scholarly journals Relating CMIP5 Model Biases to Seasonal Forecast Skill in the Tropical Pacific

2020 ◽  
Vol 47 (5) ◽  
Author(s):  
Hui Ding ◽  
Matthew Newman ◽  
Michael A. Alexander ◽  
Andrew T. Wittenberg
Keyword(s):  
Seasonal Forecast ◽  
Tropical Pacific ◽  
Forecast Skill ◽  
Cmip5 Model ◽  
Model Biases ◽  
The Tropical Pacific

scholarly journals An Assessment of the CFS Real-Time Seasonal Forecasts

2010 ◽  
Vol 25 (3) ◽  
pp. 950-969 ◽  
Author(s):  
Wanqiu Wang ◽  
Mingyue Chen ◽  
Arun Kumar
Keyword(s):  
Soil Moisture ◽  
Indian Ocean ◽  
Real Time ◽  
Tropical Pacific ◽  
Forecast Skill ◽  
Sea Surface ◽  
Cold Bias ◽  
Seasonal Forecasts ◽  
Forecast System ◽  
The Tropical Pacific

Abstract This study assesses the real-time seasonal forecasts for 2005–08 with the current National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). The forecasts are compared with retrospective forecasts (or hindcasts) for 1981–2004 to examine the consistency of the forecast system, and with the Atmospheric Model Intercomparison Project (AMIP) simulations forced with observed sea surface temperatures (SSTs) to contrast the realized skill against the potential predictability due to the specification of the observed sea surface temperatures. The analysis focuses on the forecasts of SSTs, 2-m surface air temperature (T2M), and precipitation. The CFS forecasts maintained a good level of prediction skill for SSTs in the tropical Pacific, the western Indian Ocean, and the northern Atlantic. The SST forecast skill is within the range of hindcast skill levels calculated with 4-yr windows, which can vary greatly associated with the interannual El Niño–Southern Oscillation (ENSO) variability. Overall, the SST forecast skill over the globe is comparable to the average of the hindcast skill. For the tropical eastern Pacific, however, the forecast skill at lead times longer than 2 months is less than the average hindcast skill due to the relatively weaker ENSO variability during the forecast period (2005–08). The forecasts and hindcasts show a similar level of precipitation skill over most of the globe. For T2M, the spatial distribution of skill differs substantially between the forecasts and hindcasts. In particular, the T2M skill of the forecasts for the Northern Hemisphere during its warm seasons is lower than that of the hindcasts. Comparison with the AMIP simulations shows similar levels of precipitation skill over the tropical Pacific. Over the tropical Indian Ocean, the CFS forecasts show a substantially higher level of skill than the AMIP simulations for a large part of the period. This conforms with the results from previous studies that while interannual variability in the tropical Pacific atmosphere is slaved to the underlying SST anomalies, specification of SSTs (as for the AMIP simulations) in the Indian Ocean may lead to incorrect simulation of the atmospheric variability. Over the tropical Atlantic, the precipitation skill of both the CFS forecasts and AMIP simulations is low, suggesting that SSTs have less control over the atmospheric anomalies and the predictability is low. The analysis reveals several deficiencies in the current CFS that need to be corrected for improved seasonal forecasting. For example, the CFS tends to consistently forecast larger ENSO amplitude and delayed transition between the ENSO phases. Forecasts of T2M also have a strong cold bias in Northern Hemisphere mid- to high latitudes during warm seasons. This error is due to initial soil moisture anomalies, which appear to be too wet compared with two other observational analyses. The strong impacts of soil moisture on the seasonal forecasts, and large discrepancies among the soil moisture analyses, call for more accurate specification of soil moisture. Furthermore, average forecast SST and T2M anomalies for 2005–08 show a cold bias over the entire globe, indicating that the model is unable to maintain the observed long-term warming trend.

