Interannual Prediction Research Articles

Seasonal climate forecasts of the South Asian monsoon using multiple coupled models

This study addresses seasonal climate forecasts using coupled atmosphere—ocean multimodels. Using as many as 67 different seasonal-forecast runs per season from a variety of coupled (atmosphere—ocean) models consensus seasonal forecasts have been prepared from about 4500 experiments. These include the European Center’s DEMETER (Development of a European Multi-Model Ensemble System for Seasonal to Inter-Annual Prediction) database and a suite of Florida State University (FSU) models (based on different combinations of physical parametrizations). This is one of the largest databases on coupled models. The monsoon region was selected to examine the predictability issue. The methodology involves construction of seasonal anomalies of all model forecasts for a number of variables including precipitation, 850 hPa winds, 2-m/surface temperatures, and sea surface temperatures. This study explores the skills of the ensemble mean and the FSU multimodel superensemble. The metrics for forecast evaluation include computation of hindcast and verification anomalies from model/observed climatology, time-series of specific climate indices, and standard deterministic ensemble mean scores such as anomaly correlation coefficient and root mean square error. The results were deliberately prepared to match the metrics used by European DEMETER models. Invariably in all modes of evaluation, the results from the FSU multimodel superensemble demonstrate greater skill for most of the variables tested here than those obtained in earlier studies. The specific inquiry of this study was on this question: is it going to be wetter or drier, warmer or colder than the long-term recent climatology of the monsoon; and where and when during the next season? These results are most encouraging, and they suggest that this vast database and the superensemble methodology are able to provide some useful answers to the seasonal monsoon forecast issue compared to the use of single climate models or from the conventional ensemble averaging.

The Development of Seasonal and Inter-Annual Climate Forecasting

A review of the development and status of seasonal to inter-annual climate forecasting up until 2001 is presented covering not only the successes but also identifying some of the major challenges remaining. Included is discussion on the history of the enterprise; the scientific basis of modern seasonal to inter-annual prediction and its background of predictability theory; the current status of predictions and the measurement of their skill; the experiences and consequences of the 1997–1998 El Nino event; approaches to linking forecasts with applications; and a view to the future.

Operational predictability of monthly average maximum temperature over the Iberian Peninsula using DEMETER simulations and downscaling

The multi-model ensemble for seasonal to interannual prediction developed in the European Union project DEMETER has been used to quantify the predictability of monthly average maximum temperature that could be achieved operationally over the Iberian Peninsula. Statistical downscaling based on canonical correlation analysis is applied to increase the spatial resolution available from the global models. The downscaling is based on empirical connections between the North Atlantic sea level pressure and monthly average maximum temperature over the Iberian Peninsula. The maximum temperature estimated from the multi-model ensemble and the single models is compared to the observations. The statistical downscaling model skill is characterized by means of the correlation, variance fraction and the Brier skill score. The results suggest the following: the downscaling model works properly when driven by observed large-scale fields in terms of the correlation and the variance fraction scores, despite some problems owing to sample degeneracy; the predictability is almost limited to February, which is one of the initialization months of the DEMETER ensemble, and it is lost when this month is not considered as starting month. This result is supported by the fact that the areally averaged reproducibility is lower during non-initialization months. In any case, the analysis of the variance test performed reveals that the monthly average maximum temperature is scarcely predictable. Finally, the results also support the advantage of using a multi-model ensemble approach instead of single models participating in DEMETER.

Interannual variability and predictability of African easterly waves in a GCM

The interannual variability of African Easterly Waves (AEWs) is assessed with the help of spatio-temporal spectral analysis (STSA) and complex empirical orthogonal functions methods applied to the results of ten-member multiyear ensemble simulations. Two sets of experiments were conducted with the Meteo-France ARPEGE-Climat GCM, one with interactive soil moisture (control), and the other with soil moisture relaxed towards climatological monthly means calculated from the control. Composites of Soudano–Sahelian AEWs were constructed and associated physical processes and dynamics were studied in the frame of the waves. It is shown that the model is able to simulate realistically some interannual variability in the AEWs, and that this dynamical aspect of the West African climate is potentially predictable (i.e. signal can be extracted from boundary conditions relatively to internal error of the GCM), especially along the moist Guinean coast. Compared with ECMWF 15-year reanalysis (ERA15), the maximum activity of AEWs is located too far to the South and is somewhat too zonal, but the main characteristics of the waves are well represented. The major impact of soil moisture relaxation in the GCM experiments is to reduce the seasonal potential predictability of AEWs over land by enhancing their internal variability.

