North American Multimodel Ensemble Research Articles

Skillful seasonal prediction of the 2022–23 mega soil drought over the Yangtze River basin by combining dynamical climate prediction and copula analysis

An unprecedented soil moisture drought broke out over the Yangtze River basin (YRB) in the summer of 2022 and lasted until the spring of 2023, caused great economic losses and serious environmental issues. With the rapid onset and long-lasting duration, the mega soil drought challenges the current seasonal prediction capacity. Whether the state-of-the-art climate models provide skillful predictions of the onset, persistence and recovery of the 2022–23 mega soil drought needs to be assessed. Identified by the drought area percentage, here we show that the mega soil drought over the YRB started from July, 2022, reached the peak in August, and diminished in April, 2023. Combined with real-time predictions of monthly precipitation released by three climate models participating in the North American multi-model ensemble (NMME) project, we predict the monthly evolution of the 2022–23 soil drought through a joint distribution between precipitation and soil moisture established by the copula method. The results indicate that the NMME/copula prediction well reproduced the spatiotemporal evolution of the mega soil drought at 1 month lead. Using the climatological prediction that relies on the information of initial soil moisture conditions as the reference forecast, the Brier skill score (BSS) values for NMME multi-model ensemble are 0.26, 0.23 and 0.2 for the forecast lead times increased from 1 to 3 months during the entire soil drought period. Specifically, the BSS is 0.14 at 2 months lead during drought onset stage, and 0.26 at 3 months lead during persistence stage, while it is close to zero at all leads during the recovery stage. Our study implies that climate models have great potential in probabilistic seasonal prediction of the onset and persistency of mega soil drought through combining with the copula method.

Climate forecast skill and teleconnections on seasonal time scales over Central Africa based on the North American Multi-Model Ensemble (NMME)

Climate forecast skill and teleconnections on seasonal time scales over Central Africa based on the North American Multi-Model Ensemble (NMME)

On the Role of Indian Ocean SST in Influencing the Differences in Atmospheric Variability Between 2020–2021 and 2021–2022 La Niña Boreal Winters

AbstractThe difference in observed atmospheric anomalies over the Northern Hemisphere winter between 2021–22 and 2020–21 La Niña years indicated a tripole pattern consisting of a Japan cyclone, a Bering Sea anticyclone, and a cyclone over the North American continent. This feature, however, was not replicated in the North American Multi‐Model Ensemble (NMME) forecasts. A set of model sensitivity experiments was performed to better understand the cause of this discrepancy. The results revealed the possible role of the influence of sea surface temperature (SST) anomalies, particularly over the Indian Ocean, on the observed circulation differences that was further modulated by internal atmospheric variability. The failure in predicting circulation changes in NMME was next attributed to the errors in SST predictions over the Indian Ocean and highlights the need for improvements in SST forecasts over this region.

An attempt using equatorial waves to predict tropical sea surface temperature anomalies associated with the Atlantic zonal mode

IntroductionThe forecast for anomalous sea surface temperature (SST) events associated with Atlantic zonal mode, also known as Atlantic Niño/Niña, is full of challenge for both statistical and dynamical prediction models. MethodsThis study combines SST, wind and equatorial wave signal to construct a linear model, aiming to evaluate the potential of equatorial waves in extending the lead time of a skilful prediction for Atlantic Niño/Niña events. Wave-induced geopotential simulated by linear ocean models and potential energy flux calculated using a group-velocity-based wave energy flux scheme are involved to capture the signal of equatorial waves in the model establishment. ResultsThe constructed linear prediction model has demonstrated comparable prediction skill for the SST anomaly to the dynamical models of the North American Multimodel Ensemble (NMME) during the test period (1992-2016). Compared with the statistical forecast using SST persistence, the model notably improves the six-month-lead prediction (Anomaly correlation coefficient increases from 0.07 to 0.28), which owes to the conservation of wave energy in the narrow Atlantic basin that the Rossby waves reflected in the eastern boundary will transfer the energy back to the central equatorial basin and again affect the SST there. ConclusionThis study offers a streamlined model and a straightforward demonstration of leveraging wave energy transfer route for the prediction of Atlantic Niño/Niñas.