Diagnosing Secular Variations in Retrospective ENSO Seasonal Forecast Skill Using CMIP5 Model‐Analogs

2019 ◽  
Vol 46 (3) ◽  
pp. 1721-1730 ◽  
Author(s):  
Hui Ding ◽  
Matthew Newman ◽  
Michael A. Alexander ◽  
Andrew T. Wittenberg
Keyword(s):  
Seasonal Forecast ◽  
Forecast Skill ◽  
Cmip5 Model ◽  
Secular Variations

scholarly journals Coupled Model Biases Breed Spurious Low‐Frequency Variability in the Tropical Pacific Ocean

2018 ◽  
Vol 45 (19) ◽  
Author(s):  
Dhrubajyoti Samanta ◽  
Kristopher B. Karnauskas ◽  
Nathalie F. Goodkin ◽  
Sloan Coats ◽  
Jason E. Smerdon ◽  
...  
Keyword(s):  
Pacific Ocean ◽  
Low Frequency ◽  
Coupled Model ◽  
Tropical Pacific ◽  
Tropical Pacific Ocean ◽  
Model Biases ◽  
Low Frequency Variability ◽  
The Tropical Pacific

Growing Coconut Palms in the Pacific Islands: Colliding Knowledge and Values

2020 ◽  
Author(s):  
Judith A. Bennett
Keyword(s):  
Pacific Islands ◽  
Social Values ◽  
Coconut Oil ◽  
Tropical Pacific ◽  
Experiential Knowledge ◽  
The West ◽  
Passive Resistance ◽  
The People ◽  
The Pacific ◽  
The Tropical Pacific

Coconuts provided commodities for the West in the form of coconut oil and copra. Once colonial governments established control of the tropical Pacific Islands, they needed revenue so urged European settlers to establish coconut plantations. For some decades most copra came from Indigenous growers. Administrations constantly urged the people to thin old groves and plant new ones like plantations, in grid patterns, regularly spaced and weeded. Local growers were instructed to collect all fallen coconuts for copra from their groves. For half a century, the administrations’ requirements met with Indigenous passive resistance. This paper examines the underlying reasons for this, elucidating Indigenous ecological and social values, based on experiential knowledge, knowledge that clashed with Western scientific values.

scholarly journals Yearly evolution of Euro-Atlantic weather regimes and of their sub-seasonal predictability

2021 ◽  
Author(s):  
Nicola Cortesi ◽  
Verónica Torralba ◽  
Llorenó Lledó ◽  
Andrea Manrique-Suñén ◽  
Nube Gonzalez-Reviriego ◽  
...  
Keyword(s):  
Annual Cycle ◽  
Winter Season ◽  
Skill Score ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Seasonal Forecasts ◽  
Sea Level Pressure ◽  
Weather Regimes ◽  
Start Dates ◽  
Weekly Frequency

AbstractIt is often assumed that weather regimes adequately characterize atmospheric circulation variability. However, regime classifications spanning many months and with a low number of regimes may not satisfy this assumption. The first aim of this study is to test such hypothesis for the Euro-Atlantic region. The second one is to extend the assessment of sub-seasonal forecast skill in predicting the frequencies of occurrence of the regimes beyond the winter season. Two regime classifications of four regimes each were obtained from sea level pressure anomalies clustered from October to March and from April to September respectively. Their spatial patterns were compared with those representing the annual cycle. Results highlight that the two regime classifications are able to reproduce most part of the patterns of the annual cycle, except during the transition weeks between the two periods, when patterns of the annual cycle resembling Atlantic Low regime are not also observed in any of the two classifications. Forecast skill of Atlantic Low was found to be similar to that of NAO+, the regime replacing Atlantic Low in the two classifications. Thus, although clustering yearly circulation data in two periods of 6 months each introduces a few deviations from the annual cycle of the regime patterns, it does not negatively affect sub-seasonal forecast skill. Beyond the winter season and the first ten forecast days, sub-seasonal forecasts of ECMWF are still able to achieve weekly frequency correlations of r = 0.5 for some regimes and start dates, including summer ones. ECMWF forecasts beat climatological forecasts in case of long-lasting regime events, and when measured by the fair continuous ranked probability skill score, but not when measured by the Brier skill score. Thus, more efforts have to be done yet in order to achieve minimum skill necessary to develop forecast products based on weather regimes outside winter season.

