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 Forecast Skill of the NAO in the Subseasonal to-Seasonal Prediction Models

2021 ◽  
pp. 1-50
Author(s):  
Pei-Ning Feng ◽  
Hai Lin ◽  
Jacques Derome ◽  
Timothy M. Merlis
Keyword(s):  
Prediction Models ◽  
Model Performance ◽  
Seasonal Prediction ◽  
Forecast Skill ◽  
Prediction Skill ◽  
Boreal Winter ◽  
Consistent Difference ◽  
Initial Condition ◽  
Quasi Biennial Oscillation ◽  
Project Model

AbstractThe prediction skill of the North Atlantic Oscillation (NAO) in boreal winter is assessed in the operational models of the WCRP/WWRP Subseasonal-to-Seasonal (S2S) prediction project. Model performance in representing the contribution of different processes to the NAO forecast skill is evaluated. The S2S models with relatively higher stratospheric vertical resolutions (high-top models) are in general more skillful in predicting the NAO than those models with relatively lower stratospheric resolutions (low-top models). Comparison of skill is made between different groups of forecasts based on initial condition characteristics: phase and amplitude of the NAO, easterly and westerly phases of the quasi-biennial oscillation (QBO), warm and cold phases of ENSO, and phase and amplitude of the Madden-Julia Oscillation (MJO). The forecasts with a strong NAO in the initial condition are more skillful than with a weak NAO. Those with negative NAO tend to have more skillful predictions than positive NAO. Comparisons of NAO skill between forecasts during easterly and westerly QBO and between warm and cold ENSO show no consistent difference for the S2S models. Forecasts with strong initial MJO tend to be more skillful in the NAO prediction than weak MJO. Among the eight phases of MJO in the initial condition, phases 3-4 and phase 7 have better NAO forecast skills compared with the other phases.The results of this study have implications for improving our understanding of sources of predictability of the NAO. The situation dependence of the NAO prediction skill is likely useful in identifying “ windows of opportunity” for subseasonal to seasonal predictions.

scholarly journals Seasonal statistical–dynamical prediction of the North Atlantic Oscillation by probabilistic post-processing and its evaluation

2020 ◽  
Vol 27 (1) ◽  
pp. 121-131 ◽  
Author(s):  
André Düsterhus
Keyword(s):  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
Statistical Prediction ◽  
Post Processing ◽  
Probabilistic Prediction ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Abstract. Dynamical models of various centres have shown in recent years seasonal prediction skill of the North Atlantic Oscillation (NAO). By filtering the ensemble members on the basis of statistical predictors, known as subsampling, it is possible to achieve even higher prediction skill. In this study the aim is to design a generalisation of the subsampling approach and establish it as a post-processing procedure. Instead of selecting discrete ensemble members for each year, as the subsampling approach does, the distributions of ensembles and statistical predictors are combined to create a probabilistic prediction of the winter NAO. By comparing the combined statistical–dynamical prediction with the predictions of its single components, it can be shown that it achieves similar results to the statistical prediction. At the same time it can be shown that, unlike the statistical prediction, the combined prediction has fewer years where it performs worse than the dynamical prediction. By applying the gained distributions to other meteorological variables, like geopotential height, precipitation and surface temperature, it can be shown that evaluating prediction skill depends highly on the chosen metric. Besides the common anomaly correlation (ACC) this study also presents scores based on the Earth mover's distance (EMD) and the integrated quadratic distance (IQD), which are designed to evaluate skills of probabilistic predictions. It shows that by evaluating the predictions for each year separately compared to applying a metric to all years at the same time, like correlation-based metrics, leads to different interpretations of the analysis.

scholarly journals Seasonal statistical-dynamical prediction of the North Atlantic Oscillation by probabilistic post-processing