A Relative Sea Surface Temperature Index for Classifying ENSO Events in a Changing Climate

Abstract El Niño–Southern Oscillation (ENSO) is often characterized through the use of sea surface temperature (SST) departures from their climatological values, as in the Niño-3.4 index. However, this approach is problematic in a changing climate when the climatology itself is varying. To address this issue, van Oldenborgh et al. proposed a relative Niño-3.4 SST index, which subtracts the tropical mean SST anomaly from the Niño-3.4 index and multiplies by a scaling factor. We extend their work by providing a simplified calculation procedure for the scaling factor, and confirm that the relative index demonstrates reduced sensitivity to climate change and multidecadal variability. In particular, we show in three observational SST datasets that the relative index provides a more consistent classification of historical El Niño and La Niña oceanic conditions that is more robust across climatological periods compared to the nonrelative index. Forecast skill of the relative Niño-3.4 index in the North American Multimodel Ensemble (NMME) and ACCESS-S2 is slightly reduced for targets during the first half of the year because subtracting the tropical mean removes a source of additional skill. For targets in the second half of the year, the relative and nonrelative indices are equally skillful. Observed ENSO teleconnections in 200-hPa geopotential height and precipitation during key seasons are sharper and explain more variability over Australia and the contiguous United States when computed with the relative index. Overall, the relative Niño-3.4 index provides a more robust option for real-time monitoring and forecasting ENSO in a changing climate. Significance Statement The goal of this study is to further explore a relative sea surface temperature index for monitoring and prediction of El Niño–Southern Oscillation. Sea surface temperature indices are typically computed as a difference from a 30-yr climatological average, and El Niño and La Niña events occur when values exceed a certain threshold. This method is suitable when the climate is stationary. However, because of climate change and other lower-frequency variations, historical El Niño and La Niña events are reclassified depending on which climatological period is selected. A relative index is investigated to ameliorate this problem.

Evaluating the Performance of the North Pacific Victoria Mode as an ENSO Predictor Based on Multimodel Ensemble Hindcasts

Abstract The Victoria mode (VM), similar to the Pacific meridional mode (PMM), is forced by North Pacific Oscillation atmospheric variability. Both the boreal spring VM and PMM can trigger the onset of El Niño–Southern Oscillation (ENSO) events in the following winter. Previous studies have examined the precursor relationship between the PMM and ENSO based on a subset of models drawn from the North American Multimodel Ensemble (NMME) system. They suggested that the PMM can act as a precursor to El Niño events, whereas it fails to predict La Niña events. Utilizing the hindcasts of these models from NMME, this study further investigates the role of the VM as an ENSO predictor to examine the real usefulness of the VM for ENSO prediction. When compared with the PMM, the VM can predict both El Niño and La Niña events with some skill, showing that the VM seems to be a more reliable predictor of ENSO. We found that the unique role of the VM in ENSO prediction originates from the symmetric impact of the VM on ENSO events. The VM, as a basin-scale sea surface temperature (SST) pattern, combines the role of the SST over the subtropical northeastern Pacific that is similar to the PMM in initializing El Niño events with that of the SST over the western North Pacific that is different from PMM in initializing La Niña events, resulting in the symmetric effect of the VM on ENSO prediction. Thus, it is useful to consider VM variability as a reference for ENSO prediction. Significance Statement We aim to investigate whether the robust relationship between the boreal spring Victoria mode (VM) and the following winter ENSO in observations and climate models has any real predictive use, thus bridging the gap between theory and practical application. Our analyses based mainly on model hindcast datasets examine the forecasting skill of the VM for El Niño and La Niña events. We also reveal the symmetric impact of VM on ENSO through the analysis of observations, which explains the VM’s skill in predicting both El Niño and La Niña events. This study deepens our understanding of the effect of North Pacific SST variability on ENSO.