Operational predictability of monthly average maximum temperature over the Iberian Peninsula using DEMETER simulations and downscaling

The multi-model ensemble for seasonal to interannual prediction developed in the European Union project DEMETER has been used to quantify the predictability of monthly average maximum temperature that could be achieved operationally over the Iberian Peninsula. Statistical downscaling based on canonical correlation analysis is applied to increase the spatial resolution available from the global models. The downscaling is based on empirical connections between the North Atlantic sea level pressure and monthly average maximum temperature over the Iberian Peninsula.The maximum temperature estimated from the multi-model ensemble and the single models is compared to the observations. The statistical downscaling model skill is characterized by means of the correlation, variance fraction and the Brier skill score. The results suggest the following: the downscaling model works properly when driven by observed large-scale fields in terms of the correlation and the variance fraction scores, despite some problems owing to sample degeneracy; the predictability is almost limited to February, which is one of the initialization months of the DEMETER ensemble, and it is lost when this month is not considered as starting month. This result is supported by the fact that the areally averaged reproducibility is lower during non-initialization months. In any case, the analysis of the variance test performed reveals that the monthly average maximum temperature is scarcely predictable. Finally, the results also support the advantage of using a multi-model ensemble approach instead of single models participating in DEMETER.

Weather and seasonal climate prediction for South America using a multi-model superensemble

This work examines the feasibility of weather and seasonal climate predictions for South America using the multi-model synthetic superensemble approach for climate, and the multi-model conventional superensemble approach for numerical weather prediction, both developed at Florida State University (FSU). The effect on seasonal climate forecasts of the number of models used in the synthetic superensemble is investigated. It is shown that the synthetic superensemble approach for climate and the conventional superensemble approach for numerical weather prediction can reduce the errors over South America in seasonal climate prediction and numerical weather prediction. For climate prediction, a suite of 13 models is used. The forecast lead-time is 1 month for the climate forecasts, which consist of precipitation and surface temperature forecasts. The multi-model ensemble is comprised of four versions of the FSU-Coupled Ocean-Atmosphere Model, seven models from the Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction (DEMETER), a version of the Community Climate Model (CCM3), and a version of the predictive Ocean Atmosphere Model for Australia (POAMA). The results show that conditions over South America are appropriately simulated by the Florida State University Synthetic Superensemble (FSUSSE) in comparison to observations and that the skill of this approach increases with the use of additional models in the ensemble. When compared to observations, the forecasts are generally better than those from both a single climate model and the multi-model ensemble mean, for the variables tested in this study. For numerical weather prediction, the conventional Florida State University Superensemble (FSUSE) is used to predict the mass and motion fields over South America. Predictions of mean sea level pressure, 500 hPa geopotential height, and 850 hPa wind are made with a multi-model superensemble comprised of six global models for the period January, February, and December of 2000. The six global models are from the following forecast centers: FSU, Bureau of Meteorology Research Center (BMRC), Japan Meteorological Agency (JMA), National Centers for Environmental Prediction (NCEP), Naval Research Laboratory (NRL), and Recherche en Prevision Numerique (RPN). Predictions of precipitation are made for the period January, February, and December of 2001 with a ‘multi-analysis-multi-model’ superensemble where, in addition to the six forecast models just mentioned, five additional versions of the FSU model are used in the ensemble, each with a different initialization (analysis) based on different physical initialization procedures. On the basis of observations, the results show that the FSUSE provides the best forecasts of the mass and motion field variables to forecast day 5, when compared to both the models comprising the ensemble and the multi-model ensemble mean during the wet season of December–February over South America. Individual case studies show that the FSUSE provides excellent predictions of rainfall for particular synoptic events to forecast day 3. Copyright © 2005 Royal Meteorological Society

Probabilistic seasonal prediction of the winter North Atlantic Oscillation and its impact on near surface temperature

The North Atlantic Oscillation (NAO) is a major winter climate mode, describing one-third of the inter-annual variability of the upper-level flow in the Atlantic European mid-latitudes. It provides a statistically well-defined pattern to study the predictability of the European winter climate. In this paper, the predictability of the NAO and the associated surface temperature variations are considered using a dynamical prediction approach. Two state-of-the-art coupled atmosphere–ocean ensemble forecast systems are used, namely the seasonal forecast system 2 from the European Centre for Medium Range Weather Forecast (ECMWF) and the multi-model system developed within the joint European project DEMETER (Development of a European Multi-Model Ensemble Prediction System for Seasonal to Inter-annual Prediction). The predictability is defined in probabilistic space using the debiased ranked probability skill score with adapted discretization (RPSSD). The potential predictability of the NAO and its impact are also investigated in a perfect model approach, where each ensemble member is used once as “observation”. This approach assumes that the climate system is fully represented by the model physics.

