Eastern Pacific Intraseasonal Variability: A Predictability Perspective

2014 ◽  
Vol 27 (23) ◽  
pp. 8869-8883 ◽  
Author(s):  
J. M. Neena ◽  
Xianan Jiang ◽  
Duane Waliser ◽  
June-Yi Lee ◽  
Bin Wang
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Initial Conditions ◽  
Intraseasonal Variability ◽  
Coupled Model ◽  
Eastern Pacific ◽  
Prediction Skill ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Weather And Climate

Abstract The eastern Pacific (EPAC) warm pool is a region of strong intraseasonal variability (ISV) during boreal summer. While the EPAC ISV is known to have large-scale impacts that shape the weather and climate in the region (e.g., tropical cyclones and local monsoon), simulating the EPAC ISV is still a great challenge for present-day global weather and climate models. In the present study, the predictive skill and predictability of the EPAC ISV are explored in eight coupled model hindcasts from the Intraseasonal Variability Hindcast Experiment (ISVHE). Relative to the prediction skill for the boreal winter Madden–Julian oscillation (MJO) in the ISVHE (~15–25 days), the skill for the EPAC ISV is considerably lower in most models, with an average skill around 10 days. On the other hand, while the MJO exhibits a predictability of 35–45 days, the predictability estimate for the EPAC ISV is 20–30 days. The prediction skill was found to be higher when the hindcasts were initialized from the convective phase of the EPAC ISV as opposed to the subsidence phase. Higher prediction skill was also found to be associated with active MJO initial conditions over the western Pacific (evident in four out of eight models), signaling the importance of exploring the dynamic link between the MJO and the EPAC ISV. The results illustrate the possibility and need for improving dynamical prediction systems to facilitate more accurate and longer-lead predictions of the EPAC ISV and associated weather and short-term climate variability.

scholarly journals An Evaluation Metric for Intraseasonal Variability and its Application to CMIP3 Twentieth-Century Simulations

2010 ◽  
Vol 23 (13) ◽  
pp. 3497-3508 ◽  
Author(s):  
Prince K. Xavier ◽  
Jean-Philippe Duvel ◽  
Pascale Braconnot ◽  
Francisco J. Doblas-Reyes
Keyword(s):  
Climate Models ◽  
Intraseasonal Variability ◽  
Coupled Model ◽  
Summer Monsoon Rainfall ◽  
Boreal Summer ◽  
Phase 3 ◽  
Weather And Climate ◽  
Intercomparison Project ◽  
The Indian Ocean ◽  
Evaluation Metric

Abstract The intraseasonal variability (ISV) is an intermittent phenomenon with variable perturbation patterns. To assess the robustness of the simulated ISV in climate models, it is thus interesting to consider the distribution of perturbation patterns rather than only one average pattern. To inspect this distribution, the authors first introduce a distance that measures the similarity between two patterns. The reproducibility (realism) of the simulated intraseasonal patterns is then defined as the distribution of distances between each pattern and the average simulated (observed) pattern. A good reproducibility is required to analyze the physical source of the simulated disturbances. The realism distribution is required to estimate the proportion of simulated events that have a perturbation pattern similar to observed patterns. The median value of this realism distribution is introduced as an ISV metric. The reproducibility and realism distributions are used to evaluate boreal summer ISV of precipitations over the Indian Ocean for 19 phase 3 of the Coupled Model Intercomparison Project (CMIP3) models. The 19 models are classified in increasing ISV metric order. In agreement with previous studies, the four best ISV metrics are obtained for models having a convective closure totally or partly based on the moisture convergence. Models with high metric values (poorly realistic) tend to give (i) poorly reproducible intraseasonal patterns, (ii) rainfall perturbations poorly organized at large scales, (iii) small day-to-day variability with overly red temporal spectra, and (iv) less accurate summer monsoon rainfall distribution. This confirms that the ISV is an important link in the seamless system that connects weather and climate.