Improving Boreal Summer Precipitation Predictions From the Global NMME Through Res34‐Unet

AbstractGlobal warming and climate change have increased the frequency and intensity of floods and droughts, limiting economic development and threatening human survival. Therefore, accurate global forecasts well in advance of precipitation are essential to facilitate timely adaptation. Current seasonal forecasts are based mainly on numerical models, but raw forecasts suffer from systematic bias and under/overdispersion problems and cannot be directly used in applications. In addition, bias correction methods for global forecasts need to be further developed. Based on a fusion of ResNet34 and Unet, called Res34‐Unet, deep learning post‐processing is proposed to correct global precipitation forecasts of the North American Multi‐Model Ensemble (NMME). Compared with raw global NMME predictions, post‐processed precipitation predictions can be improved by up to 45%, which is significant at different latitudes. Feature importance analysis shows that precipitation itself, meridional wind, and sea surface temperature are key factors.

The Indian Ocean Weakens the ENSO Spring Predictability Barrier: Role of the Indian Ocean Basin and Dipole Modes

Abstract Prediction of El Niño–Southern Oscillation (ENSO) is hindered by a spring predictability barrier (SPB). In this paper, we investigate the effects of the Indian Ocean (IO) on the SPB. Using a seasonally varying extended IO–ENSO recharge oscillator model, we find that the SPB is much weakened when IO is coupled with ENSO. To gauge the relative role of the Indian Ocean dipole (IOD) and the Indian Ocean Basin (IOB) modes in weakening ENSO SPB, we develop an empirical dynamical model, the linear inverse model (LIM). By coupling/decoupling the IOB or IOD mode with ENSO, we show that the IOB significantly weakens eastern Pacific and central Pacific ENSO SPBs, while the IOD plays a weaker role. The evolution of the optimum initial structures also illustrates the importance of the IOB in ENSO SPB. Moreover, the IOB strongly influences the forecast skill of La Niña SPB rather than El Niño SPB. This point is also identified through six coupled models from the North American multimodel ensemble. It may be related to the role of the IO in the asymmetry in the duration of El Niño and La Niña. The IOB-induced easterly wind anomalies are conducive to the development of La Niña and thus the prediction of La Niña events, whereas these anomalous easterlies are less important during the development of El Niño and the related forecast of El Niño events.

Reconciling roles of the South China Sea summer monsoon and ENSO in prediction of the Indian Ocean dipole

The Indian Ocean dipole (IOD) is a remarkable interannual variability in the tropical Indian Ocean. The improved prediction of IOD is of a great value because of its large socioeconomic impacts. Previous studies reported that both El Niño-Southern Oscillation (ENSO) and South China Sea summer monsoon (SM) play a dominant role in the western and eastern pole of the IOD, respectively. They can be used as predictors of the IOD at 3 month lead beyond self-persistence. Here, we develop an empirical model of multi-factors in which the western pole is predicted by ENSO and persistence and the eastern pole is predicted by SM and persistence. This new empirical model outperforms largely the average level of the dynamical models from the North American multi-model ensemble (NMME) project in predicting the peak IOD in boreal autumn, with a correlation coefficient of ∼0.86 and a root mean square error of ∼0.24 °C. Furthermore, the hit rate of positive culminated IOD in this new empirical model is equivalent to that in current NMME models (above 65%), much higher than that for negative culminated IOD. This improvement of skill using the empirical model suggests a perspective for better understanding and predicting the IOD.

