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

A SST based large multi-model ensemble forecasting system for Indian summer monsoon rainfall

An ensemble mean and probabilistic approach is essential for reliable forecast of the All India Summer Monsoon Rainfall (AIR) due to the seminal role played by internal fast processes in interannual variability (IAV) of the monsoon. In this paper, we transform a previously used empirical model to construct a large ensemble of models to deliver useful probabilistic forecast of AIR. The empirical model picks up predictors only from global sea surface temperature (SST). Methodology of construction implicitly incorporates uncertainty arising from internal variability as well as from the decadal variability of the predictor-predictand relationship. The forecast system demonstrates the capability of predicting monsoon droughts with high degree of confidence. Results during independent verification period (1999-2008) suggest a roadmap for generating empirical probabilistic forecast of monsoon IAV for practical delivery to the user community

Dynamics of "internal” interannual variability of the Indian summer monsoon in a GCM

The poor predictability of the Indian summer monsoon (ISM) appears to be due to the fact that a large fraction of interannual variability (IAV) is governed by unpredictable "internal” low frequency variations. Mechanisms responsible for the internal IAV of the monsoon have not been clearly identified. Here, an attempt has been made to gain insight regarding the origin of internal IAV of the seasonal (June-September, JJAS) mean rainfall from "internal” IAV of the ISM simulated by an atmospheric general circulation model (AGCM) driven by fixed annual cycle of sea surface temperature (SST). The underlying hypothesis that monsoon ISOs are responsible for internal IAV of the ISM is tested. The spatial and temporal characteristics of simulated summer intraseasonal oscillations (ISOs) are found to be in good agreement with those observed. A long integration with the AGCM forced with observed SST, shows that ISO activity over the Asian monsoon region is not modulated by the observed SST variations. The internal IAV of ISM, therefore, appears to be decoupled from external IAV. Hence, insight gained from this study may be useful in understanding the observed internal IAV of ISM. The spatial structure of the ISOs has a significant projection on the spatial structure of the seasonal mean and a common spatial mode governs both intraseasonal and interannual variability. Statistical average of ISO anomalies over the season (seasonal ISO bias) strengthens or weakens the seasonal mean. It is shown that interannual anomalies of seasonal mean are closely related to the seasonal mean of intraseasonal anomalies and explain about 50% of the IAV of the seasonal mean. The seasonal mean ISO bias arises partly due to the broad-band nature of the ISO spectrum allowing the time series to be aperiodic over the season and partly due to a non-linear process where the amplitude of ISO activity is proportional to the seasonal bias of ISO anomalies. The later relation is a manifestation of the binomial character of rainfall time series. The remaining 50% of the IAV may arise due to land-surface processes, interaction between high frequency variability and ISOs, etc

Later wet seasons with more intense rainfall over Africa under future climate change

Changes in the seasonality of precipitation over Africa have high potential for detrimental socio-economic impacts due to high societal dependence upon seasonal rainfall. Here, for the first time we conduct a continental scale analysis of changes in wet season characteristics under the RCP 4.5 and RCP 8.5 climate projection scenarios across an ensemble of CMIP5 models using an objective methodology to determine the onset and cessation of the wet season. A delay in the wet season over West Africa and the Sahel of over 5-10 days on average, and later onset of the wet season over Southern Africa is identified, and associated with increasing strength of the Saharan Heat Low in late boreal summer, and a northward shift in the position of the tropical rain belt over August-December. Over the Horn of Africa rainfall during the `short rains' season is projected to increase by over 100mm on average by the end of the 21st century under an RCP 8.5 scenario. Average rainfall per rainy day is projected to increase, while the number of rainy days in the wet season declines in regions of stable or declining rainfall (West and Southern Africa) and remains constant in Central Africa, where rainfall is projected to increase. Adaptation strategies should account for shorter wet seasons, increasing intensity and decreasing rainfall frequency, which will have implications for crop yields and surface water supplies

Developing a framework for an early warning system of seasonal temperature and rainfall tailored to aquaculture in Bangladesh

The occurrence of high temperature and heavy rain events during the monsoon season are a major climate risk affecting aquaculture production in Bangladesh. Despite the advances in the seasonal forecasting, the development of operational tools remains a challenge. This work presents the development of a seasonal forecasting approach to predict the number of warm days (NWD) and number of heavy rain days (NHRD) tailored to aquaculture in two locations of Bangladesh (Sylhet and Khulna). The approach is based on the use of meteorological and pond temperature data to generate linear models of the relationship between three-monthly temperature and rainfall statistics and NWD and NHRD, and on the evaluation of the skill of three operational dynamical models from the North American Multi-Model Ensemble (NMME) project. The linear models were used to evaluate the forecasts for two seasons and 1-month lead time: May to July (MJJ), forecast generated in April, and August to October (ASO), forecast generated in July. Differences were observed in the skill of the models predicting maximum temperature and rainfall (Spearman correlation, Root Mean Square Error, Bias statistics, and Willmott’s Index of Agreement,), in addition to NWD and NHRD from linear models, which also vary for the target seasons and location. In general, the models show higher predictive skill for NWD than NHRD, and for Sylhet than in Khulna. Among the three evaluated NMME models, CanSIPSv2 and GFDL-SPEAR exhibit the best performance, they show similar features in terms of error metrics, but CanSIPSv2 presents a lower interannual standard deviation

The global monsoon system: research and forecast

The main objective of this workshop was to provide a forum for discussion between researchers and forecasters on the current status of monsoon forecasting and on priorities and opportunities for monsoon research. WMO hopes that through this series of quadrennial workshops, the following goals can be accomplished: (a) to update forecasters on the latest reseach findings and forecasting technology; (b) to update researchers on monsoon analysis and forecasting; (c) to identify basic and applied research priorities and opportunities; (d) to identify opportunities and priorities for acquiring observations; (e) to discuss the approach of a web-based training document in order to update forecasters on developments of direct relevance to monsoon forecasting

An assessment of Indian monsoon seasonal forecasts and mechanisms underlying monsoon interannual variability in the Met Office GloSea5-GC2 system

We assess Indian summer monsoon seasonal forecasts in GloSea5-GC2, the Met Office fully coupled subseasonal to seasonal ensemble forecasting system. Using several metrics, GloSea5-GC2 shows similar skill to other state-of-the-art forecast systems. The prediction skill of the large-scale South Asian monsoon circulation is higher than that of Indian monsoon rainfall. Using multiple linear regression analysis we evaluate relationships between Indian monsoon rainfall and five possible drivers of monsoon interannual variability. Over the time period studied (1992-2011), the El Nino-Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD) are the most important of these drivers in both observations and GloSea5-GC2. Our analysis indicates that ENSO and its teleconnection with the Indian rainfall are well represented in GloSea5-GC2. However, the relationship between the IOD and Indian rainfall anomalies is too weak in GloSea5-GC2, which may be limiting the prediction skill of the local monsoon circulation and Indian rainfall. We show that this weak relationship likely results from a coupled mean state bias that limits the impact of anomalous wind forcing on SST variability, resulting in erroneous IOD SST anomalies. Known difficulties in representing convective precipitation over India may also play a role. Since Indian rainfall responds weakly to the IOD, it responds more consistently to ENSO than in observations. Our assessment identifies specific coupled biases that are likely limiting GloSea5-GC2 prediction skill, providing targets for model improvement

Probabilistic estimates of future changes in California temperature and precipitation usingstatistical and dynamical downscaling

Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling