The Performance of ECMWF Subseasonal Forecasts to Predict the Rainy Season Onset Dates in Vietnam
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Abstract The onset of the rainy season is an important date for the mostly rain-fed agricultural practices in Vietnam. Subseasonal to seasonal (S2S) ensemble hindcasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) are used to evaluate the predictability of the rainy season onset dates (RSODs) over five climatic subregions of Vietnam. The results show that the ECMWF model reproduces well the observed interannual variability of RSODs, with a high correlation ranging from 0.60 to 0.99 over all subregions at all lead times (up to 40 days) using five different RSOD definitions. For increasing lead times, forecasted RSODs tend to be earlier than the observed ones. Positive skill score values for almost all cases examined in all subregions indicate that the model outperforms the observed climatology in predicting the RSOD at subseasonal lead times (∼28–35 days). However, the model is overall more skillful at shorter lead times. The choice of the RSOD criterion should be considered because it can significantly influence the model performance. The result of analyzing the highest skill score for each subregion at each lead time shows that criteria with higher 5-day rainfall thresholds tend to be more suitable for the forecasts at long lead times. However, the values of mean absolute error are approximately the same as the absolute values of the mean error, indicating that the prediction could be improved by a simple bias correction. The present study shows a large potential to use S2S forecasts to provide meaningful predictions of RSODs for farmers.Keywords:
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Wet season
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Abstract Utilizing the subseasonal‐to‐seasonal (S2S) operational forecasts from the European Centre for Medium‐Range Weather Forecasts, the forecast skill for East Asian extreme cold events during 2015–2019 is evaluated. The results from the ensemble mean surface air temperature anomaly, the extreme forecast index, and the continuous ranked probability score skill reveal that extreme cold events can be captured by numerical models with a lead time of 7 days. It is also found that long‐persistent extreme cold events tend to have a longer skillful forecast lead time, which can exceed 10 days. The long skillful forecast lead time indicates that these events have a high intrinsic predictability, and the remote sea surface temperature anomaly, tropical intra‐seasonal oscillation and stratospheric polar vortex may be possible reasons for this predictability. The results suggest that it may be possible to make S2S and beyond skillful forecasts.
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Canonical correlation analysis (CCA) is explored as a multivariate linear statistical methodology with which to forecast fluctuations of the El Niño/Southern Oscillation (ENSO) in real time. CCA is capable of identifying critical sequence of predictor patterns that tend to evolve into subsequent patterns that can be used to form a forecast. The CCA model is used to forecast the 3-month mean sea surface temperature (SST) in several regions of the tropical Pacific and Indian oceans for projection times of 0 to 4 seasons beyond the immediately forthcoming season. The predictor variables, representing the climate situation in the four consecutive 3-month periods ending at the time of the forecast, are 1) quasi-global seasonal mean sea level pressure (SLP) and 2) SST in the predictand regions themselves. Forecast skill is estimated using cross-validation, and persistence is used as the primary skill control measure. Results indicate that a large region in the eastern equatorial Pacific (120°−170°W longitude) has the highest overall predictability, with excellent skill realized for winter forecasts made at the end of summer. CCA outperforms persistence in this region under most conditions, and does noticeably better with the SST included as a predictor in addition to the SLP. It is demonstrated that better forecast performance at the longer lead times would be obtained if some significantly earlier (i.e., up to 4 years) predictor data were included, because the ability to predict the lower-frequency ENSO phase changes would increase. The good performance of the current system at shorter lead times appears to be based largely on the ability to predict ENSO evolution for events already in progress. The forecasting of the eastern tropical Pacific SST using CCA is now done routinely on a monthly basis for a 0-, 1-, and 2-season lead at the Climate Analysis Center. Further refinements, and expected associated increases in skill, are planned for the coming several years.
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Abstract The onset of the rainy season is an important date for the mostly rain-fed agricultural practices in Vietnam. Subseasonal to seasonal (S2S) ensemble hindcasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) are used to evaluate the predictability of the rainy season onset dates (RSODs) over five climatic subregions of Vietnam. The results show that the ECMWF model reproduces well the observed interannual variability of RSODs, with a high correlation ranging from 0.60 to 0.99 over all subregions at all lead times (up to 40 days) using five different RSOD definitions. For increasing lead times, forecasted RSODs tend to be earlier than the observed ones. Positive skill score values for almost all cases examined in all subregions indicate that the model outperforms the observed climatology in predicting the RSOD at subseasonal lead times (∼28–35 days). However, the model is overall more skillful at shorter lead times. The choice of the RSOD criterion should be considered because it can significantly influence the model performance. The result of analyzing the highest skill score for each subregion at each lead time shows that criteria with higher 5-day rainfall thresholds tend to be more suitable for the forecasts at long lead times. However, the values of mean absolute error are approximately the same as the absolute values of the mean error, indicating that the prediction could be improved by a simple bias correction. The present study shows a large potential to use S2S forecasts to provide meaningful predictions of RSODs for farmers.