Unraveling the Formation Mechanism of Exceptionally Strong Marine Heatwaves in the Northeast Pacific in 2020

Abstract Throughout 2020, a persistent marine heatwave (MHW) event occurred in the northeast (NE) Pacific, which exhibited a record-breaking intensity and long duration. Three peaks of sea surface temperature (SST) anomaly are identified in April, July, and November 2020, respectively, all of which are caused by the surface latent heat flux (LH) term. A Taylor expansion analysis for the LH term reveals that positive LH anomaly (LHA) played the decisive role in increasing the latent heating effect, whereas the mixed layer depth anomaly was not important in the 2020 MHW. The positive LHA was primarily caused by the southerly wind anomalies, which advected more humid air from lower latitudes to the NE Pacific and thus reduced the difference between the saturated specific humidity at sea surface and actual surface specific humidity. This reduced sea–air humidity difference was conducive to less evaporation, leading to positive LHA. Using an atmospheric general circulation model forced by observed SSTs, we find that although the atmospheric circulation anomalies associated with the 2020 MHW can be partly constrained by both tropical Pacific SST and local SST over the North Pacific, the role of internal atmospheric variability in the 2020 MHW’s formation cannot be overlooked. The prediction skill for the 2020 MHW in the North American Multimodel Ensemble models is limited to one month, indicating the challenge of accurately predicting MHW in the NE Pacific. The finding about the contribution of sea–air humidity difference to the LHA provides a new insight into the formation mechanism of MHW. Significance Statement An exceptionally strong and persistent marine heatwave occurred in the northeast Pacific throughout 2020, with three peaks in April, July, and November, causing considerable damage to local fisheries and ecosystems. This study found that the evolution of this marine heatwave was due to the less-than-normal release of latent heat at the ocean surface. This was mainly caused by the reduced sea–air humidity difference, which was attributed to the increased near-surface moisture transported by southerly wind anomalies. Our model experiments demonstrated that the associated anomaly fields in the atmosphere partly stemmed from stochastic processes. The state-of-the-art dynamic models can only predict this marine heatwave one month in advance, indicating the necessity of improving prediction skill for marine heatwave’s evolution in current models.

Forecast skill of Bangladesh summer monsoon rainfall in C3S and NMME models after calibration

This study assesses the dynamical seasonal predictions initialized in April and May for forecasting Bangladesh summer (June to September: JJAS) monsoon rainfall (BSMR) over the 1993–2016 period. The BSMR from nine models, sourced from the North American Multi-Model Ensemble (NMME) and the Copernicus Climate Change Service (C3S), undergoes a calibration process. This calibration employs the Canonical Correlation Analysis (CCA) technique to rectify biases within each individual model's ensemble mean BSMR data (referred as predictor or X variable). These corrections are made in comparison to observed BSMR (referred as predictand or Y variable), acquired from the Climate Hazards Group InfraRed Precipitation with Station data. Subsequently, the models that undergo calibration are amalgamated to construct a calibrated multi-model ensemble (CMME), which, in turn, facilitates the generation of a forecast for BSMR. The CCA correction brings about a significant improvement in root-mean-square error, underscoring the presence of correctable systematic biases in the raw model forecasts. However, these CCA corrections weakly enhance the skill (anomaly correlation) across the region. The scores that assess discrimination (the two-alternative forced-choice: 2AFC and the area under the relative operating characteristic curve: ROC for above/below normal BSMR) for tercile-based forecasts exceeded 50 % across a substantial portion of the region. This indicates a superior level of discrimination compared to what one would anticipate based on climatology. Using the CMME approach, a probabilistic forecast for the 2022 BSMR was generated and proved quite effective in capturing the observed 2022 BSMR tercile, which includes below-normal and above-normal categories of rainfall in the central-southern and northern regions of Bangladesh respectively. Furthermore, the absence of substantial skill improvements may be attributed to inaccuracies in the teleconnection patterns of the simulated first leading principal component (PC) time series of BSMR with the El Niño Southern Oscillation. In contrast, the second PC time series exhibits a similar connection to observations. These findings emphasize the importance and utility of statistical post-processing in producing reliable seasonal climate outlooks for the region.