Predictability of extreme events in UFS at subseasonal time scale

2021 ◽  
Author(s):  
Christiana Stan
Keyword(s):  
Extreme Events ◽  
Time Scale ◽  
Initial Conditions ◽  
Coupled Model ◽  
Forecast Skill ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Forecast System ◽  
Cold Events ◽  
Second Category

<p>The predictability of extreme events over the continental United States (CONUS) in the Unified Forecast System (UFS) Couple Model is studied at subseasonal time scale. The benchmark runs of UFS (GFSv15), a coupled model consisting of atmospheric component (FV3GFS) with 28 km resolution and ocean (MOM6) and sea ice (CICE5) components with global 0.25° resolution, for the period April 2011–December 2017 have been assessed. The model’s month-long forecasts initiated on the first and fifteenth of each month are used to examine the predictability of extreme events in precipitation and 2m temperature. The atmospheric and ice initial conditions are from CFSR data, and the ocean initial conditions are from 3Dvar CPC. The errors in the week 1–4 predictions and the corresponding spatial correlation between model and observation over CONUS are presented. The differences in the predictability of the extreme events between the boreal summer and winter are discussed. Two categories of extreme events are evaluated: 95<sup>th</sup> and 99<sup>th</sup> percentile, respectively. The forecast skill of extreme events in the 95<sup>th</sup> percentile is higher than the forecast skill of events in the second category. The forecast skill of warm and cold events in the 95<sup>th</sup> percentile shows seasonal dependence and is higher during the boreal winter.</p>

Association of Convection with the 5-Day Rossby–Haurwitz Wave

2015 ◽  
Vol 72 (9) ◽  
pp. 3309-3321 ◽  
Author(s):  
Malcolm J. King ◽  
Matthew C. Wheeler ◽  
Todd P. Lane
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Tropical Convection ◽  
Marshall Islands ◽  
Eastern Pacific ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Tropical Andes ◽  
Quasi Biennial Oscillation ◽  
Frequency Spectra

Abstract The seasonality, regionality, and nature of the association between tropical convection and the 5-day wavenumber-1 Rossby–Haurwitz wave are examined. Spectral coherences between daily outgoing longwave radiation (OLR), a proxy for convection, and 850-hPa zonal wind over the period January 1979–February 2013 are compared for different seasons and for phases of El Niño–Southern Oscillation (ENSO) and the quasi-biennial oscillation (QBO). Increased coherence, indicating a stronger association, occurs in boreal spring and autumn, with slightly reduced coherence in boreal summer and significantly reduced coherence in boreal winter. The regionality of the association is examined using lagged-regression techniques. Significant local signals in tropical convection are found over West Africa, the tropical Andes, the eastern Pacific Ocean, and the Marshall Islands. The relative phasing between the 5-day wave wind and OLR signals is in quadrature in Africa and the Marshall Islands, in phase with easterlies over the Andes, and out of phase with easterlies over the eastern Pacific. Frequency spectra of precipitation averaged over the identified local regions reveal spectral peaks in the 4–6-day range. The phasing between the large-scale wind and local convection signals suggests that the 5-day wave is actively modulating the convection around the Americas.

The 3–4-Week MJO Prediction Skill in a GFDL Coupled Model

2015 ◽  
Vol 28 (13) ◽  
pp. 5351-5364 ◽  
Author(s):  
Baoqiang Xiang ◽  
Ming Zhao ◽  
Xianan Jiang ◽  
Shian-Jiann Lin ◽  
Tim Li ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Strong Dependence ◽  
Intraseasonal Variability ◽  
Coupled Model ◽  
Prediction Skill ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Madden Julian Oscillation ◽  
Geophysical Fluid Dynamics ◽  
Potential Predictability ◽  
Anomaly Correlation Coefficient

