Operational Climate Prediction Research Articles

Possibilities for Improvement in Long-term Predictions of the Operational Climate Prediction System (GloSea6) for Spring by including Atmospheric Chemistry-Aerosol Interactions over East Asia

Possibilities for Improvement in Long-term Predictions of the Operational Climate Prediction System (GloSea6) for Spring by including Atmospheric Chemistry-Aerosol Interactions over East Asia

Persistent Meteorological Drought in the Yangtze River Basin during Summer–Autumn 2022: Relay Effects of Different Atmospheric Internal Variabilities

During the summer–autumn (July–October, Jul–Oct) period of 2022, the Yangtze River Basin (YRB) of China experienced an extreme meteorological drought, with Jul–Oct containing the lowest precipitation in the YRB since 1979. The possible causes of this drought were analyzed in the present study. Surprisingly, unlike many previous drought events, we found that this event was not characterized by a consistent atmospheric circulation anomaly regime throughout the entire drought period. Instead, two distinct circulation patterns were responsible for the precipitation deficit in two different stages, i.e., July–August (Jul–Aug) and September–October (Sep–Oct). In Jul–Aug, the YRB precipitation deficit primarily resulted from an intensified and northward-shifted East Asian subtropical jet, which allowed for an extremely northwestward shift of western Pacific subtropical highs, leading to an anomalous descending motion. Such circulation patterns in Jul–Aug originated from the dispersion of Rossby waves upstream from central Asia and Europe. Meanwhile, in Sep–Oct, the YRB drought was primarily attributed to a low-level cyclonic anomaly over the western North Pacific, which was closely associated with frequent tropical cyclones traveling across this region. Observational analysis and a model ensemble hindcast suggest that atmospheric internal variabilities dominated the drought process, while the SSTA, particularly the La Niña event, played a limited role. Therefore, this long-lasting extreme YRB meteorological drought was largely driven by the relay effects of different atmospheric internal variabilities in Jul–Aug and Sep–Oct, respectively, which shows limited model predictability and poses a great challenge for operational climate predictions.

Seasonal predictability of summer north african subtropical high in operational climate prediction models

Seasonal predictability of summer North African Subtropical High (NASH) is investigated in this study by utilizing the hindcast data from four operational climate prediction models, including BCC_CSM1.1(m), NCEP CFSv2, ECMWF System 4, and JMA CPSv2. By reconstructing indices describing the variations in intensity, area, eastern boundary and ridge line of the NASH, it is shown that the intensity and area indices present high prediction skills compared to the relatively low prediction skills of position indices. The multi-model ensemble (MME) mean, calculated as the arithmetic average of the four models, presents relatively higher and stabler skills than individual models. Further investigation indicates that the prediction skill of the NASH is largely reliant on the models’ ability in reproducing the relationship between the NASH indices and the tropical-to-subtropical sea surface temperature (SST) anomalies associated with the El Niño/Southern Oscillation (ENSO). The pattern of atmospheric circulation anomaly over the North Africa in response to ENSO is well captured by the models, which suggests the dominant source of predictability of the NASH.

Evaluation on the Forecast Skills of Precipitation and Its Influencing Factors in the Flood Season in Liaoning Province of China

To clarify the precipitation forecast skills of climate forecast operations in the flood season in Liaoning Province of China, this study examines the forecast accuracies of China’s national and provincial operational climate prediction products and the self-developed objective prediction methods and climate model products by Shenyang Regional Climate Center (SRCC) in the flood season in Liaoning. Furthermore, the forecast accuracies of the main influencing factors on the precipitation in the flood season of Liaoning are assessed. The results show that the SRCC objective methods have a relatively high accuracy. The European Centre for Medium-Range Weather Forecasts (ECMWF) sub-seasonal forecast initialized at the sub-nearest time has the best performance in June. The National Climate Center (NCC) Climate System Model sub-seasonal forecast initialized at the sub-nearest time, and the ECMWF seasonal and sub-seasonal forecasts initialized at the nearest time, perform the best in July. The NCC sub-seasonal forecast initialized at the sub-nearest time has the best performance in August. For the accuracy of the SRCC objective method, the more significant the equatorial Middle East Pacific sea surface temperature (SST) anomaly is, the higher the evaluation score of the dynamic–analogue correction method is. The more significant the North Atlantic SST tripole is, the higher the score of the hybrid downscaling method is. For the forecast accuracy of the main influencing factors of precipitation, the tropical Atlantic SST and the north–south anti-phase SST in the northwest Pacific can well predict the locations of the southern vortex and the northern vortex in early summer, respectively. The warm (clod) SST in China offshore has a good forecast performance on the weak (strong) southerly wind in midsummer in Northeast China. The accuracy of using the SST in the Nino 1+2 areas to predict the north–south location of the western Pacific subtropical high is better than that of using Kuroshio SST. The accuracy of predicting northward-moving typhoons from July to September by using the SST in the west-wind-drift area is better than using the SST in the Nino 3 area. The above conclusions are of great significance for improving the short-term climate prediction in Liaoning.

