate predictions. [ABSTRACT FROM AUTHOR]

Variations of East Asian westerly jet (EAJ) drastically affect the rainfall in southern China (SC) during Meiyu season. Previous studies conventionally focused on the entire EAJ's variations and their impacts. In this study, we find that the variations of EAJ eastern section (EAJE) and western section (EAJW) are mostly independent of each other. Moreover, the independent components of EAJE and EAJW's variations have different impacts on the rainfall in SC. Southward-displaced and intensified EAJE (SI-EAJE) and southward-displaced and weakened EAJW (SW-EAJW), the first two leading modes of EAJ variations, are associated with increased rainfall anomalies centred over central and southern parts of SC, respectively. Associated with the SI-EAJE (SW-EAJW), anomalous cold cyclone is centred around the Japan Sea (central China). The associated anomalous westerly wind and northward-decreasing (northwestward-decreasing) temperature centred over the central (southern) SC together induce midtropospheric warm advection and thus positive rainfall anomalies centred over the central (southern) SC. The anomalous circulations also cause moisture convergences roughly collocated with the anomalous warm advections over the SC, which intensifies the rainfall anomalies. Warm sea surface temperature anomalies in the equatorial central-eastern Pacific during boreal spring induce meridional teleconnection along the coastal East Asia in Meiyu season through suppressing the convective activities in the northwestern Pacific. The resultant SI-EAJE and associated anomalous circulations increase the rainfall over the central SC, and vice versa. Dipole of increased (decreased) spring snow anomalies on the eastern (western) Tibetan Plateau suppresses (intensifies) convective activities in Meiyu season. The resultant SW-EAJW and associated anomalous circulations increase the rainfall in the southern SC. • Interannual variations of EAJ eastern and western section are primarily independent. • The two EAJ sections cause different Meiyu rainfall anomalies in the southern China. • Thermal advection and moisture anomalies induce the different rainfall anomalies. • Variations of EAJ eastern section can be induced by preceding ENSO in boreal spring. • Dipole of spring snow anomalies on Tibetan Plateau changes the EAJ western section. [ABSTRACT FROM AUTHOR]