2019 ◽  
Author(s):  
André Düsterhus
Keyword(s):  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
Statistical Prediction ◽  
Post Processing ◽  
Probabilistic Prediction ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Abstract. Dynamical models of various centres have shown in recent years seasonal prediction skill of the North Atlantic Oscillation (NAO). By filtering the ensemble members on the basis of statistical predictors, known as subsampling, it is possible to achieve even higher prediction skill. In this study the aim is to design a generalisation of the subsampling approach and establish it as a post-processing procedure. Instead of selecting discrete ensemble members for each year, as the subsampling approach does, the distributions of ensembles and statistical predictors are combined to create a probabilistic prediction of the winter NAO. By comparing the combined statistical-dynamical prediction with the predictions of its single components, it can be shown that it achieves similar results to the statistical prediction. At the same time it can be shown, that unlike the statistical prediction the combined prediction has less years where it performs worse than the dynamical prediction. By applying the gained distributions to other meteorological variables, like geopotential height, precipitation and surface temperature it can be shown that evaluating prediction skill depends highly on the chosen metric. Besides the common anomaly correlation (ACC) this study also presents a score basing on the Earth Mover’s Distance (EMD), which is designed to evaluate skill of probabilistic predictions. It shows that by evaluating the predictions separately compared to applying a metric on all years at the same time, like correlation based metrics, leads to different interpretations of the analysis.

scholarly journals How Predictable Are the Arctic and North Atlantic Oscillations? Exploring the Variability and Predictability of the Northern Hemisphere

2018 ◽  
Vol 31 (3) ◽  
pp. 997-1014 ◽  
Author(s):  
Daniela I. V. Domeisen ◽  
Gualtiero Badin ◽  
Inga M. Koszalka
Keyword(s):  
Time Scales ◽  
Northern Hemisphere ◽  
North Atlantic ◽  
Prediction Models ◽  
The Arctic ◽  
Ensemble Prediction ◽  
Atmospheric Conditions ◽  
Short Term ◽  
The North ◽  
The North Atlantic Oscillation

ABSTRACT The North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO) describe the dominant part of the variability in the Northern Hemisphere extratropical troposphere. Because of the strong connection of these patterns with surface climate, recent years have shown an increased interest and an increasing skill in forecasting them. However, it is unclear what the intrinsic limits of short-term predictability for the NAO and AO patterns are. This study compares the variability and predictability of both patterns, using a range of data and index computation methods for the daily NAO and AO indices. Small deviations from Gaussianity are found along with characteristic decorrelation time scales of around one week. In the analysis of the Lyapunov spectrum it is found that predictability is not significantly different between the AO and NAO or between reanalysis products. Differences exist, however, between the indices based on EOF analysis, which exhibit predictability time scales around 12–16 days, and the station-based indices, exhibiting a longer predictability of 18–20 days. Both of these time scales indicate predictability beyond that currently obtained in ensemble prediction models for short-term predictability. Additional longer-term predictability for these patterns may be gained through local feedbacks and remote forcing mechanisms for particular atmospheric conditions.

Influence of the QBO on MJO prediction skill in the subseasonal-to-seasonal prediction models

2019 ◽  
Vol 53 (3-4) ◽  
pp. 1681-1695 ◽  
Author(s):  
Yuna Lim ◽  
Seok-Woo Son ◽  
Andrew G. Marshall ◽  
Harry H. Hendon ◽  
Kyong-Hwan Seo
Keyword(s):  
Prediction Models ◽  
Seasonal Prediction ◽  
Prediction Skill

scholarly journals Global Assessment of Atmospheric River Prediction Skill

2018 ◽  
Vol 19 (2) ◽  
pp. 409-426 ◽  
Author(s):  
Michael J. DeFlorio ◽  
Duane E. Waliser ◽  
Bin Guan ◽  
David A. Lavers ◽  
F. Martin Ralph ◽  
...  
Keyword(s):  
North Atlantic ◽  
North Pacific ◽  
Global Assessment ◽  
El Nino ◽  
Forecast Skill ◽  
Prediction Skill ◽  
Atmospheric River ◽  
The North ◽  
The North Atlantic ◽  
The North Pacific