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Lead time
Ensemble average
Wet season
Lead (geology)
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Abstract Arctic Oscillation (AO) variability impacts climate anomalies over the middle to high latitudes of the Northern Hemisphere. Recently, state‐of‐the‐art climate prediction models have proved capable of skillfully predicting the AO during the winter, revealing a previously unrealized source of climate predictability. Hindcasts from the North American Multimodel Ensemble (NMME) show that the seasonal, ensemble mean 200 hPa AO index is skillfully predicted up to 7 months in advance and that this skill, especially at longer leads, is coincident with previously unknown and strong relations ( r > 0.9) with the El Niño–Southern Oscillation (ENSO). The NMME is a seasonal prediction system that comprises eight models and up to 100 members with forecasts out to 12 months. Observed ENSO‐AO correlations are within the spread of the NMME member correlations, but the majority of member correlations are stronger than observed, consistent with too high predictability in the model, or overconfidence.
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The skill of seasonal precipitation forecasts is assessed worldwide -grid point by grid point- for the forty-years period 1961-2000. To this aim, the ENSEMBLES multi-model hindcast is considered. Although predictability varies with region, season and lead-time, results indicate that 1) significant skill is mainly located in the tropics -20 to 40% of the total land areas-, 2) overall, SON (MAM) is the most (less) skillful season and 3) predictability does not decrease noticeably from one to four months lead-time -this is so especially in northern south America and the Malay archipelago, which seem to be the most skillful regions of the world-. An analysis of teleconnections revealed that most of the skillful zones exhibit significant teleconnections with El Ni\~no. Furthermore, models are shown to reproduce similar teleconnection patterns to those observed, especially in SON -with spatial correlations of around 0.6 in the tropics-. Moreover, these correlations are systematically higher for the skillful areas. Our results indicate that the skill found might be determined to a great extent by the models' ability to properly reproduce the observed El Ni\~no teleconnections, i.e., the better a model simulates the El Ni\~no teleconnections, the higher its performance is.
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Using decadal prediction experiments from the WCRP/CMIP5 suite that were initialized every year from 1960-onward, we explore long-lead predictability of ENSO events. Both deterministic and probabilistic skill metrics are used to assess the ability of these decadal prediction systems to reproduce ENSO variability as represented by the NINO3.4 index (EN3.4). Several individual systems as well as the multi-model mean can predict ENSO events 3–4 years in advance, though not for every event during the hindcast period. This long-lead skill is beyond the previously documented predictability limits of initialized prediction systems. As part of the analysis, skill in reproducing the annual cycle of EN3.4, and the annual cycle of its interannual variability is examined. Most of the prediction systems reproduce the seasonal cycle of EN3.4, but are less able to capture the timing and magnitude of the variability. However, for the prediction systems used here, the fidelity of annual cycle characteristics does not appear to be related to the system's ability to predict ENSO events. In addition, the performance of the multi-model ensemble mean is explored and compared to the multi-model mean based solely on the most skillful systems; the latter is found to yield better results for the deterministic metrics. Finally, an analysis of the near-surface temperature and precipitation teleconnections reveals that the ability of the systems to detect ENSO events far in advance could translate into predictive skill over land for several lead years, though with reduced amplitudes compared to observations.
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In this study, the sources and strengths of statistical short-term climate predictability for local surface climate (temperature and precipitation) and 700-mb geopotential height in the Northern Hemisphere are explored at all times of the year at lead times of up to one year. Canonical correlation analysis is the linear statistical methodology employed. Predictor and predictand averaging periods of 1 and 3 months are used, with four consecutive predictor periods, followed by a lead time and then a single predictand period. Predictor fields are quasi-global sea surface temperature (SST), Northern Hemisphere 700-mb height, and prior values of the predictand field itself. Cross-validation is used to obtain, to first order, uninflated skill estimates. Results reveal mainly modest statistical predictive skill except for certain fields, locations, and times of the year when predictability is far above chance expectation and good enough to be beneficial to appropriate users. The time of year when skills are generally highest is January through April. Global SST is the most skill-producing predictor field, perhaps because 1) the lower boundary condition is a more consistent influence on climate on timescales of 1 to 3 months than the atmosphere's internal dynamics, or 2) SST is the only field in this study that provides tropical information directly. Prediction is generally more skillful on the 3-month than 1-month timesale. The skill of the forecasts is often insensitive to the forecast lead time; that is, inserting 3, or sometimes 6 or more, months between the predictor and predictand periods causes little skill decrease from that of 1 month or less. This has favorable implications for long-lead forecasting. Much of the higher skill occurs in association with fluctuations of the El Niño/Southern Oscillation (ENSO) and is found in midwinter through midspring in specific pockets of the Pacific and North American regions. Predictive skill for precipitation is also found in the same context but is lower than that for 700-mb height or temperature. Warm season predictability, slightly lower than that of winter-spring and not clearly documented in earlier work, is related to episodes of like-signed SST anomalies in the tropical oceans throughout the world in the preceding months. There is an interdecadal component in the variability of these global SST conditions. Generalized positive (negative) 700-mb and surface temperature anomalies in middle to late summer (but fall in southern Europe), generally at subtropical latitudes throughout much of the Northern Hemisphere (but with some midlatitude continental protrusions), occur following episodes of uniformly positive (negative) SST anomalies in the tropical oceans throughout the world in the preceding winter through late spring. The occurrence of a mature warm (cold) ENSO extreme the previous winter may contribute to such a worldwide SST condition in the intervening spring season. In the United States, the effect is a general (monopole) anomalous warmth (coolness) from mid-July through August across much of the country.
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