Predictable and Unpredictable Components of Cape Town Winter Rainfall

Abstract In early 2018, due in part to a severe and extended meteorological drought, Cape Town was at risk of being one of the first major metropolitan areas in the world to run out of water. The magnitude of the crisis was exacerbated by the fact that such a prolonged and severe drought was both unanticipated and unpredicted. In this work, we analyze data from both observations and seasonal forecasts made as part of the North American Multimodel Ensemble (NMME) to better understand the predictability of rainfall in the Cape Town (CT) region. We find that there are statistically significant correlations between observed CT rainfall and sea surface temperatures in the tropical Atlantic (∼0.45) as well as a pattern of 200-mb geopotential height (z200) anomalies resembling the Southern Annular Mode (SAM; ∼0.4). Examination of hindcasts from the NMME demonstrates that the models accurately reproduce the observed correlation between CT rainfall and z200 anomalies. However, they fail to reproduce correlations between CT rainfall and the tropical South Atlantic. Decomposition of the correlations into contributions from predictable and unpredictable components indicates that CT rainfall in the models is dominated by unpredicted atmospheric variability (correlation ∼ 0.84) relative to predicted (correlation ∼ 0.14), which may be related to the failure to simulate the connection with the tropical Atlantic. Significance Statement Water crises are occurring with increasing severity and frequency around the globe. The ability to accurately forecast wet season rainfall would be invaluable to water managers and other decision-makers. Here, we explore the reasons behind the failure of a suite of operational seasonal forecast models to accurately predict rainfall in the Cape Town region of South Africa.

PyNMME: A python toolkit to retrieve, calibrate and verify seasonal precipitation forecasts

The North American Multi-Model Ensemble (NMME) experiment assembles valuable ensemble forecasts from more than ten global climate models (GCMs). Focusing on environmental applications of NMME forecasts, this paper develops the pyNMME, a Python-based toolkit to implement data retrieval, forecast calibration and forecast verification. Specifically, the toolkit is composed of three modules that streamline the processes of retrieving the NMME data for locations and seasons of interest from high-dimensional datasets, calibrating raw GCM forecasts by the linear scaling, quantile mapping and Bernoulli-Gamma-Gaussian models and verifying predictive performances by twenty verification metrics and six types of diagnostic plots. The results show that the characteristics of bias, reliability and skill of raw forecasts vary across the globe and that the three calibration models in the pyNMME effectively improve raw forecasts to different extents. Overall, the pyNMME can serve as a useful tool to exploit valuable NMME precipitation forecasts for environmental modelling and management.

Statistical refinement of the North American Multi-Model Ensemble precipitation forecasts over Karoon basin, Iran

Abstract An effective postprocessing approach has been examined to improve the skill of North American Multi-Model Ensemble (NMME) precipitation forecasts in the Karoon basin, Iran. The Copula–Bayesian approach was used along with the Normal Kernel Density marginal distribution and the Kernel Copula function. This process creates more than one postprocessing precipitation value as results candidates (first pass). A similar process is used for a second pass to obtain preprocessed values based on the candidate inputs, which helps identify the most suitable postprocessed value. The application of the technique for order preference by similarity to the ideal solution method based on conditional probability distribution functions of the first and second passes leads to achieving final improved forecast data among the existing candidates. To validate the results, data from 1982–2010 and 2011–2018 were used for the calibration and forecast periods. The results show that while the GFDL and CFS2 models tend to overestimate precipitation, most other NMME models underestimate it. Postprocessing improves the accuracy of forecasts for most models by 20%–40%. Overall, the proposed Copula–Bayesian postprocessing approach could provide more reliable forecasts with higher spatial and temporal consistency, better detection of extreme precipitation values, and a significant reduction in uncertainties.