Abstract Based on a new version of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled model, the Madden–Julian oscillation (MJO) prediction skill in boreal wintertime (November–April) is evaluated by analyzing 11 years (2003–13) of hindcast experiments. The initial conditions are obtained by applying a simple nudging technique toward observations. Using the real-time multivariate MJO (RMM) index as a predictand, it is demonstrated that the MJO prediction skill can reach out to 27 days before the anomaly correlation coefficient (ACC) decreases to 0.5. The MJO forecast skill also shows relatively larger contrasts between target strong and weak cases (32 versus 7 days) than between initially strong and weak cases (29 versus 24 days). Meanwhile, a strong dependence on target phases is found, as opposed to relative skill independence from different initial phases. The MJO prediction skill is also shown to be about 29 days during the Dynamics of the MJO/Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011 (DYNAMO/CINDY) field campaign period. This model’s potential predictability, the upper bound of prediction skill, extends out to 42 days, revealing a considerable unutilized predictability and a great potential for improving current MJO prediction.

scholarly journals Modulation of Marine Low Clouds Associated with the Tropical Intraseasonal Variability over the Eastern Pacific

2014 ◽  
Vol 27 (14) ◽  
pp. 5560-5574 ◽  
Author(s):  
Xianan Jiang ◽  
Terence L. Kubar ◽  
Sun Wong ◽  
William S. Olson ◽  
Duane E. Waliser
Keyword(s):  
North Pacific ◽  
Climate Models ◽  
Critical Role ◽  
Inversion Layer ◽  
Intraseasonal Variability ◽  
Heat Fluxes ◽  
Eastern Pacific ◽  
Boreal Summer ◽  
Low Clouds ◽  
Low Cloud

Abstract Owing to its profound influences on global energy balance, accurate representation of low cloud variability in climate models is an urgent need for future climate projection. In the present study, marine low cloud variability on intraseasonal time scales is characterized, with a particular focus over the Pacific basin during boreal summer and its association with the dominant mode of tropical intraseasonal variability (TISV) over the eastern Pacific (EPAC) intertropical convergence zone (ITCZ). Analyses indicate that, when anomalous TISV convection is enhanced over the elongated EPAC ITCZ, reduction of low cloud fraction (LCF) is evident over a vast area of the central North Pacific. Subsequently, when the enhanced TISV convection migrates to the northern part of the EPAC warm pool, a “comma shaped” pattern of reduced LCF prevails over the subtropical North Pacific, along with a pronounced reduction of LCF present over the southeast Pacific (SEPAC). Further analyses indicate that surface latent heat fluxes and boundary heights induced by anomalous low-level circulation through temperature advection and changes of total wind speed, as well as midlevel vertical velocity associated with the EPAC TISV, could be the most prominent factors in regulating the intraseasonal variability of LCF over the North Pacific. For the SEPAC, temperature anomalies at the top of the boundary inversion layer between 850 and 800 hPa play a critical role in the local LCF intraseasonal variations. Results presented in this study provide not only improved understanding of variability of marine low clouds and the underlying physics, but also a prominent benchmark in constraining and evaluating the representation of low clouds in climate models.

scholarly journals Short Warm-Side Temperature Distribution Tails Drive Hot Spots of Warm Temperature Extreme Increases under Near-Future Warming

2018 ◽  
Vol 31 (23) ◽  
pp. 9469-9487 ◽  
Author(s):  
Paul C. Loikith ◽  
J. David Neelin ◽  
Joyce Meyerson ◽  
Jacob S. Hunter
Keyword(s):  
Hot Spots ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Global Climate Models ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Global Evaluation ◽  
Underlying Distribution ◽  
Future Warming

Regions of shorter-than-Gaussian warm-side temperature anomaly distribution tails are shown to occur in spatially coherent patterns in global reanalysis. Under such conditions, future warming may be manifested in more complex ways than if the underlying distribution were close to Gaussian. For example, under a uniform warm shift, the simplest prototype for future warming, a location with a short tail would experience a greater increase in extreme warm exceedances relative to a fixed threshold compared to if the distribution were Gaussian. The associated societal and environmental impacts make realistic representation of these short tails an important target for climate models. Global evaluation of the ability for a suite of global climate models (GCMs) contributing to phase 5 of the Coupled Model Intercomparison Project (CMIP5) suggests that most models approximately capture the principal observed coherent regions of short tails. This suggests the underlying dynamics and physics occur on scales resolved by the models, and helps build confidence in model simulations of extremes. Furthermore, most GCMs show more rapid future increases in exceedances of the historical 95th percentile in regions exhibiting short tails in the historical climate. These regions, where the ratio of exceedances projected by the GCM compared to that expected from a Gaussian sometimes exceeds 1.5, are termed hot spots. Prominent hot spots include western North America, Central America, a broad swath of northwestern Eurasia, and the Indochina Peninsula during boreal winter. During boreal summer, central and western Australia, parts of southern Africa, and portions of central South America are major hot spots.

