Interannual Climate Prediction Research Articles

ENSO affects the North Atlantic Oscillation 1 year later.

We demonstrate a 1-year lagged extratropical response to the El Niño-Southern Oscillation (ENSO) in observational analyses and climate models. The response maps onto the Arctic Oscillation and is strongest in the North Atlantic, where it resembles the North Atlantic Oscillation (NAO). Unexpectedly, these 1-year lagged teleconnections are at least as strong as the better-known simultaneous winter connections. However, the 1-year lagged response is oppositein sign to the simultaneous response such that 1 year later, El Niño is followed by a positive NAO, whereas La Niña is followed by a negative NAO. The lagged response may also interfere with simultaneous ENSO teleconnections. We show here that these effects are unlikely to be caused by residual aliasing of ENSO cycles; rather, slowly migrating atmospheric angular momentum anomalies explain both the sign and the timing of the extratropical response. Our results have implications for understanding ENSO teleconnections, explaining observed extratropical climate variability and interpreting seasonal to interannual climate predictions.

The 2023/24 El Niño and the Feasibility of Long-Lead ENSO Forecasting

Abstract Through its atmospheric teleconnections, El Niño–Southern Oscillation (ENSO) shifts and disrupts weather and climate patterns far beyond the equatorial Pacific where it occurs often resulting in catastrophic consequences in many countries of the world. It is also the largest source of seasonal and interannual climate predictability. Despite its huge importance, ENSO forecasting is still not performed operationally at longer leads than about 6 months ahead. At the same time, there is mounting scientific evidence that forecasts are possible even more than a year in advance. Early warning of ENSO events could substantially mitigate some of the most damaging impacts, such as floods, droughts, and harvest failure and help avoid famine, migration, and disease outbreaks. Here, we present forecasts from a statistical ENSO model of the next El Niño predicted to occur in the winter of 2023/24, at lead times between 11 and 17 months ahead of an expected peak in December 2023. We use a statistical unobserved dynamic components model (EDCM) based on subsurface ocean temperatures as well as sea surface temperatures and zonal wind stress. EDCM has been previously validated through hindcasts of the major El Niños since 1970 and through real-time forecasts of the 2015/16 and 2018/19 El Niños. Our statistical framework and results indicate that there is potential for doubling the operational predictive lead time of ENSO to at least 12 months, with additional promise for even earlier anticipation of 19 months. Such longer-lead forecasts could be of high value, because decision-making and management in a number of key socioeconomic sectors could be greatly improved.

Diurnal Cycle Dependence of ENSO Influence on the Winter Surface Air Temperature in Southeastern China

Abstract It is well known that El Niño–Southern Oscillation (ENSO) can influence the East Asian winter climate by modifying the atmospheric circulation over the western North Pacific (WNP). While the impact on precipitation in southeastern China has been extensively studied, the ENSO signal in surface air temperature (SAT) remains overlooked. In this paper, we identify robust ENSO footprints in the winter daily minimum SAT in southeastern China, with El Niño winters generally accompanied by warmer-than-normal minimum SAT anomalies. In contrast, the responses of maximum SAT are weak and negligible, suggesting a diurnal cycle–dependent ENSO influence. Further analysis indicates that this diurnal cycle dependence stems primarily from the disparate surface radiative heating between day and night induced by ENSO-related local total cloud cover (TCC) change. The warmer minimum SAT occurring in the early morning of El Niño winters is mainly caused by the enhanced surface downward longwave radiative heating as a result of the TCC increase. However, in the afternoon of El Niño winters, although the anomalous horizontal advection of warm air plays a role, there is surface radiative cooling as the weakening of solar radiation due to TCC reflection overwhelms the increase in downward longwave radiation, which leads to a weakened sensible heat flux and thus has a cooling effect on the SAT. Ultimately, these two processes effectively cancel each other out and together produce insignificant maximum SAT responses. Our conclusions carry important implications for the seasonal to interannual winter climate prediction in southeastern China. Significance Statement The purpose of this study is to better understand how ENSO influences winter surface air temperature in southeastern China. Despite insignificant responses of winter daily maximum temperature to ENSO, we find here a robust ENSO influence on the daily minimum temperature, with warm anomalies tending to occur during El Niño events. Further evidence suggests that this diurnal cycle–dependent ENSO influence is mainly caused by ENSO-induced change in local cloudiness, which has a different radiative heating effect between day and night. Our findings are of great importance for improving regional climate prediction, which will benefit social and economic activities.

