Seasonal Precipitation Prediction Research Articles

Evaluation of Wintertime Precipitation Estimates and Forecasts in the Mountains of Colorado

Abstract Wintertime precipitation poses many observational and forecasting challenges, especially in the complex topography of the western United States where radar beam blockage and difficulty siting in situ observations yields more sparse observations than in the eastern United States. Uncertainty in western U.S. winter precipitation is known to be high, so much so that some studies have found model simulated precipitation to produce similar or better large-scale estimates of annual precipitation than gridded observational products during climatologically anomalous years. This study evaluates high-resolution gridded precipitation estimates from Multi-Radar Multi-Sensor (MRMS) and Stage IV as well as forecasts from NOAA’s High-Resolution Rapid Refresh (HRRR) model in the Colorado Rocky Mountains. Gridded precipitation estimates and forecasts are compared with in situ SNOTEL measurements for two seasons of wintertime precipitation. The influence of forecast length, lead time, and model elevation on seasonal precipitation predictions from the HRRR are investigated. Additional comparisons are made with the relatively dense network of observations deployed in Colorado’s East River Watershed during the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) campaign. Gridded products and forecasts are found to underestimate cold-season precipitation by 25%–65% relative to in situ and aircraft measurements, with longer forecast periods and lead times (6–24 h) having smaller biases (25%–30%) than shorter forecast periods and lead times (55%–65%). The assessment of multiple years of observations indicates that these biases are related more to the data and methods used to create the gridded products and forecasts than to precipitation characteristics. Significance Statement In the mountainous western United States, it is very challenging to both observe and forecast wintertime precipitation, yet snowfall plays an important role in providing the region’s annual water supply. This study aims to increase our understanding of the biases in observations and forecasts of snowfall in the Colorado Rocky Mountains, which can in turn impact forecasts of water availability for the ensuing warm season. In this study we find high-resolution gridded precipitation estimates and forecasts to underestimate cold-season precipitation when compared with in situ observing stations, with longer-range forecasts (e.g., daily) being the least biased. These findings were consistent over two years of study and have broad implications for the hydrologic modeling and water management communities.

Investigating the Performance of CMIP6 Seasonal Precipitation Predictions and a Grid Based Model Heterogeneity Oriented Deep Learning Bias Correction Framework

AbstractClimate change is expected to alter the magnitude and spatiotemporal patterns of hydro‐climate variables such as precipitation, which has significant impacts on the ecosystem, human societies and water security. Global Climate Models are the major tools to simulate historical as well as future precipitation. However, due to imperfect model structures, parameters and boundary conditions, direct model outputs are subject to large uncertainty, which needs serious evaluation and bias correction before usage. In this study, seasonal precipitation predictions from 30 Coupled Model Inter‐comparison Project Phase 6 (CMIP6) models and Climate Research Unit observations are used to evaluate historical precipitation climatology in global continents during 1901–2014. A grid based model heterogeneity oriented Convolutional Neural Network (CNN) is proposed to correct the ensemble mean precipitation bias ratio. Besides, regression based Linear Scaling (LS), distribution based Quantile Mapping (QM) and spatial correlation CNN bias correction approaches are employed for comparison. Results of model performance evaluation indicate that generally precipitation prediction is more reliable in JJA than DJF on the global scale. Most models tend to have larger bias ratio for extreme precipitation. In addition, current CMIP6 models still have certain issues in accurate simulation of precipitation in mountainous regions and the regions affected by complex climate systems. Moreover, the proposed grid based model heterogeneity oriented CNN has better performance in ensemble mean bias correction than LS, QM, and spatial correlation CNN, which could consider the relative model performance and capture the features similar to actual climate dynamics.

Remote effects of Tibetan Plateau spring land temperature on global subseasonal to seasonal precipitation prediction and comparison with effects of sea surface temperature: the GEWEX/LS4P Phase I experiment