Subseasonal predictions for climate services, a recipe for operational implementation

The implementation of operational climate service prototypes, which encompasses the co-design and delivery of real-time actionable products with/to stakeholders, contributes to efficiently leveraging operational climate predictions into actionable climate information by providing practical insight on the actual use of climate predictions. This work showcases a general guideline for implementing an operational climate service based on subseasonal predictions. At this timescale, many strategic decisions can benefit from timely predictions of climate variables. Still, the use of subseasonal predictions is not fully exploited. Here, we describe the key aspects considered to set up an operational climate service from the conception to the production phase. These include the choice of the subseasonal systems, the data sources and the methodology employed for post-processing the predictions. To illustrate the process with a real case, we present the detailed workflow design of the implementation of a climate service based on subseasonal predictions and describe the bias adjustment and verification methodologies implemented. This work was developed in the H2020 S2S4E project, where industrial and research partners co-developed a fully-operational Decision Support Tool (DST) providing 18 months of real-time subseasonal and seasonal forecasts tailored to the specific needs of the renewable energy sector. The operational workflow can be adapted to serve forecast products to other sectors, as has been proved in the H2020 vitiGEOSS project, where the workflow was modified to provide downscaled subseasonal predictions to specific locations. We consider this a valuable contribution to future developments of similar service implementations and the producers of the climate data.

A predictable prospect of the South Asian summer monsoon

Prediction of the South Asian summer monsoon (SASM) has remained a challenge for both scientific research and operational climate prediction for decades. By identifying two dominant modes of the SASM, here we show that the unsatisfactory prediction may be due to the fact that the existing SASM indices are mostly related to the less predictable second mode. The first mode, in fact, is highly predictable. It is physically linked to the variation of the Indian monsoon trough coupled with large rainfall anomalies over core monsoon zone and the northern Bay of Bengal. An index is constructed as a physical proxy of this first mode, which can be well predicted one season in advance, with an overall skill of 0.698 for 1979–2020. This result suggests a predictable prospect of the SASM, and we recommend the new index for real-time monitoring and prediction of the SASM.

A new nudging scheme for the current operational climate prediction system of the National Marine Environmental Forecasting Center of China

A new nudging scheme for the current operational climate prediction system of the National Marine Environmental Forecasting Center of China

Seasonal predictability of primary East Asian summer circulation patterns by three operational climate prediction models

AbstractSeasonal predictability of the Primary East‐Asian Summer Circulation Patterns (PEASCPs), including the Western Pacific Subtropical High (WPSH), South Asian High (SAH), anomalous Philippine Sea AntiCyclone (PSAC) and East Asian Summer Monsoon (EASM), are investigated by using the hindcasts from the three operational climate prediction models, including BCC_CSM1.1(m), NCEP CFSv2 and ECMWF System 4. We show that prediction skills of the indices for representing these PEASCPs are sensitive to the initial calendar month of model prediction, and the ensemble mean of the three models provides relatively higher and more stable skills than forecasts from an individual model. In general, the indices of intensity and area have high prediction skills while the position indices have relatively low skills. Specifically, the skills of the WPSH intensity, area, SAH centre intensity, PSAC and EASM are higher, while the skills of the WPSH western boundary and SAH centre latitude are lower, and the skills of WPSH ridge line and SAH centre longitude are the lowest. Further analysis shows that the El Niño/Southern Oscillation (ENSO) has a large contribution to these prediction skills, and these patterns of atmospheric circulation anomalies in response to ENSO can be well captured by models, which is the major predictability source of the skills.