Abstract Atmospheric rivers (ARs) are global phenomena that transport water vapor horizontally and are associated with hydrological extremes. In this study, the Atmospheric River Skill (ATRISK) algorithm is introduced, which quantifies AR prediction skill in an object-based framework using Subseasonal to Seasonal (S2S) Project global hindcast data from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The dependence of AR forecast skill is globally characterized by season, lead time, and distance between observed and forecasted ARs. Mean values of daily AR prediction skill saturate around 7–10 days, and seasonal variations are highest over the Northern Hemispheric ocean basins, where AR prediction skill increases by 15%–20% at a 7-day lead during boreal winter relative to boreal summer. AR hit and false alarm rates are explicitly considered using relative operating characteristic (ROC) curves. This analysis reveals that AR forecast utility increases at 10-day lead over the North Pacific/western U.S. region during positive El Niño–Southern Oscillation (ENSO) conditions and at 7- and 10-day leads over the North Atlantic/U.K. region during negative Arctic Oscillation (AO) conditions and decreases at a 10-day lead over the North Pacific/western U.S. region during negative Pacific–North America (PNA) teleconnection conditions. Exceptionally large increases in AR forecast utility are found over the North Pacific/western United States at a 10-day lead during El Niño + positive PNA conditions and over the North Atlantic/United Kingdom at a 7-day lead during La Niña + negative PNA conditions. These results represent the first global assessment of AR prediction skill and highlight climate variability conditions that modulate regional AR forecast skill.

The GMAO High‐Resolution Coupled Model and Assimilation System for Seasonal Prediction

2021 ◽  
Author(s):  
Andrea Molod ◽  
Keyword(s):  
Coupled Model ◽  
Seasonal Prediction ◽  
Earth System ◽  
Aerosol Model ◽  
Earth Observing System ◽  
The North ◽  
The Earth ◽  
Weakly Coupled ◽  
The North Atlantic Oscillation ◽  
The Impact

<p>The Global Modeling and Assimilation Office (GMAO) is about to release a new version of the Goddard Earth Observing System (GEOS) Subseasonal to Seasonal prediction (S2S) system, GEOS‐S2S‐3, that represents an improvement in performance and infrastructure over the  previous system, GEOS-S2S-2. The system will be described briefly, highlighting some features unique to GEOS-S2S, such as the coupled interactive aerosol model and ensemble  perturbation strategy and size. Results are presented from forecasts and from climate  equillibrium simulations. GEOS-S2S-3 will be used to produce a long term weakly coupled reanalysis called MERRA-2 Ocean.</p><p>The climate or equillibrium state of the atmosphere and ocean shows a reduction in systematic error relative to GEOS‐S2S‐2, attributed in part to an increase in ocean resolution and to the upgrade in the glacier runoff scheme.  The forecast skill shows improved prediction  of the North Atlantic Oscillation, attributed to the increase in forecast ensemble members.  </p><p>With the release of GEOS-S2S-3 and MERRA-2 Ocean, GMAO will continue its tradition of maintaining a state‐of‐the‐art seasonal prediction system for use in evaluating the impact on seasonal and decadal forecasts of assimilating newly available satellite observations, as well as evaluating additional sources of predictability in the Earth system through the expanded coupling of the Earth system model and assimilation components.</p>

scholarly journals An Examination of Precipitation in Observations and Model Forecasts during NAME with Emphasis on the Diurnal Cycle

2007 ◽  
Vol 20 (9) ◽  
pp. 1680-1692 ◽  
Author(s):  
John E. Janowiak ◽  
Valery J. Dagostaro ◽  
Vernon E. Kousky ◽  
Robert J. Joyce
Keyword(s):  
United States ◽  
Diurnal Cycle ◽  
Prediction Models ◽  
Numerical Models ◽  
The United States ◽  
Rain Gauge ◽  
North American Monsoon ◽  
Eastern United States ◽  
Direct Evaluation ◽  
The North

