scholarly journals Understanding Skill of Seasonal Mean Precipitation Prediction over California during Boreal Winter and Role of Predictability Limits

2020 ◽  
Vol 33 (14) ◽  
pp. 6141-6163
Author(s):  
Arun Kumar ◽  
Mingyue Chen
Keyword(s):  
Role Model ◽  
Systematic Approach ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
Boreal Winter ◽  
Southeast United States ◽  
The North ◽  
Precipitation Prediction ◽  
Prediction Systems

AbstractUsing extensive hindcasts from seasonal prediction systems participating in the North American Multi-Model Ensemble (NMME), possible causes for low skill in predicting seasonal mean precipitation over California during December–February (DJF) are investigated. The analysis focuses on investigating two possibilities for low prediction skill: role model biases or inherent predictability limits. The motivation for the analysis was the seasonal prediction during DJF 2015/16 that called for enhanced probability for above normal precipitation over southern California (which was consistent with expected conditions during an extreme El Niño) while the observed precipitation was below normal. Based on various analysis approaches and using hindcast datasets from multiple seasonal prediction systems, we build up the evidence that low skill in predicting seasonal mean precipitation over California is likely to be due to inherent predictability associated with a low signal-to-noise (SNR) regime. For the same set of seasonal prediction systems, the precipitation variability over California is contrasted with that over the southeast United States where prediction skill, as well as the SNR, is higher. The discussion also notes that building a knowledge base that goes beyond the well-known response to ENSO (based on the linear regression or composite techniques) has proven to be difficult and a systematic approach to reaching resolution to some of the overarching questions is required, and toward that end, a pathway is suggested.

scholarly journals Seasonal prediction of the boreal winter stratosphere

2021 ◽  
Author(s):  
Alice Portal ◽  
Paolo Ruggieri ◽  
Froila M. Palmeiro ◽  
Javier García-Serrano ◽  
Daniela I. V. Domeisen ◽  
...  
Keyword(s):  
Southern Oscillation ◽  
Polar Vortex ◽  
Seasonal Prediction ◽  
Wave Activity ◽  
Boreal Winter ◽  
Quasi Biennial Oscillation ◽  
Stratospheric Polar Vortex ◽  
Eddy Heat Flux ◽  
Prediction Systems ◽  
Model Database

AbstractThe predictability of the Northern Hemisphere stratosphere and its underlying dynamics are investigated in five state-of-the-art seasonal prediction systems from the Copernicus Climate Change Service (C3S) multi-model database. Special attention is devoted to the connection between the stratospheric polar vortex (SPV) and lower-stratosphere wave activity (LSWA). We find that in winter (December to February) dynamical forecasts initialised on the first of November are considerably more skilful than empirical forecasts based on October anomalies. Moreover, the coupling of the SPV with mid-latitude LSWA (i.e., meridional eddy heat flux) is generally well reproduced by the forecast systems, allowing for the identification of a robust link between the predictability of wave activity above the tropopause and the SPV skill. Our results highlight the importance of November-to-February LSWA, in particular in the Eurasian sector, for forecasts of the winter stratosphere. Finally, the role of potential sources of seasonal stratospheric predictability is considered: we find that the C3S multi-model overestimates the stratospheric response to El Niño–Southern Oscillation (ENSO) and underestimates the influence of the Quasi–Biennial Oscillation (QBO).