The prediction skill for precipitation anomalies in late spring and summer months—a significant component of extreme climate events—has remained stubbornly low for years. This paper presents a new idea that utilizes information on boreal spring land surface temperature/subsurface temperature (LST/SUBT) anomalies over the Tibetan Plateau (TP) to improve prediction of subsequent summer droughts/floods over several regions over the world, East Asia and North America in particular. The work was performed in the framework of the GEWEX/LS4P Phase I (LS4P-I) experiment, which focused on whether the TP LST/SUBT provides an additional source for subseasonal-to-seasonal (S2S) predictability. The summer 2003, when there were severe drought/flood over the southern/northern part of the Yangtze River basin, respectively, has been selected as the focus case. With the newly developed LST/SUBT initialization method, the observed surface temperature anomaly over the TP has been partially produced by the LS4P-I model ensemble mean, and 8 hotspot regions in the world were identified where June precipitation is significantly associated with anomalies of May TP land temperature. Consideration of the TP LST/SUBT effect has produced about 25–50% of observed precipitation anomalies in most hotspot regions. The multiple models have shown more consistency in the hotspot regions along the Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train. The mechanisms for the LST/SUBT effect on the 2003 drought over the southern part of the Yangtze River Basin are discussed. For comparison, the global SST effect has also been tested and 6 regions with significant SST effects were identified in the 2003 case, explaining about 25–50% of precipitation anomalies over most of these regions. This study suggests that the TP LST/SUBT effect is a first-order source of S2S precipitation predictability, and hence it is comparable to that of the SST effect. With the completion of the LS4P-I, the LS4P-II has been launched and the LS4P-II protocol is briefly presented.

A Statistical Prediction Model for Summer Precipitation in China Based on TSD Method and EOF Modes’ Time Coefficients

It is a challenge to improve the skill of seasonal precipitation prediction, because there are many factors affecting summer precipitation in China, which are found on different time scales and have complex interactions with each other. For these reasons, we establish a prediction model with the time-scale decomposition (TSD) method to investigate whether the TSD has an improving effect on the prediction skill of summer precipitation in China. Using this statistical model, the predictors and predictands will be separated into interannual and interdecadal time scales, after which Empirical Orthogonal Function (EOF) decomposition is performed on these two components, and their time coefficients are predicted, respectively. The hindcast cross-validation results show that the model without TSD has prediction skills only in some regions of East China and South China. Compared with the model without TSD, surprisingly, the model with TSD can significantly improve the prediction performance in more regions in China, such as Xinjiang Province and Northeast China. The anomaly correlation coefficients (ACC) between hindcast precipitation with TSD and observation are higher in most years than that without TSD. The results of the independent sample test show that the forecast model with TSD has a stable and gratifying prediction skill, and the averaged ACC is increased by more than 0.1.

Subselection of seasonal ensemble precipitation predictions for East Africa

AbstractThe Greater Horn of Africa (GHA) is highly vulnerable to climate and weather hazards such as drought, heat waves, and floods. There is a need for accurate seasonal forecasts to prepare for risks (such as crop failure and reduced grazing opportunities) and take advantage of favorable conditions (rains arrive on time and where they are needed) when they arise. As such, information at finer spatial scales than current state‐of‐the‐art global prediction models can provide is needed. Dynamical downscaling is one method employed to obtain information at finer scales. However, providers of seasonal forecasts over the GHA are hampered by limited computational resources and time constraints that restrict the number of global model ensemble members that can be downscaled. Some ensemble subselection criteria must be employed. Currently, providers take an uninformed (or random) approach. Specifically, forecasters simply take the first ensemble member of the global model seasonal forecast ensemble. However, recent work, focused on decadal prediction, has shown that subselecting global model ensemble members in an informed way, that is, according to their ability to reproduce key features of the climate system, results in improved predictions. This emerges from the fact that the climate system is likely more predictable than our models would have us believe. Seeing an opportunity for improvement, we apply the same thinking to the seasonal context and assess several procedures for subselecting ensemble members from seasonal predictions with exchangeable members. Such informed subselections have the potential to take advantage of information in an ensemble of global simulations that might be missed by random selection. Three subselection methods are investigated, with a focus on seasonal predictions for rainfall over GHA. We demonstrate that informed subselection leads to systematically higher skill than random subselection. We find that (1) for small subsample sizes, such as would be chosen for dynamical downscaling and/or downstream impact modeling, informed subselection nearly always outperforms random subselection, (2) subselecting based on well‐known teleconnections benefits those seasons in which such pathways are active, such as OND and JJAS, and (3) ‐means subselection outperforms random selection for small ensemble sizes throughout all seasons, including the notoriously difficult to predict MAM season. These techniques require only input that is available at the time of the forecast release and are easy to apply operationally.