Forecast of summer precipitation in the Yangtze River Valley based on South China Sea springtime sea surface salinity

As a major moisture source, the South China Sea (SCS) has a significant impact on the summer precipitation over China. The ocean-to-land moisture transport generates sea surface salinity (SSS) anomalies that can be used to predict summer precipitation on land. This study illustrates a high correlation between springtime SSS in the central SCS and summer precipitation over the middle and lower Yangtze River Valley (the YRV region). The linkage between spring SSS in the central SCS and summer YRV precipitation is established by ocean-to-land moisture transport by atmospheric processes and land–atmosphere soil moisture feedback. In spring, oceanic moisture evaporated from the sea surface generates high SSS in the central SCS and directly feeds the precipitation over southern China and the YRV region. The resulting soil moisture anomalies last for about 3 months triggering land–atmosphere soil moisture feedback and modulating the tropospheric moisture content and circulation in the subsequent summer. Evaluation of the atmospheric moisture balance suggests both a dynamic contribution (stronger northward meridional winds) and a local thermodynamic contribution (higher tropospheric moisture content) enhance the summer moisture supply over the YRV, generating excessive summer precipitation. Thus, spring SSS in the SCS can be utilized as an indicator of subsequent summer precipitation over the YRV region, providing value for operational climate prediction and disaster early warning systems in China.

Operational climate prediction in the era of big data in China: Reviews and prospects

Big data has emerged as the next technological revolution in IT industry after cloud computing and the Internet of Things. With the development of climate observing systems, particularly satellite meteorological observation and high-resolution climate models, and the rapid growth in the volume of climate data, climate prediction is now entering the era of big data. The application of big data will provide new ideas and methods for the continuous development of climate prediction. The rapid integration, cloud storage, cloud computing, and full-sample analysis of massive climate data makes it possible to understand climate states and their evolution more objectively, thus predicting the future climate more accurately. This paper describes the application status of big data in operational climate prediction in China; it analyzes the key big data technologies, discusses the future development of climate prediction operations from the perspective of big data, speculates on the prospects for applying climatic big data in cloud computing and data assimilation, and puts forward the notion of big data-based super-ensemble climate prediction methods and computerbased deep learning climate prediction methods.

Sea surface temperature predictions using a multi-ocean analysis ensemble scheme

This study examined the global sea surface temperature (SST) predictions by a so-called multiple-ocean analysis ensemble (MAE) initialization method which was applied in the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2). Different from most operational climate prediction practices which are initialized by a specific ocean analysis system, the MAE method is based on multiple ocean analyses. In the paper, the MAE method was first justified by analyzing the ocean temperature variability in four ocean analyses which all are/were applied for operational climate predictions either at the European Centre for Medium-range Weather Forecasts or at NCEP. It was found that these systems exhibit substantial uncertainties in estimating the ocean states, especially at the deep layers. Further, a set of MAE hindcasts was conducted based on the four ocean analyses with CFSv2, starting from each April during 1982–2007. The MAE hindcasts were verified against a subset of hindcasts from the NCEP CFS Reanalysis and Reforecast (CFSRR) Project. Comparisons suggested that MAE shows better SST predictions than CFSRR over most regions where ocean dynamics plays a vital role in SST evolutions, such as the El Nino and Atlantic Nino regions. Furthermore, significant improvements were also found in summer precipitation predictions over the equatorial eastern Pacific and Atlantic oceans, for which the local SST prediction improvements should be responsible. The prediction improvements by MAE imply a problem for most current climate predictions which are based on a specific ocean analysis system. That is, their predictions would drift towards states biased by errors inherent in their ocean initialization system, and thus have large prediction errors. In contrast, MAE arguably has an advantage by sampling such structural uncertainties, and could efficiently cancel these errors out in their predictions.