NMME-based Assessment of Prediction Skills of US Summertime Droughts and Pluvials: Role of Near-surface Temperature Prediction Skill

2021 ◽  
Author(s):  
Jonghun Kam ◽  
Sungyoon Kim ◽  
Joshua Roundy
Keyword(s):  
Surface Temperature ◽  
Climate Extremes ◽  
Prediction Skill ◽  
Seasonal Forecasts ◽  
Near Surface ◽  
Anomaly Correlation ◽  
The North ◽  
Anomaly Correlation Coefficient ◽  
Precipitation Anomalies

<p>This study used the North American Multi-Model Ensemble (NMME) system to understand the role of near surface temperature in the prediction skill for US climate extremes. In this study, the forecasting skill was measured by anomaly correlation coefficient (ACC) between the observed and forecasted precipitation (PREC) or 2-meter air temperature (T2m) over the contiguous United States (CONUS) during 1982–2012. The strength of the PREC-T2m coupling was measured by ACC between observed PREC and T2m or forecasted PREC and T2m over the CONUS. This study also assessed the NMME forecasting skill for the summers of 2004 (spatial anomaly correlation between PREC and T2m: 0.05), 2011 (-0.65), and 2012 (-0.60) when the PREC-T2m coupling is weaker or stronger than the 1982–2012 climatology (ACC:-0.34). This study found that most of the NMME models show stronger (negative) PREC-T2m coupling than the observed coupling, indicating that they fail to reproduce interannual variability of the observed PREC-T2m coupling. Some NMME models with skillful prediction for T2m show the skillful prediction of the precipitation anomalies and US droughts in 2011 and 2012 via strong PREC-T2m coupling despite the fact that the forecasting skill is year-dependent and model-dependent. Lastly, we explored how the forecasting skill for SSTs over north Pacific and Atlantic Oceans affects the forecasting skill for T2m and PREC over the US. The findings of this study suggest a need for the selective use of the current NMME seasonal forecasts for US droughts and pluvials.</p>

A novel model independence methodology to improve multi-model seasonal forecasts combination

2020 ◽  
Author(s):  
Franco Catalano ◽  
Andrea Alessandri ◽  
Kristian Nielsen ◽  
Irene Cionni ◽  
Matteo De Felice
Keyword(s):  
Seasonal Prediction ◽  
Boreal Summer ◽  
Brier Score ◽  
Boreal Winter ◽  
Seasonal Forecasts ◽  
Surface Solar Radiation ◽  
Model Ensembles ◽  
Prediction Systems ◽  
June July ◽  
Forecasts Combination

<p align="justify">Multi-model ensembles (MMEs) are powerful tools in dynamical climate prediction as they account for the overconfidence and the uncertainties related to single model ensembles. The potential benefit that can be expected by using a MME amplifies with the increase of the independence of the contributing Seasonal Prediction Systems. To this aim, a novel methodology has been developed to assess the relative independence of the prediction systems in the probabilistic information they provide.</p><p align="justify"><span>We considered the Copernicus C3S seasonal forecasts product considering the one-month lead retrospective seasonal predictions for boreal summer and boreal winter seasons (1</span><sup><span>st</span></sup><span> May and 1</span><sup><span>st</span></sup><span> November start dates, i.e. June-July-August, JJA and December-January-February, DJF). We analysed the seasonal hindcasts in terms of deterministic and probabilistic scores with a particular focus on </span><span>continental areas</span><span>, since little evaluation has been performed so far over land domains that is where most of the applications of seasonal forecasts are based. The most relevant target variables of interest for the energy users have been considered and skill differences between the prediction systems have been analysed together with related possible sources of predictability. The analysis evidenced the importance of snow-albedo processes for temperature predictions in DJF and the effect of the atmospheric dynamics through moisture convergence for the prediction of surface solar radiation in JJA. </span><span>A</span><span> new metric, the Brier Score Covariance, designed to quantify the probabilistic independence among the models, has been </span><span>developed and </span><span>applied to optimize model selection and combination strategies with a particular focus on the most relevant variables for energy applications.</span></p>

Evaluation of FIO-ESM v1.0 Seasonal Prediction Skills Over the North Pacific

2021 ◽  
Author(s):  
Yajuan Song ◽  
Xunqiang Yin
Keyword(s):  
Data Assimilation ◽  
North Pacific ◽  
Climate Model ◽  
Initial Conditions ◽  
Coupled Model ◽  
Seasonal Prediction ◽  
Optimal Interpolation ◽  
The North ◽  
Prediction Systems ◽  
The North Pacific