Climate-driven changes in the predictability of seasonal precipitation

Climate-driven changes in precipitation amounts and their seasonal variability are expected in many continental-scale regions during the remainder of the 21st century. However, much less is known about future changes in the predictability of seasonal precipitation, an important earth system property relevant for climate adaptation. Here, on the basis of CMIP6 models that capture the present-day teleconnections between seasonal precipitation and previous-season sea surface temperature (SST), we show that climate change is expected to alter the SST-precipitation relationships and thus our ability to predict seasonal precipitation by 2100. Specifically, in the tropics, seasonal precipitation predictability from SSTs is projected to increase throughout the year, except the northern Amazonia during boreal winter. Concurrently, in the extra-tropics predictability is likely to increase in central Asia during boreal spring and winter. The altered predictability, together with enhanced interannual variability of seasonal precipitation, poses new opportunities and challenges for regional water management.

Potential Predictability of Seasonal Global Precipitation Associated with ENSO and MJO

A covariance decomposition method is applied to a monthly global precipitation dataset to decompose the interannual variability in the seasonal mean time series into an unpredictable component related to “weather noise” and to a potentially predictable component related to slowly varying boundary forcing and low-frequency internal dynamics. The “potential predictability” is then defined as the fraction of the total interannual variance accounted for by the latter component. In tropical oceans (30° E–0° W, 30° S–30° N), the consensus is that the El Nino-Southern Oscillation (ENSO, with 4–8 year cycles) is a dominant driver of the potentially predictable component, while the Madden-Julian Oscillation (MJO, with 30–90 days cycles) is a dominant driver of the unpredictable component. In this study, the consensus is verified by using the Nino3-4 SST index and a popular MJO index. It is confirmed that Nino3-4 SST does indeed explain a significant part of the potential predictable component, but only limited variability of the unpredictable component is explained by the MJO index. This raises the question of whether the MJO is dominant in the variability of the unpredictable component of the precipitation, or the current MJO indexes do not represent MJO variability well.

Improving Seasonal Prediction of Summer Precipitation in the Middle–Lower Reaches of the Yangtze River Using a TU-Net Deep Learning Approach

Abstract The two-step U-Net model (TU-Net) contains a western North Pacific subtropical high (WNPSH) prediction model and a precipitation prediction model fed by the WNPSH predictions, oceanic heat content, and surface temperature. The data-driven forecast model provides improved 4-month lead predictions of the WNPSH and precipitation in the middle and lower reaches of the Yangtze River (MLYR), which has important implications for water resources management and precipitation-related disaster prevention in China. When compared with five state-of-the-art dynamical climate models including the Climate Forecast System of Nanjing University of Information Science and Technology (NUIST-CFS1.0) and four models participating in the North American Multi-Model Ensemble (NMME) project, the TU-Net produces comparable skills in forecasting 4-month lead geopotential height and winds at the 500- and 850-hPa levels. For the 4-month lead prediction of precipitation over the MLYR region, the TU-Net has the best correlation scores and mean latitude-weighted RMSE in each summer month and in boreal summer [June–August (JJA)], and pattern correlation coefficient scores are slightly lower than the dynamical models only in June and JJA. In addition, the results show that the constructed TU-Net is also superior to most of the dynamical models in predicting 2-m air temperature in the MLYR region at a 4-month lead. Thus, the deep learning-based TU-Net model can provide a rapid and inexpensive way to improve the seasonal prediction of summer precipitation and 2-m air temperature over the MLYR region. Significance Statement The purpose of this study is to examine the seasonal predictive skill of the western North Pacific subtropical high anomalies and summer rainfall anomalies over the middle and lower reaches of the Yangtze River region by means of deep learning methods. Our deep learning model provides a rapid and inexpensive way to improve the seasonal prediction of summer precipitation as well as 2-m air temperature. The work has important implications for water resources management and precipitation-related disaster prevention in China and can be extended in the future to predict other climate variables as well.

A Physics‐Based Empirical Model for the Seasonal Prediction of the Central China July Precipitation

AbstractJuly is the rainy peak month of central China, with a large interannual variation of local precipitation often causing serious droughts and floods. The seasonal prediction of the central China July precipitation (CCJP) is an important but still challenging task. Here, we suggest several robust seasonal predictors for the CCJP, including the preceding winter intensity of El Niño‐South Oscillation (ENSO), the winter‐to‐spring decaying rate of ENSO signals in the central Pacific, as well as the spring tropical and subpolar North Atlantic sea surface temperature anomalies. A physics‐based empirical model is then developed to predict the CCJP by using the principal component regression of the aforementioned seasonal predictors. In our statistical model, the seasonal prediction skill of the CCJP is high, with the cross‐validated reforecast skill at 0.81 during 1993–2021. This suggests a skillful seasonal prediction of the CCJP, with potentially enormous benefits for the local society and economy.