DEVELOPMENT OF A EUROPEAN MULTIMODEL ENSEMBLE SYSTEM FOR SEASONAL-TO-INTERANNUAL PREDICTION (DEMETER)

A multi-model ensemble-based system for seasonal-to-interannual prediction has been developed in a joint European project known as DEMETER (Development of a European Multimodel Ensemble Prediction System for Seasonal to Interannual Prediction). The DEMETER system comprises seven global atmosphere–ocean coupled models, each running from an ensemble of initial conditions. Comprehensive hindcast evaluation demonstrates the enhanced reliability and skill of the multimodel ensemble over a more conventional single-model ensemble approach. In addition, innovative examples of the application of seasonal ensemble forecasts in malaria and crop yield prediction are discussed. The strategy followed in DEMETER deals with important problems such as communication across disciplines, downscaling of climate simulations, and use of probabilistic forecast information in the applications sector, illustrating the economic value of seasonal-to-interannual prediction for society as a whole.

Forecast Calibration and Combination: A Simple Bayesian Approach for ENSO

This study presents a new simple approach for combining empirical with raw (i.e., not bias corrected) coupled model ensemble forecasts in order to make more skillful interval forecasts of ENSO. A Bayesian normal model has been used to combine empirical and raw coupled model December SST Niño-3.4 index forecasts started at the end of the preceding July (5-month lead time). The empirical forecasts were obtained by linear regression between December and the preceding July Niño-3.4 index values over the period 1950–2001. Coupled model ensemble forecasts for the period 1987–99 were provided by ECMWF, as part of the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) project. Empirical and raw coupled model ensemble forecasts alone have similar mean absolute error forecast skill score, compared to climatological forecasts, of around 50% over the period 1987–99. The combined forecast gives an increased skill score of 74% and provides a well-calibrated and reliable estimate of forecast uncertainty.

Predictability of the Seasonal Mean Atmospheric Circulation during Autumn, Winter, and Spring

Abstract The predictability of the autumn, boreal winter, and spring seasons with foreknowledge of sea surface temperatures (SSTs) is studied using ensembles of seasonal simulations of three general circulation models (GCMs): the Center for Ocean–Land–Atmosphere Studies (COLA) GCM, the National Aeronautics and Space Administration Seasonal to Interannual Prediction Project (NSIPP) GCM, and the National Centers for Environmental Prediction (NCEP) GCM. Warm-minus-cold composites of the ensemble mean and observed tropical Pacific precipitation, averaged for the three warmest El Nino and three coldest La Nina winters, show large positive anomalies near the date line that extend eastward to the South American coast. The same is true for composites of the spring following the event. In the composites of the autumn preceding the event, the precipitation is weaker and shifted off the equator in the far eastern Pacific, where equatorial SSTs are too low to support convection. The corresponding boreal winter 200-hP...

U.S. regional climate simulations and seasonal forecasts

Does a regional climate model provide a more useful regional climatology than can be obtained from larger‐scale global analyses or a better regional forecast than can be obtained by a large‐scale seasonal prediction model? To examine these questions, U.S. regional spectral model (RSM) climate simulations forced by 1‐day global spectral model (GSM) forecasts initialized from the National Centers for Environmental Prediction (NCEP) operational analysis are compared with regional RSM simulations forced by the NASA Seasonal to Interannual Prediction Project general circulation model (GCM). The GCM was continuously forced by observed sea surface temperature variations, but since sea surface temperatures (SSTs) are so persistent, these forced GCM simulations were equivalent to seasonal GCM predictions, which are mainly based upon persistent SSTs anyhow. RSM simulations forced by these two global modes were compared at the same spatial resolution as the global models (200 km) and at higher resolution (50 km). Resolution was important for producing better geographic pictures but does not currently produce significantly more skillful regional climate simulations or forecasts of the temporal variability, which already have significant skill from the global models. However, regional climate simulations and forecasts better depict the precipitation intensity, especially over the U.S. West. Finally, re‐initialized (from the large‐scale analysis) 1‐day RSM forecasts were compared with continuous RSM simulations, and this increased the overall regional skill back to the original GSM skill, which was somewhat degraded in continuous RSM simulations.