Decadal variability of the interannual climate predictability associated with the Indo-Pacific oceanic channel dynamics in CCSM4

The lag correlations between the observed sea surface temperature anomalies (SSTA) in the southeastern tropical Indian Ocean in fall and those in the Pacific cold tongue at the one-year time lag are calculated in running windows of 7–11 years and are shown to have decadal variability. Similar decadal variability has also been identified in the historical simulations of the Community Climate System Model version 4 (CCSM4) that participates in the Coupled Model Intercomparison Project phase-5 (CMIP5). During the positive phases of the decadal variability, the significant lag correlations are diagnosed to be dominated by the oceanic channel dynamics, i.e., upwelling Kelvin waves associated with the Indian Ocean Dipole (IOD) force enhanced Indonesian Throughflow transport anomalies and western Pacific thermocline anomalies to propagate eastward, resulting in SSTA in the central-eastern equatorial Pacific Ocean. During the negative phases of the decadal variability, the subsurface lag correlations suggest that the western Pacific Ocean are still dominated by the oceanic channel dynamics, but do not correlate well with the SSTA in the cold tongue due to a deeper thermocline in the eastern equatorial Pacific. The IOD-ENSO teleconnection is not affected significantly by the anthropogenic forcing during either the positive or the negative decadal phases. The CCSM4 model is found to underestimate Indonesian Throughflow transport variability but overestimate the westerly wind anomalies in the western-central equatorial Pacific, which forces unrealistic anomalies in the equatorial Pacific Ocean associated with the IOD.

Eddy-induced meridional transport variability at ocean western boundary

Meridional transport variability induced by westward-propagating eddies impinging on the western boundary is investigated both analytically and numerically. A simple theory is first developed in the framework of the reduced-gravity model which relates eddy-induced meridional transport to eddy thickness anomalies propagated into the western boundary by long Rossby waves, and this is followed by a suite of numerical model experiments. It is found that eddies impinging on the western boundary excite boundary waves that propagate equatorward along the western boundary, which leads to coherent meridional overturning circulation (MOC) anomalies equatorward of the incident eddy field. The magnitude and duration of eddy-induced MOC anomalies are variable and irregular, ranging from less than 1 Sv to over 5 Sv and from less than 10 days to over 100 days. Importantly, these eddy-induced MOC anomalies lead to considerable meridional heat transport variability across the latitudes, with implications for seasonal and interannual climate variability and prediction.

Contributions of Weakly Coupled Data Assimilation–Based Land Initialization to Interannual Predictability of Summer Climate over Europe

Abstract The land surface is a potential source of climate predictability over the Northern Hemisphere midlatitudes but has received less attention than sea surface temperature in this regard. This study quantified the degree to which realistic land initialization contributes to interannual climate predictability over Europe based on a coupled climate system model named FGOALS-g2. The potential predictability provided by the initialization, which incorporates the soil moisture and soil temperature of a land surface reanalysis product into the coupled model with a dimension-reduced projection four-dimensional variational data assimilation (DRP-4DVar)-based weakly coupled data assimilation (WCDA) system, was analyzed first. The effective predictability (i.e., prediction skill) of the hindcasts by FGOALS-g2 with realistic and well-balanced initial conditions from the initialization were then evaluated. Results show an enhanced interannual prediction skill for summer surface air temperature and precipitation in the hindcast over Europe, demonstrating the potential benefit from realistic land initialization. This study highlights the significant contributions of land surface to interannual predictability of summer climate over Europe.

Skilful interannual climate prediction from two large initialised model ensembles

Climate prediction skill on the interannual timescale, which sits between that of seasonal and decadal, is investigated using large ensembles from the Met Office and CESM initialised coupled prediction systems. A key goal is to determine what can be skillfully predicted about the coming year when combining these two ensembles together. Annual surface temperature predictions show good skill at both global and regional scales, but skill diminishes when the trend associated with global warming is removed. Skill for the extended boreal summer (months 7–11) and winter (months 12–16) seasons are examined, focusing on circulation and rainfall predictions. Skill in predicting rainfall in tropical monsoon regions is found to be significant for the majority of regions examined. Skill increases for all regions when active ENSO seasons are forecast. There is some regional skill for predicting extratropical circulation, but predictive signals appear to be spuriously weak.