<p>Accurate prediction over the North Pacific, especially for the key parameter of sea<br>surface temperature (SST), remains a challenge for short-term climate prediction. In<br>this study, seasonal predicted skills of the First Institute of Oceanography Earth System<br>Model version 1.0 (FIO-ESM v1.0) over the North Pacific were assessed. Ensemble<br>adjustment Kalman filter (EAKF) and Projection Optimal Interpolation (Projection-OI) data<br>assimilation schemes were used to provide initial conditions for FIO-ESM v1.0 hindcasts<br>that were started from the first day of each month between 1993 and 2017. Evolution<br>and spacial distribution of SST anomalies over the North Pacific were reasonably<br>reproduced in EAKF and Projection-OI assimilation output. Two hindcast experiments<br>show that the skill of FIO-ESM v1.0 with the EAKF data assimilation scheme to predict<br>SST over the North Pacific is considerably higher than that with Projection-OI data<br>assimilation for all lead times of 1–6 months, especially in the central North Pacific where<br>the subsurface ocean temperature in the initial conditions is significantly improved by<br>EAKF data assimilation. For the Kuroshio–Oyashio extension (KOE) region, the errors<br>in the initial conditions have more rapid propagation resulting in large discrepancies<br>between simulated and observed values, which are reduced by inducing surface<br>waves into the climate model. Incorporation of realistic initial conditions and reasonable<br>physical processes into the coupled model is essential to improving seasonal prediction<br>skill. These results provide a solid basis for the development of operational seasonal<br>prediction systems for the North Pacific.</p>

Assessing Predictive Potential Associated with the MJO during the Boreal Winter

2020 ◽  
Vol 148 (12) ◽  
pp. 4957-4969
Author(s):  
Arun Kumar ◽  
Jieshun Zhu ◽  
Wanqiu Wang
Keyword(s):  
Surface Temperature ◽  
Observational Data ◽  
Prediction Skill ◽  
Meteorological Variables ◽  
Boreal Winter ◽  
Madden Julian Oscillation ◽  
Potential Predictability ◽  
Upper Limit ◽  
Prediction Systems ◽  
A Chain

AbstractIn this paper, the question of potential predictability in meteorological variables associated with skillful prediction of the Madden–Julian oscillation (MJO) during boreal winter is analyzed. The analysis is motivated by the fact that dynamical prediction systems are now capable of predicting MJO up to 30 days or earlier (measured in terms of anomaly correlation for RMM indices). Translating recent gains in MJO prediction skill and relating them back to potential for predicting meteorological variables—for example, precipitation and surface temperature—is not straightforward because of a chain of steps that go into the computation and evaluation of RMM indices. This paper assesses potential predictability in meteorological variables that could be attributed to skillful prediction of the MJO. The analysis is based on the observational data alone and assesses the upper limit of MJO-associated predictability that could be achieved.

scholarly journals Representation of Boreal Winter MJO and Its Teleconnection in a Dynamical Ensemble Seasonal Prediction System

2018 ◽  
Vol 31 (21) ◽  
pp. 8803-8818 ◽  
Author(s):  
Hyerim Kim ◽  
Myong-In Lee ◽  
Daehyun Kim ◽  
Hyun-Suk Kang ◽  
Yu-Kyung Hyun
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Seasonal Prediction ◽  
La Niña ◽  
Prediction Skill ◽  
Boreal Winter ◽  
Teleconnection Patterns ◽  
La Nina ◽  
Forecast Lead Time