Sea Surface Temperature Predictions in NCEP CFSv2 Using a Simple Ocean Initialization Scheme

Abstract In contrast to operational climate predictions based on sophisticated ocean data assimilation schemes at the National Centers for Environmental Predictions (NCEP), this study applied a simple ocean initialization scheme to the NCEP latest seasonal prediction model, the Climate Forecast System, version 2 (CFSv2). In the scheme, sea surface temperature (SST) was the only observed information applied to derive ocean initial states. The physical basis for the method is that, through air–sea coupling, SST is capable of reproducing some observed features of ocean evolutions by forcing the atmospheric winds. SST predictions based on the scheme are compared against hindcasts from the National (lately North American) Multimodel Ensemble (NMME) project. It was found that due to substantial biases in the tropical eastern Pacific in the ocean initial conditions produced by SST assimilation, ENSO SST predictions were not as good as those with sophisticated initialization schemes (e.g., hindcasts in the NMME project). However, in other basins, SST predictions based on a simple ocean initialization procedure were not worse (sometimes even better) than those with sophisticated initialization schemes. These comparisons indicate that it was helpful that subsurface ocean information be assimilated to improve the tropical Pacific SST predictions, while SST-based ocean assimilation was an effective way to enhance SST prediction capability in other ocean basins. By examining multimodel ensembles with the simple scheme-based hindcasts either included or excluded in NMME, it is also suggested that including the hindcast would generally benefit multimodel ensemble forecasts. In addition, possible ways to further improve ENSO SST predictions with the simple initialization scheme are also discussed.

Application of the Analogue-Based Correction of Errors Method in ENSO Prediction

AbstractIn this study, a method of analogue-based correction of errors (ACE) was introduced to improve El Nino-Southern Oscillation (ENSO) prediction produced by climate models. The ACE method is based on the hypothesis that the flow-dependent model prediction errors are to some degree similar under analogous historical climate states, and so the historical errors can be used to effectively reduce such flow-dependent errors. With this method, the unknown errors in current ENSO predictions can be empirically estimated by using the known prediction errors which are diagnosed by the same model based on historical analogue states. The authors first propose the basic idea for applying the ACE method to ENSO prediction and then establish an analogue-dynamical ENSO prediction system based on an operational climate prediction model. The authors present some experimental results which clearly show the possibility of correcting the flow-dependent errors in ENSO prediction, and thus the potential of applying the ACE...

Application of the Analogue-Based Correction of Errors Method in ENSO Prediction

In this study, a method of analogue-based correction of errors(ACE) was introduced to improve El Ni?o-Southern Oscillation(ENSO) prediction produced by climate models. The ACE method is based on the hypothesis that the flow-dependent model prediction errors are to some degree similar under analogous historical climate states, and so the historical errors can be used to effectively reduce such flow-dependent errors. With this method, the unknown errors in current ENSO predictions can be empirically estimated by using the known prediction errors which are diagnosed by the same model based on historical analogue states. The authors first propose the basic idea for applying the ACE method to ENSO prediction and then establish an analogue-dynamical ENSO prediction system based on an operational climate prediction model. The authors present some experimental results which clearly show the possibility of correcting the flow-dependent errors in ENSO prediction, and thus the potential of applying the ACE method to operational ENSO prediction based on climate models.

The Role of Air–Sea Coupling in Seasonal Prediction of Asia–Pacific Summer Monsoon Rainfall

Abstract This study examines the role of the air–sea coupled process in the seasonal predictability of Asia–Pacific summer monsoon rainfall by comparing seasonal predictions from two carefully designed model experiments: tier 1 (fully coupled model) and tier 2 (AGCM with prescribed SSTs). In these experiments, an identical AGCM is used in both tier 1 and tier 2 predictions; the daily mean SSTs from tier 1 coupled predictions are prescribed as a boundary condition in tier 2 predictions. Both predictions start in April from 1982 to 2009, with four ensemble members for each case. The model used is the Climate Forecast System, version 2 (CFSv2), the current operational climate prediction model for seasonal-to-interannual prediction at the National Centers for Environmental Prediction (NCEP). Comparisons indicate that tier 2 predictions produce not only higher rainfall biases but also unrealistically high rainfall variations in the tropical western North Pacific (TWNP) and some coastal regions as well. While the prediction skill in terms of anomaly correlations does not present a significant difference between the two types of predictions, the root-mean-square errors (RMSEs) are clearly larger over the above-mentioned regions in the tier 2 prediction. The reduced RMSE skills in the tier 2 predictions are due to the lack of a coupling process in AGCM-alone simulations, which, particularly, results in an unrealistic SST–rainfall relationship over the TWNP region. It is suggested that for a prediction of summer monsoon rainfall over the Asia–Pacific region, it is necessary to use a coupled atmosphere–ocean (tier 1) prediction system.