Causes and Predictions of 2022 Extremely Hot Summer in East Asia

AbstractIn the background of long‐term global warming, the northern hemisphere experienced an extremely hot summer in 2022 with the hottest on record for Europe and China, and the second‐hottest for North America and Asia. The hot summer concurred with a triple‐dip La Niña in the tropical Pacific. Given the extremity of the hot summer in East Asia in 2022, in this work, we examine the associated atmospheric circulation and assess the real‐time predictions from the North American Multimodel Ensemble (NMME). Also, we identify the contributions of long‐term warming trends, sea surface temperature (SST) forcing, and an atmospheric feedback to the hot summer. The hot summer in East Asia in 2022 is due to the extremely strong and westward expanded western Pacific subtropical high. That leads to cloud cover reduction and increases in net downward shortwave radiation at the surface, and further strengthens the positive surface air temperature (SAT) anomalies. In contrast, the seasonal‐interannual variation of SST has a minor impact. Thus, the hot summer is mainly associated with the long‐term trend and amplified by the positive feedback among the SAT, cloud cover, and net downward shortwave radiation. NMME with the initial conditions in May 2022 predicts positive SAT anomalies in most regions of East Asia, but does not capture the observed spatial distribution pattern and amplitudes. The failure implies the challenge of state‐of‐the‐art climate models in predicting such extreme events.

Abrupt sub-seasonal rainfall variability over India during summer monsoon 2021: Interaction between midlatitude and tropical circulation

The Indian subcontinent witnessed abnormal subseasonal rainfall variations during the summer monsoon season 2021 with abrupt change (54%) in rainfall from August to September. According to India Meteorological Department, in August Indian landmass received 81% rainfall of its long period average (LPA), which is lowest since 2002. On the other hand, anomalously high rainfall (+ 35% of its LPA) was received in September. The physical mechanisms responsible for such unusual changes in monthly/subseasonal rainfall are unravelled in this study. In August 2021, the southward displacement of Asian jet (SWDAJ) corroborated by westward and northwestward extension of the Western north Pacific (WNP) anticyclone induced moisture divergence over the monsoon trough region and northwestern parts of India are mainly responsible for the deficit rainfall. In the East Asian region, strong low-level westerly wind anomalies in the northern flank of WNP anticyclone couples with the upper-level westerlies associated with SWDAJ and the resulting interaction due to the barotropic–baroclinic coupling maintains WNP circulation. Apart from WNP anticyclone, anomalous Sea Surface Temperature warming over the southeast equatorial Indian Ocean also contributed to the suppressed rainfall over India. In contrast to August, during September the northward displacement of the Asian jet and associated anomalous upper-level anticyclone over northwest India linked to the mid-latitude wave train emanating from the northeast Atlantic contributed to enhanced rainfall over northwest India. Anomalous upper-level high over central Asia/northwest of India increased the moist baroclinic instability over northwest India, and enhanced rainfall there. The Climate Forecast System version 2 (CFSv2) model based on May initial conditions is some extent predicted rainfall change from August and September 2021 over India with much lesser magnitude. Inability of the model in capturing the changes in Asian jet, midlatitude wave train and associated teleconnections/interactions to Indian Summer Monsoon (ISM) rainfall limits its prediction skill. Further, the North American Multi Model Ensemble (NMME) models also could predict the correct sign of ISM rainfall anomaly in August and September, with May initial conditions, but failed to capture the spatial patterns, which needs further investigation.

Evaluating Multimodel Ensemble Seasonal Climate Forecasts on Rangeland Plant Production in the California Annual Grassland

Seasonal precipitation and temperature directly affect total plant production in the California Annual Grassland (CAG). Technological advances have resulted in skillful seasonal climate forecasts (i.e., significant correlations between actual and forecasted climate), which could be input into plant production models to inform stocking and other rangeland management decisions. This study presents a procedure for forecasting plant production in the CAG ecosystem to predict annual plant production for grazing, restoration, or other rangeland management practices using a combination of historical gridMET climate data and seasonal hindcasts (i.e., retrospective forecasts, from the North American Multi-Model Ensemble program). The results of this study first confirmed high forecast skill, throughout the growing season at all sites. We also identified skillful plant production forecasts across most of the growing season at two sites and in three of the seven forecasting months at one study site. Forecasting climate and end-of-year plant production across the growing season at three CAG sites allowed us to identify the places and times in the growing season when forecasting might be most helpful in informing management decisions. Integrating plant production forecasting into rangeland management practices could significantly improve rangeland management outcomes. These procedures provide a user guide for creating plant production forecasts for any given area of interest and may be applicable across a wide range of other agricultural and rangeland management systems.