China's Rainfall Interannual Predictability: Dependence on the Annual Cycle and Surface Anomalies

Abstract China's rainfall interannual predictability is generally believed to depend upon the accurate representation of its annual cycle as well as teleconnections with planetary surface anomalies, including tropical east Pacific sea surface temperature and Eurasian snow and soil moisture. A suite of general circulation model (GCM) simulations is used to ascertain the existence of these relationships. First, a comparison of thirty 1980–88 Atmospheric Model Intercomparison Project (AMIP) GCM simulations shows no clear correspondence between model skill to reproduce observed rainfall annual cycle and interannual variability. Thus, accurate representation of either component does not ensure the realistic simulation of the other. Second, diagnosis of the 1903–94 and 1950–97 National Center for Atmospheric Research (NCAR) Community Climate Model, version 3 (CCM3), ensemble integrations indicates the existence of teleconnections in which spring planetary surface anomalies lead China's summer rainfall variation...

Interannual prediction of the Paraná River

Interannual‐to‐decadal predictability of the Paraná river in South America is investigated by extracting near‐cyclic components in summer‐season streamflows at Corrientes over the period 1904–1997. It is found that oscillatory components with periods of about 2–5, 8 and 17 years are accompanied by statistically significant changes in monthly streamflow. Autoregressive predictive models are constructed for each component. Cross‐validated categorical hindcasts based on the 8‐yr predicted component are found to yield some skill up to four years in advance for below‐average flows. A prediction based upon the 8‐ and 17‐yr components including data up to 1999 suggests increased probability of below‐average flows until 2006.

Dynamical Seasonal Predictability of the Asian Summer Monsoon

Ensembles of hindcasts from seven models are analyzed to evaluate dynamical seasonal predictability of 850-hPa wind and rainfall for the Asian summer monsoon (ASM) during 1987, 1988, and 1993. These integrations were performed using observed sea surface temperatures and from observed initial conditions. The experiments were designed by the Climate Variability and Predictability, Working Group on Seasonal to Interannual Prediction as part of the Seasonal Prediction Model Intercomparison Project. Integrations from the European Union Prediction of Climate Variations on Seasonal to Interannual Timescales experiment are also evaluated. The National Centers for Environmental Prediction–National Center for Atmospheric Research and European Centre for Medium-Range Weather Forecasts reanalyses and observed pentad rainfall form the baseline against which the hindcasts are judged. Pattern correlations and root-mean-square differences indicate errors in the simulation of the time mean low-level flow and the rainfall exceed observational uncertainty. Most models simulate the subseasonal EOFs that are associated with the dominant variations of the 850-hPa flow during the ASM, but not with the fidelity exhibited by the reanalyses as determined using pattern correlations. Pattern correlations indicate that the first EOF, associated with the tropical convergence zone being located over the continental landmass, is best simulated. The higher-order EOFs are less well simulated, and errors in the magnitude and location of their associated precipitation anomalies compromise dynamical seasonal predictability and are related to errors of the mean state. In most instances the models fail to properly project the subseasonal EOFs/principal components onto the interannual variability with the result that hindcasts of the 850-hPa flow and rainfall are poor. In cases where the observed EOFs are known to be related to the boundary forcing, the failure of the models to properly project the EOFs onto the interannual variability indicates that the models are not setting up observed teleconnection patterns.

The Impact of Detailed Snow Physics on the Simulation of Snow Cover and Subsurface Thermodynamics at Continental Scales

The three-layer snow model is coupled to the global catchment-based Land Surface Model (LSM) of the NASA Seasonal to Interannual Prediction Project (NSIPP) project, and the combined models are used to simulate the growth and ablation of snow cover over the North American continent for the period 1987-1988. The various snow processes included in the three-layer model, such as snow melting and re-freezing, dynamic changes in snow density, and snow insulating properties, are shown (through a comparison with the corresponding simulation using a much simpler snow model) to lead to an improved simulation of ground thermodynamics on the continental scale.

An effective method for correcting the seasonal—interannual prediction of summer climate anomaly

An effective method was proposed for correcting the seasonal—interannual prediction of the summer climate anomaly. The predictive skill can be substantially improved by applying the method to the seasonal—interannual prediction carried out by a coupled ocean—atmosphere model. Thus the method has the potential to improve the operational summer climate predictions.