Ensemble ENSO hindcasts initialized from multiple ocean analyses

In this study, the impact of ocean initial conditions (OIC) on the prediction skill in the tropical Pacific Ocean is examined. Four sets of OIC are used to initialize the 12‐month hindcasts of the tropical climate from 1979 to 2007, using the Climate Forecast System, version 2 (CFSv2), the current operational climate prediction model at the National Centers for Environmental Predictions (NCEP). These OICs are chosen from four ocean analyses produced by the NCEP and the European Center for Medium Range Weather Forecasts (ECMWF). For each hindcast starting from a given OIC, four ensemble members are generated with different atmosphere and land initial states. The predictive skill in the tropical Pacific Ocean is assessed based on the ensemble mean hindcasts from each individual as well as multiple oceanic analyses. To reduce the climate drift from various oceanic analyses, an anomaly initialization strategy is used for all hindcasts. The results indicate that there exists a substantial spread in the sea surface temperature (SST) prediction skill with different ocean analyses. Specifically, the ENSO prediction skill in terms of the anomaly correlation of Niño‐3.4 index can differ by as much as 0.1–0.2 at lead times longer than 2 months. The ensemble mean of the predictions initialized from all four ocean analyses gives prediction skill equivalent to the best one derived from the individual ocean analysis. It is suggested that more accurate OIC can improve the ENSO prediction skill and an ensemble ocean initialization has the potential of enhancing the skill at the present stage.

How important is intraseasonal surface wind variability to real-time ENSO prediction?

[1] Variations with initial time in the prediction of the El Nino–Southern Oscillation (ENSO) are analyzed based on the NCEP operational forecasts. It is found that the forecasted ENSO can be greatly affected by intraseasonal surface wind variability in the western Pacific (WPac), especially that associated with the Madden‐Julian Oscillation (MJO). In the model forecasts, Kelvin waves forced by intraseasonal winds in the WPac propagate eastward and reach the eastern Pacific in about 60 days, where the induced surface temperature (SST) anomalies dissipate and expand westward. Differences in the forecasted Nino3.4 SSTs can be 0.5 K or larger depending on the phase and amplitude of the MJO. Variations in WPac surface winds are also found to have impacts on the spread of the forecasted Nino3.4 SSTs. Relevance of these results to the ensemble strategies currently used in the operational climate prediction centers is discussed. Citation: Wang, W., M. Chen, A. Kumar, and Y. Xue (2011), How important is intraseasonal surface wind variability to real‐time ENSO prediction?, Geophys. Res. Lett., 38, L13705, doi:10.1029/2011GL047684.

Low cloud errors over the southeastern Atlantic in the NCEP CFS and their association with lower‐tropospheric stability and air‐sea interaction

The low cloud errors in the tropical Atlantic and their connection with lower‐tropospheric stability and air‐sea interaction in a simulation of the Climate Forecast System (CFS) are investigated. The CFS is the coupled ocean‐atmosphere circulation model for operational climate prediction at the National Centers for Environmental Prediction. Compared with observations, the CFS produces too few low clouds over the southeastern Atlantic and too many high clouds over the whole tropical Atlantic. The center of the low clouds is also shifted westward in the model away from the cold tongue region to the central ocean. The underestimation of low clouds in the southeastern Atlantic may be one of the potential sources of the warm biases of sea surface temperature (SST) in this region. The low clouds are linked to a stable or inversion layer between 850 hPa and 925 hPa in observations, but are associated with a stable layer between 700 hPa and 850 hPa in the CFS, which may suggest that low clouds are generated in a higher layer in the CFS than in the real world. On average, the lower troposphere is less stable in the CFS than in the observations over the tropical Atlantic region, which is more favorable for the development of deep convection. Moreover, the low cloud variability in the South Atlantic is mainly associated with SST and wind stress anomalies in the subtropical South Atlantic in the CFS and in the southeastern Atlantic and along the African coast in the observations.