This study examines the representation of the Madden–Julian oscillation (MJO) and its teleconnection in boreal winter in the Global Seasonal Forecast System, version 5 (GloSea5), using 20 years (1991–2010) of hindcast data. The sensitivity of the performance to the polarity of El Niño–Southern Oscillation (ENSO) is also investigated. The real-time multivariate MJO index of Wheeler and Hendon is used to assess MJO prediction skill while intraseasonal 200-hPa streamfunction anomalies are used to evaluate the MJO teleconnection. GloSea5 exhibits significant MJO prediction skill up to 25 days of forecast lead time. MJO prediction skill in GloSea5 also depends on initial MJO phases, with relatively enhanced (degraded) performance when the initial MJO phase is 2 or 3 (8 or 1) during the first 2 weeks of the hindcast period. GloSea5 depicts the observed MJO teleconnection patterns in the extratropics realistically up to 2 weeks albeit weaker than the observed. The ENSO-associated basic-state changes in the tropics and in the midlatitudes are reasonably represented in GloSea5. MJO prediction skill during the first 2 weeks of the hindcast is slightly higher in neutral and La Niña years than in El Niño years, especially in the upper-level zonal wind anomalies. Presumably because of the better representation of MJO-related tropical heating anomalies, the Northern Hemispheric MJO teleconnection patterns in neutral and La Niña years are considerably better than those in El Niño years.

scholarly journals NAO Influence on the MJO and its Prediction Skill in the Subseasonal-to-Seasonal Prediction Models

2021 ◽  
pp. 1-45
Author(s):  
Hai Lin ◽  
Zhiyong Huang ◽  
Harry Hendon ◽  
Gilbert Brunet
Keyword(s):  
Prediction Models ◽  
Numerical Models ◽  
Research Programme ◽  
Tropical Indian Ocean ◽  
Seasonal Prediction ◽  
Forecast Skill ◽  
Phase Changes ◽  
Prediction Skill ◽  
The North ◽  
The North Atlantic Oscillation

AbstractBased on the database of the Subseasonal to Seasonal (S2S) Prediction project of the World Weather Research Programme (WWRP) / World Climate Research Programme (WCRP), the influence of the North Atlantic Oscillation (NAO) on the Madden- Julian Oscillation (MJO) and its forecast skill is investigated. It is found that most models can capture the MJO phase changes following positive and negative NAO events. About 20 days after initialized with a positive (negative) NAO, the forecast MJO appears more frequently in phase 7 (3), which corresponds to reduced (enhanced) convection in the tropical Indian Ocean and enhanced (suppressed) convection in the western Pacific. In most S2S models the MJO prediction skill is dependent on the NAO amplitude and phase in the initial condition. A strong NAO leads to a better MJO forecast skill than a weak NAO. The MJO skill tends to be higher when the forecast starts from a negative NAO than a positive NAO. These results indicates that there is a strong Northern extratropical influence on the MJO and its forecast skill. It is important for numerical models to better represent the NAO influence to improve the simulation and prediction of the MJO.

Variability of the North Atlantic Oscillation in the 20th century

2021 ◽  
Author(s):  
André Düsterhus ◽  
Leonard Borchert ◽  
Vimal Koul ◽  
Holger Pohlmann ◽  
Sebastian Brune
Keyword(s):  
North Atlantic Oscillation ◽  
20Th Century ◽  
North Atlantic ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
Major Influence ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation ◽  
Over Time

<p>The North Atlantic Oscillation (NAO) has over the year a major influence on European weather. In many applications, being it in modern or paleo climate science, the NAO is assumed to varying in strength, but otherwise often understood as being a constant feature of the pressure system over the North Atlantic. In recent years investigations on the seasonal-predictability of the winter NAO has shown that the prediction skill is varying over time. This opens the question, why this is the case and how well models are able to represent the NAO in all its variability over the 20th century.</p><p>To investigate this further we take a look at a seasonal prediction of the NAO with the Max Planck Institute Earth System Model (MPI-ESM) seasonal prediction system, with 30 members over the 20th century. We analyse its dependence of prediction skill on various features of the NAO and the North Atlantic system, like the Atlantic Multidecadal Variability (AMV). As such we will demonstrate, that the NAO is a much less stable system over time as currently assumed and that models may not be in the position to predict its full variability appropriately.</p>

scholarly journals Seasonal Prediction of North Pacific SSTs and PDO in the NCEP CFS Hindcasts