Deep learning-based subseasonal to seasonal precipitation prediction in southwest China: Algorithm comparison and sensitivity to input features

Deep learning-based subseasonal to seasonal precipitation prediction in southwest China: Algorithm comparison and sensitivity to input features

How to choose credible ensemble members for the sub-seasonal to seasonal prediction of precipitation?

The sub-seasonal to seasonal (S2S) prediction of precipitation is not only a hot topic but also a challenge. The traditional ensemble mean and ensemble probabilistic forecast methods cannot avoid the uncertainty of the initial value in the S2S prediction. Is there a more suitable ensemble postprocessing method for the S2S prediction? In this study, the hindcast data during the 1999–2010 summers from nine operational models in the international S2S prediction project has been evaluated. Based on the quantitative objective precipitation evaluation methods, such as the Equitable Threat Score and frequency bias methods, the climatological spatio-temporal distribution of the optimal probabilistic threshold on the S2S scale is proven to exist, and it can be used as the standard to judge how many ensemble members are credible. Then, different ensemble forecast strategies are adopted in different regions to construct a Deterministic Ensemble Forecast using an Optimal Probabilistic Threshold (DEFOPT) method for precipitation prediction. The hindcast data of eight S2S models outside the period 1999–2010 are used to verify the applicability of the DEFOPT method by using the historical optimal probabilistic threshold during 1999–2010. The results show that the DEFOPT outperforms the deterministic forecast from one initial value, the ensemble mean, and the deterministic ensemble forecast using a probabilistic threshold for the occurrence days of rainfall at the 1 mm and 5 mm thresholds (≥ 1 mm and ≥ 5 mm) over China during each pentad in most S2S models.

Why Seasonal Prediction of California Winter Precipitation Is Challenging

Abstract Despite an urgent demand for reliable seasonal prediction of precipitation in California (CA) due to the recent recurrent and severe drought conditions, our predictive skill for CA winter precipitation remains limited. October hindcasts by the coupled dynamical models typically show a correlation skill of about 0.3 for CA winter (November–March) precipitation. In this study, an attempt is made to understand the underlying processes that limit seasonal prediction skill for CA winter precipitation. It is found that only about 25% of interannual variability of CA winter precipitation can be attributed to influences by El Niño–Southern Oscillation (ENSO). Instead, the year-to-year CA winter precipitation variability is primarily due to circulation anomalies independent from ENSO, featuring a circulation center over the west coast United States as a portion of a short Rossby wave train pattern over the North Pacific. Analyses suggest that dynamical models show nearly no skill in predicting these ENSO-independent circulation anomalies, thus leading to limited predictive skill for CA winter precipitation. Low predictability of these ENSO-independent circulation anomalies is further demonstrated by a large ensemble of atmospheric-only climate model simulations. While low predictability of the ENSO-independent circulation anomalies could be due to chaotic internal atmospheric processes over the mid- to high latitudes, possible underexploited predictability sources for CA precipitation in models are also discussed. This study pinpoints an urgent need for improved understanding of the formation mechanisms of ENSO-independent circulation anomalies over the U.S. West Coast for a breakthrough in seasonal prediction of CA winter precipitation.

North China Spring Rainfall and Its Linkage with SST and Atmospheric Circulation

Abstract Spring rainfall is important for agriculture and economics in North China (NC). Thus, there is an imperative need for accurate seasonal prediction of the spring precipitation. This study implements a novel rainfall framework to improve understanding of NC spring rainfall. The framework is built based on a three-cluster normal mixture model. Distribution parameters are sampled using Bayesian inference and a Markov chain Monte Carlo algorithm. The probability behaviors of light, moderate, and heavy rainfall events can be reflected by the three rainfall clusters, respectively. Analysis of 61-yr data indicates that moderate rainfall makes the largest contribution (67%) to the total rainfall amount. The moderate rainfall intensity is mainly influenced by the sea surface temperature anomaly (SSTA) in the previous season over the equatorial eastern Pacific, and rainfall frequency is influenced by geopotential height anomaly in the mid- to high latitudes in spring. It is also found that more extreme precipitation events can be observed in the spring following an eastern Pacific El Niño event in the previous autumn and winter. Based on these relationships, we develop a multiple linear regression model. Hindcasts for spring precipitation using the model indicates that its anomaly correlation is 0.48, significant at the 99% confidence level. The result suggests that the newly developed model can well predict spring rainfall amount in NC.