The effect of land‐surface feedbacks on the monsoon circulation

AbstractSummary the effects of feedbacks from land‐surface forcing on intraseasonal monsoon activity are studied by performing idealized sensitivity experiments with a general circulation model.In agreement with observations, the simulated intraseasonal monsoon activity is mainly described by irregular alternations of active spells and break spells associated with fluctuations of the Tropical Convergence Zone (TCZ) between a continental and an oceanic regime. In the model, the spatial characteristic of the intraseasonal monsoon variability is a robust feature which is primarily related to an internal mode of variability of the system, rather than to a response to land‐surface feedbacks. Experimentation indicates that the simulation of northward propagating events, related to transitions in the regime, does not require the inclusion of interactive surface hydrological processes. This suggests that the transitions are also mainly related to internal atmospheric dynamics.The temporal characteristics of the fluctuations between the two TCZ regimes, however, are influenced by an interactive surface. the low‐frequency intraseasonal monsoon variability is enhanced by hydrological surface feedbacks. When the surface interacts with the atmosphere, the active and break regimes of the monsoon are equally likely. In the absence of surface feedbacks, the probability distribution is modified and the changes depend on the land‐surface conditions imposed. the results show that the probability of a monsoon break exceeds that of an active phase when the imposed land‐surface conditions are based on climatological values for July. This asymmetry in the probability distribution affects intraseasonal monsoon variability. In turn, the time‐mean monsoon circulation, depending as it does on the statistics of the intraseasonal oscillations (such as frequency of occurrence and mean amplitude), is also modified by the surface feedbacks. It follows that the surface conditions play a role in the interannual predictability of the time‐mean monsoon.

Ocean Data Assimilation, Initialization, and Predictions of ENSO with a Coupled GCM

Abstract A scheme for making seasonal to interannual predictions of El Nino–Southern Oscillation with a coupled atmosphere–ocean general circulation model that incorporates subsurface ocean measurements in the initial conditions is described. Anomaly initial conditions are used in order to reduce initial shock and climate drift. The ocean component of the prediction model has a nearly global domain, and the coupled model does not employ anomaly coupling or empirical statistical corrections. Initial conditions for the ocean were obtained from a near-global ocean analysis produced by an ocean data assimilation system. The assimilation system uses a variationally formulated optimal interpolation method to analyze oceanic fields from temperature observations and a first-guess field provided by integrating a global ocean general circulation model. The period of the analysis was 1986 through 1992. The anomaly initial conditions for the ocean were generated by adding the anomalies of the assimilated fields from ...

Eurasian snow cover variability and northern hemisphere climate predictability

We present observational evidence demonstrating dynamic interactions and feedbacks between multi‐seasonal snow cover and winter‐time circulation anomalies over mid‐high latitudes. The cooling effect of snow cover is associated with a strengthened and more expansive Siberian high with more frequent, topographically constrained intrusions west and north. Early‐season snow cover variability leads to altered general circulation patterns consistent with the dominant mode of winter variability observed in the Northern Hemisphere troposphere. The implications of the surface‐atmosphere coupling for seasonal to interannual predictability are also discussed.

Biogeographical regions of the Iberian peninsula based on freshwater fish and amphibian distributions

We classified the main Iberian river basins based on the presence and absence of freshwater fishes and amphibians. For both taxonomic groups we analysed three data sets; 1) endemic species only, to search for biotic boundaries related to historical events, 2) indigenous species, which include endemic ones, to search for biotic boundaries related to ecological factors, 3) indigenous and well‐established introduced species, to assess the influence of man in the current biogeographical patterns of fishes and amphibians. We used both phenetic and cladistic methods, followed by a consensus analysis to provide an overall biogeographical pattern. Based on all fish distributions, the Iberian Peninsula is divided into three biogeographical regions: Cantabrian, Atlantic and Mediterranean, No boundary existed between the Cantabrian and Atlantic regions when only indigenous fish species were considered. This suggests that this boundary has been induced by man, probably through the differential introduction of fish species into reservoirs at one or other side of the boundary. Run‐off and the size of the river basins are the environmental factors that distinguished the Atlantic and Mediterranean regions. However, regionalization based only on endemic freshwater fishes showed a latitudinal pattern that agrees with the paleogeographic events of the Upper Oligocene‐Lower Miocene period. By contrast, one northern and one southern region were distinguished based on all amphibian distributions and on indigenous amphibians only, which suggests that human activity has not significantly affected the overall biogeographical pattern of amphibians in the Iberian Peninsula. Interannual predictability of precipitation best accounts for this regionalization. Based on endemic amphibians, the Iberian Peninsula is divided into three regions that closely resemble the three separate land areas of the Upper Eocene‐Lower Oligocene period. The consensus between the biogeographical regions based on fishes and amphibians yields five pairs of basins. Geological origin of the basins seems to better explain the consensus between the biogeographical patterns of fishes and amphibians, whereas ecological factors probably contribute to the differences between them.