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 comparison of seasonal rainfall forecasts over Central America using dynamic and hybrid approaches from Copernicus Climate Change Service seasonal forecasting system and the North American Multimodel Ensemble

AbstractSeasonal rainfall forecasts provide information several months ahead to support decision making. These forecasts may use dynamic, statistical, or hybrid approaches, but their comparative value is not well understood over Central America. This study conducts a regional evaluation of seasonal rainfall forecasts focusing on two of the leading dynamic climate ensembles: the Copernicus Climate Change Service seasonal forecasting system (C3S) and the North American Multimodel Ensemble (NMME). We compare the multimodel ensemble mean and individual model predictions of seasonal rainfall over key wet season periods in Central America to better understand their relative forecast skill at the seasonal scale. Three types of rainfall forecasts are compared: direct dynamic rainfall predictions from the C3S and NMME ensembles, a statistical approach using the lagged observed sea surface temperature (SST), and an indirect hybrid approach, driving a statistical model with dynamic ensemble SST predictions. Results show that C3S and NMME exhibit similar regional variability with strong performance in the northern Pacific part of Central America and weaker skill primarily in eastern Nicaragua. In the northern Pacific part of the region, the models have high skill across the wet season. Indirect forecasts can outperform the direct rainfall forecasts in specific cases where the direct forecasts have lower predictive power (e.g., eastern Nicaragua during the early wet season). The indirect skill generally reflects the strength of SST associations with rainfall. The indirect forecasts based on Tropical North Atlantic SSTs are best in the early wet season and the indirect forecasts based on Niño3.4 SSTs are best in the late wet season when each SST zone has a stronger association with rainfall. Statistical predictions are competitive with the indirect and direct forecasts in multiple cases, especially in the late wet season, demonstrating how a variety of forecasting approaches can enhance seasonal forecasting.

NASA’s NMME-based S2S hydrologic forecast system for food insecurity early warning in southern Africa

In situ hydrologic monitoring over regions most susceptible to food insecurity can be a challenge in current times due to various socio-economic and political issues in combination with environmental factors such as ongoing famine or drought. Hydrologic monitoring and initializing forecasts based on remotely sensed and analyzed data can contribute significantly to early warning in such regions. Routine hydrologic forecasts, as provided by NASA’s Hydrologic Forecasting and Analysis System (NHyFAS), are a recent addition to early warning systems. A custom instance of NHyFAS, termed FLDAS-Forecast, is used by FEWS NET’sLand Data Assimilation System (FLDAS). The FLDAS-Forecast’s dynamic forecasting component was originally set up with Goddard Earth Observing System (GEOS) forecast inputs and has been recently expanded with precipitation forecast forcing from the North American Multi-Model Ensemble (NMME). This paper describes the improvements in seasonal hydrologic forecasts produced with this updated system. Evaluations in this study focus on soil moisture across southern Africa’s growing season. Soil moisture forecasts are benchmarked and evaluated relative to climatology-based forecasts and historic runs, which are driven by observation-based meteorological forcing fields, and they are verified with remotely sensed observations of soil moisture and vegetation. Through multiple deterministic and probabilistic skill assessments, we show that using the larger ensemble of NMME precipitation inputs in the forecast system results in higher quality hydrologic forecasts than are allowed by climatology- or GEOS-only-based forecasts. Further, the near-real-time NMME-based rootzone soil moisture forecasts were able to correctly predict developing drought conditions over southern Africa through late 2019 and into early 2020.