2012 ◽  
Vol 25 (17) ◽  
pp. 5689-5710 ◽  
Author(s):  
Caihong Wen ◽  
Yan Xue ◽  
Arun Kumar
Keyword(s):  
North Pacific ◽  
Initial Conditions ◽  
Southern Oscillation ◽  
Signal To Noise Ratio ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
The North ◽  
The Impact ◽  
The North Pacific ◽  
Central North Pacific

Abstract Seasonal prediction skill of North Pacific sea surface temperature anomalies (SSTAs) and the Pacific decadal oscillation (PDO) in the NCEP Climate Forecast System (CFS) retrospective forecasts is assessed. The SST forecasts exhibit significant skills over much of the North Pacific for two seasons in advance and outperform persistence over much of the North Pacific except near the Kuroshio–Oyashia Extension. Similar to the “spring barrier” feature in the El Niño–Southern Oscillation forecasts, the central North Pacific SST experiences a faster drop in prediction skill for forecasts initialized from November to February than those from May to August. Forecasts for the PDO displayed a constant phase shift from the observation with respect to lead time. The PDO skill has a clear seasonality with highest skill for forecasts initialized in boreal spring. The impact of ENSO on the PDO and North Pacific SST prediction was investigated. The analysis revealed that seasonal prediction skill in the central North Pacific mainly results from the skillful prediction of ENSO. As a result, the PDO is more skillful than persistence at all lead times during ENSO years. On the other hand, persistence is superior to the CFS forecast during ENSO-neutral conditions owing to errors in initial conditions and deficiencies in model physics. Examination of seasonal variance and predictability (signal-to-noise ratio) further articulates the influence of ENSO on the PDO skill. The results suggest that improvement of ENSO prediction as well as reduction in model biases in the western North Pacific will lead to improvements in the PDO and North Pacific SST predictions.

scholarly journals Relating model bias and prediction skill in the equatorial Atlantic

2021 ◽  
Author(s):  
François Counillon ◽  
Noel Keenlyside ◽  
Thomas Toniazzo ◽  
Shunya Koseki ◽  
Teferi Demissie ◽  
...  
Keyword(s):  
Surface Wind ◽  
Coupled System ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
Tropical Atlantic ◽  
Equatorial Atlantic ◽  
The North ◽  
Spring Predictability Barrier ◽  
The Impact ◽  
Atlantic Niño

AbstractWe investigate the impact of large climatological biases in the tropical Atlantic on reanalysis and seasonal prediction performance using the Norwegian Climate Prediction Model (NorCPM) in a standard and an anomaly coupled configuration. Anomaly coupling corrects the climatological surface wind and sea surface temperature (SST) fields exchanged between oceanic and atmospheric models, and thereby significantly reduces the climatological model biases of precipitation and SST. NorCPM combines the Norwegian Earth system model with the ensemble Kalman filter and assimilates SST and hydrographic profiles. We perform a reanalysis for the period 1980–2010 and a set of seasonal predictions for the period 1985–2010 with both model configurations. Anomaly coupling improves the accuracy and the reliability of the reanalysis in the tropical Atlantic, because the corrected model enables a dynamical reconstruction that satisfies better the observations and their uncertainty. Anomaly coupling also enhances seasonal prediction skill in the equatorial Atlantic to the level of the best models of the North American multi-model ensemble, while the standard model is among the worst. However, anomaly coupling slightly damps the amplitude of Atlantic Niño and Niña events. The skill enhancements achieved by anomaly coupling are largest for forecast started from August and February. There is strong spring predictability barrier, with little skill in predicting conditions in June. The anomaly coupled system show some skill in predicting the secondary Atlantic Niño-II SST variability that peaks in November–December from August 1st.

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