Abstract Summertime rainfall over the United States and Mexico is examined and is compared with forecasts from operational numerical prediction models. In particular, the distribution of rainfall amounts is examined and the diurnal cycle of rainfall is investigated and compared with the model forecasts. This study focuses on a 35-day period (12 July–15 August 2004) that occurred amid the North American Monsoon Experiment (NAME) field campaign. Three-hour precipitation forecasts from the numerical models were validated against satellite-derived estimates of rainfall that were adjusted by daily rain gauge data to remove bias from the remotely sensed estimates. The model forecasts that are evaluated are for the 36–60-h period after the model initial run time so that the effects of updated observational data are reduced substantially and a more direct evaluation of the model precipitation parameterization can be accomplished. The main findings of this study show that the effective spatial resolution of the model-generated precipitation is considerably more coarse than the native model resolution. On a national scale, the models overforecast the frequency of rainfall events in the 1–75 mm day−1 range and underforecast heavy events (>85 mm day−1). The models also have a diurnal cycle that peaks 3–6 h earlier than is observed over portions of the eastern United States and the NAME tier-1 region. Time series and harmonic analysis are used to identify where the models perform well and poorly in characterizing the amplitude and phase of the diurnal cycle of precipitation.

scholarly journals Altered sub-seasonal predictability of Community Atmosphere Model 5 (CAM5) in CESM 1.2.1 by the choices of dynamical core

2020 ◽  
Author(s):  
Ha-Rim Kim ◽  
Baek-Min Kim ◽  
Sang-Yoon Jun ◽  
Yong-Sang Choi
Keyword(s):  
North America ◽  
Prediction Models ◽  
Initial Conditions ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
The Arctic ◽  
Late Winter ◽  
Grid System ◽  
Early Winter ◽  
Dynamical Core

Abstract. This study investigates the prediction skill of sub-seasonal prediction models that vary based on the choice of two dynamical cores: the finite volume (FV) dynamical core on a latitude-longitude grid system and the spectral element (SE) dynamical core on a cubed-sphere grid system. Recent research showed that the SE dynamical core on a uniform grid system increases parallel scalability and removes the need for polar filters for mitigating uncertainty in climate prediction, particularly for the Arctic region. However, it still remains questionable whether the choice of dynamical cores can actually yield significant changes in prediction skill. To tackle this issue, we implemented a sub-seasonal prediction model based on the Community Atmospheric Model version 5 by incorporating the above two dynamical cores with virtually the same physics schemes. Sub-seasonal prediction skills of the SE dynamical core and FV dynamical core are verified with ERA-Interim reanalysis during the early winter (November–December) and the late winter (January–February) from 2001/2002 to 2017/2018. The prediction skills of two different dynamical cores were significantly different regardless of the similar physics scheme. In the ocean, the predictability of the SE dynamical core is similar to that of the FV dynamical core, mostly because our simulation configuration imposes the same boundary and initial conditions at the surface. Notable differences in the one-month predictability between the two cores are observed for the wintertime Arctic and mid-latitudes, particularly over North America and Eurasia continents. With a one-month lead, the SE dynamical core exhibited higher predictability over North America in late winter (r ≈ 0.45 in SE, r ≈ 0.10 in FV) whereas the FV dynamical core showed relatively higher predictability in East Asia and Eurasia in early winter (r ≈ 0.15 in SE, r ≈ 0.43 in FV). Therefore, we conclude that caution is needed when selecting the dynamical cores of sub-seasonal prediction models. Partially, these differences can be ascribed to the different manifestations of Arctic-mid-latitude linkage in the two dynamical cores; the SE dynamical core captures warmer Arctic and colder mid-latitudes relatively better than the FV dynamical core.

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