Successive Modulation of ENSO to the Future Greenhouse Warming

2008 ◽  
Vol 21 (1) ◽  
pp. 3-21 ◽  
Author(s):  
Soon-Il An ◽  
Jong-Seong Kug ◽  
Yoo-Geun Ham ◽  
In-Sik Kang
Keyword(s):  
General Circulation ◽  
Southern Oscillation ◽  
General Circulation Models ◽  
Tropical Pacific ◽  
Greenhouse Warming ◽  
Delayed Response ◽  
Subsurface Temperature ◽  
Climate System Model ◽  
The Mean ◽  
The Tropical Pacific

Abstract The multidecadal modulation of the El Niño–Southern Oscillation (ENSO) due to greenhouse warming has been analyzed herein by means of diagnostics of Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) coupled general circulation models (CGCMs) and the eigenanalysis of a simplified version of an intermediate ENSO model. The response of the global-mean troposphere temperature to increasing greenhouse gases is more likely linear, while the amplitude and period of ENSO fluctuates in a multidecadal time scale. The climate system model outputs suggest that the multidecadal modulation of ENSO is related to the delayed response of the subsurface temperature in the tropical Pacific compared to the response time of the sea surface temperature (SST), which would lead a modulation of the vertical temperature gradient. Furthermore, an eigenanalysis considering only two parameters, the changes in the zonal contrast of the mean background SST and the changes in the vertical contrast between the mean surface and subsurface temperatures in the tropical Pacific, exhibits a good agreement with the CGCM outputs in terms of the multidecadal modulations of the ENSO amplitude and period. In particular, the change in the vertical contrast, that is, change in difference between the subsurface temperature and SST, turns out to be more influential on the ENSO modulation than changes in the mean SST itself.

The tropical Pacific Oceanscape: Current issues, solutions and future possibilities.

2021 ◽  
Vol 166 ◽  
pp. 112181
Author(s):  
Michelle J. Devlin ◽  
Brett P. Lyons ◽  
Johanna E. Johnson ◽  
Jeremy M. Hills
Keyword(s):  
Tropical Pacific ◽  
The Tropical Pacific

Interannual Variations and Prediction of Spring Precipitation over China

2018 ◽  
Vol 31 (2) ◽  
pp. 655-670 ◽  
Author(s):  
YuJia You ◽  
Xiaojing Jia
Keyword(s):  
Regression Model ◽  
Yangtze River ◽  
Climate Models ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Interannual Variations ◽  
The Yangtze River ◽  
Spring Precipitation ◽  
Coupled Climate Models ◽  
Time Variations

The interannual variations and the prediction of the leading two empirical orthogonal function (EOF) modes of spring (April–May) precipitation over China for the period from 1951 to 2014 are investigated using both observational data and the seasonal forecast made by six coupled climate models. The leading EOF mode of spring precipitation over China (EOF1-prec) features a monosign pattern, with the maximum loading located over southern China. The ENSO-related tropical Pacific SST anomalies in the previous winter can serve as a precursor for EOF1-prec. The second EOF mode of spring precipitation (EOF2-prec) over China is characterized by a dipole structure, with one pole near the Yangtze River and the other one with opposite sign over the Pearl River delta. A North Atlantic sea surface temperature (SST) anomaly dipole in the preceding March is found contribute to the prec-EOF2 and can serve as its predictor. A physics-based empirical (P-E) model is then formulated using the two precursors revealed by the observational analysis to forecast the variations of EOF1-prec and EOF2-prec. Compared to coupled climate models, which have little skill in forecasting the time variations of the two EOF modes, this P-E model can significantly improve the forecast skill of their time variations. A linear regression model is further established using the time series forecast by the P-E model to forecast the spring precipitation over China. Results suggest that the seasonal forecast skill of the spring precipitation over southeastern China, especially over the Yangtze River area, can be significantly improved by the regression model.

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