Leading patterns of the tropical Atlantic variability in a coupled general circulation model

This paper examines the mean annual cycle, interannual variability, and leading patterns of the tropical Atlantic Ocean simulated in a long-term integration of the climate forecast system (CFS), a state-of-the-art coupled general circulation model presently used for operational climate prediction at the National Centers for Environmental Prediction. By comparing the CFS simulation with corresponding observation-based analyses or reanalyses, it is shown that the CFS captures the seasonal mean climate, including the zonal gradients of sea surface temperature (SST) in the equatorial Atlantic Ocean, even though the CFS produces warm mean biases and underestimates the variability over the southeastern ocean. The seasonal transition from warm to cold phase along the equator is delayed 1 month in the CFS compared with the observations. This delay might be related to the failure of the model to simulate the cross-equatorial meridional wind associated with the African monsoon. The CFS also realistically simulates both the spatial structure and spectral distributions of the three major leading patterns of the SST anomalies in the tropical Atlantic Ocean: the south tropical Atlantic pattern (STA), the North tropical Atlantic pattern (NTA), and the southern subtropical Atlantic pattern (SSA). The CFS simulates the seasonal dependence of these patterns and partially reproduces their association with the El Nino-Southern Oscillation. The dynamical and thermodynamical processes associated with these patterns in the simulation and the observations are similar. The air-sea interaction processes associated with the STA pattern are well simulated in the CFS. The primary feature of the anomalous circulation in the Northern Hemisphere (NH) associated with the NTA pattern resembles that in the Southern Hemisphere (SH) linked with the SSA pattern, implying a similarity of the mechanisms in the evolution of these patterns and their connection with the tropical and extratropical anomalies in their respective hemispheres. The anomalies associated with both the SSA and NTA patterns are dominated by atmospheric fluctuations of equivalent-barotropic structure in the extratropics including zonally symmetric and asymmetric components. The zonally symmetric variability is associated with the annular modes, the Arctic Oscillation in the NH and the Antarctic Oscillation in the SH. The zonally asymmetric part of the anomalies in the Atlantic is teleconnected with the anomalies over the tropical Pacific. The misplaced teleconnection center over the southern subtropical ocean may be one of the reasons for the deformation of the SSA pattern in the CFS.

A Regression-based Assessment of the Predictability of New Zealand Climate Anomalies

Estimates of the predictability of New Zealand monthly and seasonal temperature and rainfall anomalies are calculated using a cross-validated linear regression procedure. Predictors are indices of the large scale circulation, sea-surface temperatures, the Southern Oscillation Index and persistence. Statistical significance is estimated through a series of Monte Carlo trials. No significant forecast relationships are found for rainfall anomalies at either the monthly or seasonal time scale. Temperature forecasts are however considered to exhibit significant skill, with variance reductions of the order of 10–20% in independent trials. Temperature anomalies are most skilfully predicted over the North Island, and skill is greatest in Spring and Summer in most areas. At the monthly time scale, predictors local to the New Zealand region account for most of the forecast skill, while at the seasonal time scale, skill depends strongly upon “remote” predictors defined over regions of the southern hemisphere distant from New Zealand. Indices of meridional flow over the Tasman Sea/New Zealand region are found to be useful predictors, especially for monthly forecasts, perhaps as a proxy for atmospherically-forced sea surface temperature anomalies. Sea surface temperature anomalies to the west of New Zealand and in the tropical Indian Ocean are also useful, especially for seasonal predictions. Forecast skill is more reliably estimated at the monthly time scale than at the seasonal time scale, as a result of the larger sample size of monthly mean data. While long-term mean levels of skill may be estimated reliably over the whole data set, statistically significant decadal-scale variations are found in the predictability of temperature anomalies. Therefore, even if long-term forecast skill levels are reliably estimated, it may be impossible to predict the short-term skill of operational seasonal climate forecasts. Implications for operational climate predictions in mid-latitudes are discussed.