Improvement of summer precipitation simulation with indirect assimilation of spring soil moisture over the Tibetan Plateau

AbstractSoil moisture can be an important preceding signal in seasonal precipitation prediction because of its persistence and influence on the energy and water balance between land and atmosphere. The thawing process of frozen ground on the Tibetan Plateau (TP) during spring makes it difficult to accurately simulate soil variables (e.g., soil moisture and temperature) due to defective parameterizations in numerical models during spring. In this study, we investigate the effectiveness of soil moisture correction using an indirect nudging scheme to simulate the coupling between spring soil moisture and the subsequent precipitation with an advanced research version of the weather research and forecasting (WRF) model. The results without assimilation show that extreme cold and dry land surface states during spring cause a large bias in the spatial pattern of summer precipitation over almost the entire TP. However, the experiments with indirect assimilation in spring show that the spatial pattern of summer precipitation improved significantly. This comparison emphasizes the importance of correcting the spring soil moisture during the freeze–thaw period to significantly adjust the heat and water vapour exchange between the land and atmosphere. Additionally, changes in the circulation of the subtropical–tropical jet, vertical ascent, and region‐related moisture recycling can support an active convection environment over the eastern TP, thereby increasing summer precipitation. Furthermore, the pattern shift in water vapour convergence in summer highlights the regional horizontal transportation of water vapour. The differences between experiments with and without deep‐soil temperature nudging illustrate the significance of soil temperature in soil moisture simulation in the freeze–thaw process. More so, the deep‐soil temperature nudging scheme requires in‐depth studies to correct its variation and quantify its degree of impact on soil moisture.

Evaluation of ECMWF-SEAS5 Seasonal Temperature and Precipitation Predictions over South America

Nowadays, a challenge in Climate Science is the seasonal forecast and knowledge of the model’s performance in different regions. The challenge in South America reflects its huge territory; some models present a good performance, and others do not. Nevertheless, reliable seasonal climate forecasts can benefit numerous decision-making processes related to agriculture, energy generation, and extreme events mitigation. Thus, given the few works assessing the ECMWF-SEAS5 performance in South America, this study investigated the quality of its seasonal temperature and precipitation predictions over the continent. For this purpose, predictions from all members of the hindcasts (1993–2016) and forecasts (2017–2021) ensemble were used, considering the four yearly seasons. The analyses included seasonal mean fields, bias correction, anomaly correlations, statistical indicators, and seasonality index. The best system’s performance occurred in regions strongly influenced by teleconnection effects, such as northern South America and northeastern Brazil, in which ECMWF-SEAS5 even reproduced the extreme precipitation anomalies that happened in recent decades. Moreover, the system indicated a moderate capability of seasonal predictions in medium and low predictability regions. In summary, the results show that ECMWF-SEAS5 climate forecasts are potentially helpful and should be considered to plan various strategic activities better.

Assessing the contribution of dynamical downscaling to austral autumn Northeast Brazil seasonal precipitation prediction performance

This study aims to assess the contribution to prediction performance of dynamically downscaling seasonal precipitation for the Brazilian Northeast region (NEB) produced in February with a global climate model for the austral autumn (March-April-May). This season includes the peak precipitation period of a large portion of this region. To achieve this aim, 32 years of hindcasts for the period 1979–2010 were produced with a regional model known as the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS) run at 30 km spatial resolution, which was unidirectional nested to a global model run at approximately 200 km spatial resolution. The used global model was the Atmospheric General Circulation Models of the Brazilian Center for Weather Forecast and Climate Studies (CPTEC/AGCM), which provided initial and lateral boundary conditions for BRAMS. Hindcast ensembles of 10 members were produced with both the global and the regional models, simulating an operational prediction environment using persisted sea surface temperature (SST) anomalies observed in the previous January of each of the 32 hindcast years as oceanic boundary conditions. A comparative deterministic and probabilistic performance assessment of the two models was conducted. The deterministic assessment included the investigation of the mean error (bias) of the produced seasonal precipitation hindcasts, the degree of linear association between the observed and predicted precipitation anomalies measuring phase error through the correlation of the ensemble means with the corresponding observations, and amplitude error investigated through the ratio between the hindcast ensemble mean and observed variances. The probabilistic assessment was performed investigation the reliability, resolution, and discrimination characteristics of tercile probability predictions for the above normal and below normal categories, and also for probabilistic predictions for the occurrence of the symmetric event positive (or negative) precipitation anomaly. The two investigated modeling approaches showed similar performance, with an indication for dynamically downscaled predictions to have reduced amplitude error and slightly improved reliability and resolution compared to the predictions produced with the global model.