Interannual Subtropical Indian Ocean Variability due to Long Baroclinic Planetary Waves

AbstractLow-frequency variability in the south Indian Ocean is studied by analyzing 200 years of output from a fully coupled climate model simulation. At time scales of 2–10 years, the variability is dominated by westward-propagating features that form on the eastern side of the basin. Using feature tracking and clustering, the spatiotemporal characteristics and preferred pathways of the propagating features are identified and studied in detail. By comparison of the phase speed and vertical structure of the propagating anomalies identified by the feature tracking with linear theory, we conclude that these features are likely mode 1 or 2 baroclinic planetary waves. The effects of this low-frequency variability on the climate system is investigated. By analysis of the mixed-layer temperature budget, it is shown that at particular geographic locations, the propagating features can substantially modify the near-surface ocean and induce significant fluxes of heat into the atmosphere. In turn, these heat fluxes can drive a coherent atmospheric response, although this response does not appear to feed back onto the ocean. Finally, we discuss the implications for the interannual climate predictability.

Switch Between El Nino and La Nina is Caused by Subsurface Ocean Waves Likely Driven by Lunar Tidal Forcing

The El Nino-Southern Oscillation (ENSO) is the dominant interannual variability of Earth’s climate system, and strongly modulates global temperature, precipitation, atmospheric circulation, tropical cyclones and other extreme events. However, forecasting ENSO is one of the most difficult problems in climate sciences affecting both interannual climate prediction and decadal prediction of near-term global climate change. The key question is what cause the switch between El Nino and La Nina. For the past 30 years, ENSO forecasts have been limited to short lead times after ENSO sea surface temperature (SST) anomaly has already developed, but unable to predict the switch between El Nino and La Nina. Here, we demonstrate that the switch between El Nino and La Nina is caused by a subsurface ocean wave propagating from western Pacific to central and eastern Pacific and then triggering development of SST anomaly. This is based on analysis of all ENSO events in the past 136 years using multiple long-term observational datasets. The wave’s slow phase speed and decoupling from atmosphere indicate that it is a forced wave. Further analysis of Earth’s angular momentum budget and NASA’s Apollo Landing Mirror Experiment suggests that the subsurface wave is likely driven by lunar tidal gravitational force.

Role of sea-ice initialization in climate predictability over the Weddell Sea

Potential impact of sea-ice initialization on the interannual climate predictability over the Weddell Sea is investigated using a coupled general circulation model. Climate variability in the Weddell Sea is generally believed to have association with remote forcing such as El Niño-Southern Oscillation and the Southern Annual Mode. However, sea-ice variability in the Weddell Sea has been recently suggested to play additional roles in modulating local atmospheric variability through changes in surface air temperature and near-surface baroclinicity. Reforecast experiments from September 1st, in which the model’s sea-surface temperature (SST) and sea-ice concentration (SIC) are initialized with observations using nudging schemes, show improvements in predicting the observed SIC anomalies in the Weddell Sea up to four months ahead, compared to the other experiments in which only the model’s SST is initialized. During austral spring (Oct–Dec) of lower-than-normal sea-ice years in the Weddell Sea, reforecast experiments with the SST and SIC initializations reasonably predict high surface air temperature anomalies in the Weddell Sea and high sea-level pressure anomalies over the Atlantic sector of the Southern Ocean. These results suggest that accurate initialization of sea-ice conditions during austral winter is necessary for skillful prediction of climate variability over the Weddell Sea during austral spring.

Seasonal to interannual predictability of oceanic net primary production inferred from satellite observations

Seasonal to interannual predictions of ecosystem dynamics have the potential to improve the management of living marine resources. Prediction of oceanic net primary production (NPP), the foundation of marine food webs and the biological carbon pump, is particularly promising, with recent analysis suggesting that ecosystem feedback processes may lead to higher predictability of NPP at interannual scales than for physical variables like sea surface temperature (SST). Here, we assessed the potential predictability of oceanic NPP and SST across seasonal to interannual lead times using reduced dimension, linear dynamical spatio-temporal models (rDSTM). This approach combines empirical orthogonal function (EOF) analysis with vector autoregressive (VAR) modeling to simplify the analysis of spatio-temporal data. The rDSTMs were fitted to monthly NPP and SST anomalies derived from 20 years of remote sensing data (1997–2017), considering two alternative algorithms commonly used to estimate NPP (VGPM and Eppley-VGPM) and optimally analyzed SST fields (AVHRR OISST). The local decay of anomalies provided high predictability up to three months, and subsequent interactions with remote forcing significantly extended predictability beyond the initial anomaly decay. Indeed, interactions among spatial modes associated with the propagation of major climate modes, particularly the El Niño-Southern Oscillation (ENSO), extended the predictability horizon above one year in some regions. Patterns of enhanced NPP predictability matched the location of oligotrophic gyres and transition regions between ocean biomes, where fluctuations in biome boundaries generate large biogeochemical perturbations that leave lasting imprints on NPP. In these areas, the predictability horizon for NPP was longer than for SST, although SST was more predictable over large areas of the equatorial and northeast Pacific. Our results support the potential for extending seasonal to interannual physical climate predictions to predict ocean productivity.