Extended seasonal prediction of spring precipitation over the Upper Colorado River Basin

This study provides extended seasonal predictions for the Upper Colorado River Basin (UCRB) precipitation in boreal spring using an artificial neural network (ANN) model and a stepwise linear regression model, respectively. Sea surface temperature (SST) predictors are developed taking advantage of the correlation between the precipitation and SST over three ocean basins. The extratropical North Pacific has a higher correlation with the UCRB spring precipitation than the tropical Pacific and North Atlantic. For the ANN model, the Pearson correlation coefficient between the observed and predicted precipitation exceeds 0.45 (p-value < 0.01) for a lead time of 12 months. The mean absolute percentage error (MAPE) is below 20% and the Heidke skill score (HSS) is above 50%. Such long-lead prediction skill is probably due to the UCRB soil moisture bridging the SST and precipitation. The stepwise linear regression model shows similar prediction skills to those of ANN. Both models show prediction skills superior to those of an autoregression model (correlation < 0.10) that represents the baseline prediction skill and those of three of the North American Multi-Model Ensemble (NMME) forecast models. The three NMME models exhibit different skills in predicting the precipitation, with the best skills of the correlation ~ 0.40, MAPE < 25%, and HSS > 40% for lead times less than 8 months. This study highlights the advantage of oceanic climate signals in extended seasonal predictions for the UCRB spring precipitation and supports the improvement of the UCRB streamflow prediction and related water resource decisions.

Seasonal prediction of summer extreme precipitation over the Yangtze River based on random forest

The 2020 summer extreme precipitation event over the Mid-Lower Reaches of Yangtze River in China caused widespread socioeconomic impacts, with a death toll of hundreds and direct economic loss of half billion CNY. Seasonal prediction of summer extreme precipitation event over this region, however, has long been a challenge, due to the underlying interactions among various atmospheric and oceanic factors. Based on the random forest (RF), a classical machine learning method, a series of predictive models are trained and tested on the samples during 1951–2019, with 14 preceding atmospheric and oceanic indices as potential predictors. It is found that the model based on 3 indices has optimal performance in terms of the distinguishing capacity between extreme and non-extreme events. For the 2020 summer extreme event, the model predicts a large probability far beyond the climatological mean level, indicating a very likely extreme event. Interpretation of the decision trees in the RF model reveals 3 main decision paths leading to an extreme precipitation event over this region. The first one is driven by a strong eastern tropical Pacific (EP) El Niño which starts to decay in spring but does not totally disappear in summer. The second one results from the combined effect of an EP La Niña and normal sea surface temperature over the North Indian Ocean (NIO). The last one is also associated with a decaying EP El Niño, but the EP El Niño here is much weaker than that in the first path. Both the Pacific-El Niño-independent warming of NIO and cooling in the eastern tropical western Pacific in spring play important roles. The RF integrates different nonlinear physical mechanisms in a model and discovers weak signals which are easily omitted by traditional linear methods. The trained model can self-improve with increasing samples and serve as a reference for operational prediction of extreme precipitation events in the region.

Regionalization of climate teleconnections across Central Asian mountains improves the predictability of seasonal precipitation

Mountains play a critical role in water cycles in semiarid regions by providing for the majority of the total runoff. However, hydroclimatic conditions in mountainous regions vary considerably in space and time, with high interannual fluctuations driven by large-scale climate oscillations. Here, we investigated teleconnections between global climate oscillations and the peak precipitation season from February to June in the Tian-Shan and Pamir Mountains of Central Asia. Using hierarchical climate regionalization, we identified seven subregions with distinct precipitation patterns, and assessed correlations with selected climate oscillations at different time lags. We then simulated the seasonal precipitation in each subregion from 1979 to 2020 using the most prevalent teleconnections as predictors with support vector regression (SVR). Our findings indicate that the El Niño–Southern Oscillation, the Pacific Decadal Oscillation, and the Eastern Atlantic/West Russia pattern are among the major determinants of the seasonal precipitation. The dominant lead-lag times of these oscillations make them reliable predictors ahead of the season. We detected notable teleconnections with the North Atlantic Oscillation and Scandinavian Pattern, with their strongest associations emerging after onset of the season. While the SVR-based models exhibit robust prediction skills, they tend to underestimate precipitation in extremely wet seasons. Overall, our study highlights the value of appropriate spatial and temporal aggregations for exploring the impacts of climate teleconnections on precipitation in complex terrains.

Deep Learning for Seasonal Precipitation Prediction over China

Deep Learning for Seasonal Precipitation Prediction over China