Winter to winter recurrence of atmospheric circulation anomalies over East Asia and its impact on winter surface air temperature anomalies.

The persistence of atmospheric circulation anomalies over East Asia shows a winter to winter recurrence (WTWR) phenomenon. Seasonal variations in sea level pressure anomalies and surface wind anomalies display significantly different characteristics between WTWR and non-WTWR years. The WTWR years are characterized by the recurrence of both a strong (weak) anomalous Siberian High and an East Asian winter monsoon over two successive winters without persistence through the intervening summer. However, anomalies during the non-WTWR years have the opposite sign between the current and ensuing winters. The WTWR of circulation anomalies contributes to that of surface air temperature anomalies (SATAs), which is useful information for improving seasonal and interannual climate predictions over East Asia and China. In the positive (negative) WTWR years, SATAs are cooler (warmer) over East Asia in two successive winters, but the signs of the SATAs are opposite in the preceding and subsequent winters during the non-WTWR years.

Prediction of primary climate variability modes at the Beijing Climate Center

Climate variability modes, usually known as primary climate phenomena, are well recognized as the most important predictability sources in subseasonal–interannual climate prediction. This paper begins by reviewing the research and development carried out, and the recent progress made, at the Beijing Climate Center (BCC) in predicting some primary climate variability modes. These include the El Nino–Southern Oscillation (ENSO), Madden–Julian Oscillation (MJO), and Arctic Oscillation (AO), on global scales, as well as the sea surface temperature (SST) modes in the Indian Ocean and North Atlantic, western Pacific subtropical high (WPSH), and the East Asian winter and summer monsoons (EAWM and EASM, respectively), on regional scales. Based on its latest climate and statistical models, the BCC has established a climate phenomenon prediction system (CPPS) and completed a hindcast experiment for the period 1991–2014. The performance of the CPPS in predicting such climate variability modes is systematically evaluated. The results show that skillful predictions have been made for ENSO, MJO, the Indian Ocean basin mode, the WPSH, and partly for the EASM, whereas less skillful predictions were made for the Indian Ocean Dipole (IOD) and North Atlantic SST Tripole, and no clear skill at all for the AO, subtropical IOD, and EAWM. Improvements in the prediction of these climate variability modes with low skill need to be achieved by improving the BCC’s climate models, developing physically based statistical models as well as correction methods for model predictions. Some of the monitoring/prediction products of the BCC-CPPS are also introduced in this paper.

El Niño's impact on California precipitation: seasonality, regionality, and El Niño intensity

California has experienced severe drought in recent years posing great challenges to agricultural production, water resources, and land management. El Niño, as the prime source of seasonal to interannual climate predictability, offers the potential of amelioration of drought in California. Here El Niño’s impacts on California winter precipitation are examined, focusing on variations by season, region, and the strength of El Niño using observational data for the period 1901–2010. The El Niño influence on California precipitation strengthens from early to late winter and is stronger in the south than the north. Eight of ten moderate-to-strong El Niños in the late winter put southern California in the wettest tercile and none of these ten events put northern California in the driest tercile. The early to late winter strengthening of the El Niño impact on precipitation occurs even as El Niño weakens and is associated with a strengthening and eastward extending tropical deep convection anomaly allowed by the late winter warming of the climatological mean sea surface temperature over the tropical eastern Pacific.

Commentary: Communicating climate complexity

Commentary: Communicating climate complexity

STREAMFLOW FORECASTING FOR THE DAM ORÓS/CE FROM HYDROMETEOROLOGICAL DATA USING PERCEPTRONS

The modeling of seasonal and interannual streamflow forecasting at northeastern Brazil represents a great relevance problem to the use and management of water resources; which demands greater prediction ability models. This is still a difficult task to solve due to the seasonal and interannual climate variability at the semi-arid region. This work presents the artificial neural networks (ANN) as an alternative for modeling the seasonal to interannual climate prediction,. For the development of this task the hydropraphic Oros weir Basin was chosen due to its importance as water resources in the State of Ceara. According to recent studies, the temperatures of the North Atlantic, South Atlantic and equatorial Pacific can be satisfactorily as predictors for the Northeast climate. The proposed model predicts, in July, the next rainy season (January to June) river flow regime. This time frame is of great relevance for the allocation of water resources. Among the studied models, those using the average temperature anomalies of April, May and June preceding the predicted year as input data showed the highest Nash-Suttcliffe efficiency (0.80).

The North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction

The recent U.S. National Academies report, Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, was unequivocal in recommending the need for the development of a North American Multimodel Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multimodel ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation and has proven to produce better prediction quality (on average) than any single model ensemble. This multimodel approach is the basis for several international collaborative prediction research efforts and an operational European system, and there are numerous examples of how this multimodel ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test bed (CTB) NMME workshops (18 February and 8 April 2011), a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data are readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (www.cpc.ncep.noaa.gov/products/NMME/). Moreover, the NMME forecast is already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, and presents an overview of the multimodel forecast quality and the complementary skill associated with individual models.

Ensemble of sea ice initial conditions for interannual climate predictions

Polar climate studies are severely hampered by the sparseness of the sea ice observations. We aim at filling this critical gap by producing two 5-member sea ice historical simulations strongly constrained by ocean and atmosphere observational data and covering the 1958–2006 and 1979–2012 periods. This is the first multi-member sea ice reconstruction covering more than 50 years. The obtained sea ice conditions are in reasonable agreement with the few available observations. These best estimates of sea ice conditions serve subsequently as initial sea ice conditions for a set of 28 3-year-long retrospective climate predictions. We compare it to a set in which the sea ice initial conditions are taken from a single-member sea ice historical simulation constrained by atmosphere observations only. We find an improved skill in predicting the Arctic sea ice area and Arctic near surface temperature but a slightly degraded skill in predicting the Antarctic sea ice area. We also obtain a larger spread between the members for the sea ice variables, thus more representative of the forecast error.

Seasonal to interannual climate predictability in mid and high northern latitudes in a global coupled model

The upper limit of climate predictability in mid and high northern latitudes on seasonal to interannual time scales is investigated by performing two perfect ensemble experiments with the global coupled atmosphere–ocean–sea ice model ECHAM5/MPI-OM. The ensembles consist of six members and are initialized in January and July from different years of the model’s 300-year control integration. The potential prognostic predictability is analyzed for a set of oceanic and atmospheric climate parameters. The predictability of the atmospheric circulation is small except for southeastern Europe, parts of North America and the North Pacific, where significant predictability occurs with a lead time of up to half a year. The predictability of 2 m air temperature shows a large land–sea contrast with highest predictabilities over the sub polar North Atlantic and North Pacific. A combination of relatively high persistence and advection of sea surface temperature anomalies into these areas leads to large predictability. Air temperature over Europe, parts of North America and Asia shows significant predictability of up to half a year in advance. Over the ice-covered Arctic, air temperature is not predictable at time scales exceeding 2 months. Sea ice thickness is highly predictable in the central Arctic mainly due to persistence and in the Labrador Sea due to dynamics. Surface salinity is highly predictable in the Arctic Ocean, northern North Atlantic and North Pacific for several years in advance. We compare the results to the predictability due to persistence and show the importance of dynamical processes for the predictability.

Recent Advances On Soil Moisture Data Assimilation

This study reviews recent progress on soil moisture data assimilation. Data assimilation is a process of merging observations with a system dynamic model to provide an improved estimate of the states of the environment. The application of data assimilation in hydrology is relatively new, however, rapid progress has been made in the last decade or so with the available remotely sensed soil moisture data. After briefing the history of soil moisture data assimilation, the review focuses on the most common data assimilation methods and recent progress made in soil moisture data assimilation through a case study of the soil moisture initialization activities for NASA's seasonal and interannual climate prediction. The example demonstrates that soil moisture data assimilation has made great progress in the last decade, however is still in its infancy. Good quality remotely sensed soil moisture data with accurate uncertainty information at continental and global scale are needed to ensure the success of the operational use of soil moisture data assimilation technique. Further advancement on the current soil moisture data assimilation methods is necessary to be able to assimilate multisource hydrological remote sensing data into land surface models for the best use of various remote sensing data sources at continental and global scales.