scholarly journals Sensitivity of boreal-summer circulation and precipitation to atmospheric aerosols in selected regions – Part 1: Africa and India

2009 ◽  
Vol 27 (10) ◽  
pp. 3989-4007 ◽  
Author(s):  
Y. C. Sud ◽  
E. Wilcox ◽  
W. K.-M. Lau ◽  
G. K. Walker ◽  
X.-H. Liu ◽  
...  
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Climate Models ◽  
Initial Conditions ◽  
Regional Climate ◽  
Circulation Model ◽  
Climate Simulation ◽  
Boreal Summer ◽  
Summer Circulation ◽  
Precipitation Difference

Abstract. Version-4 of the Goddard Earth Observing System (GEOS-4) General Circulation Model (GCM) was employed to assess the influence of potential changes in aerosols on the regional circulation, ambient temperatures, and precipitation in four selected regions: India and Africa (current paper), as well as North and South America (companion paper). Ensemble-simulations were carried out with the GCM to assess the aerosol direct and indirect effects, hereafter ADE and AIE. Each simulation was started from the NCEP-analyzed initial conditions for 1 May and was integrated through May-June-July-August of each year: 1982–1987 to provide an ensemble set of six simulations. In the first set, called experiment (#1), climatological aerosols were prescribed. The next two experiments (#2 and #3) had two sets of simulations each: one with 2X and other with 1/2X the climatological aerosols over each of the four selected regions. In experiment #2, the anomaly regions were advectively restricted (AR), i.e., the large-scale prognostic fields outside the aerosol anomaly regions were prescribed while in experiment #3, the anomaly regions were advectively Interactive (AI) as is the case in a normal GCM integrations, but with the same aerosols anomalies as in experiment #2. Intercomparisons of circulation, diabatic heating, and precipitation difference fields showed large disparities among the AR and AI simulations, which raised serious questions about the proverbial AR assumption, commonly invoked in regional climate simulation studies. Consequently AI simulation mode was chosen for the subsequent studies. Two more experiments (#4 and #5) were performed in the AI mode in which ADE and AIE were activated one at a time. The results showed that ADE and AIE work in concert to make the joint influences larger than sum of each acting alone. Moreover, the ADE and AIE influences were vastly different for the Indian and Africa regions, which suggest an imperative need to include them rationally in climate models. We also found that the aerosol induced increase of tropical cirrus clouds would potentially offset any cirrus thinning that may occur due to warming in response to CO2 increase.

scholarly journals Parameter Optimization for Real-World ENSO Forecast in an Intermediate Coupled Model

2019 ◽  
Vol 147 (5) ◽  
pp. 1429-1445 ◽  
Author(s):  
Yuchu Zhao ◽  
Zhengyu Liu ◽  
Fei Zheng ◽  
Yishuai Jin
Keyword(s):  
Data Assimilation ◽  
Parameter Optimization ◽  
Real World ◽  
Climate Models ◽  
Coupled Model ◽  
Prediction Skill ◽  
Model Parameters ◽  
Real World Data ◽  
The Real ◽  
Advection Term

Abstract We performed parameter estimation in the Zebiak–Cane model for the real-world scenario using the approach of ensemble Kalman filter (EnKF) data assimilation and the observational data of sea surface temperature and wind stress analyses. With real-world data assimilation in the coupled model, our study shows that model parameters converge toward stable values. Furthermore, the new parameters improve the real-world ENSO prediction skill, with the skill improved most by the parameter of the highest climate sensitivity (gam2), which controls the strength of anomalous upwelling advection term in the SST equation. The improved prediction skill is found to be contributed mainly by the improvement in the model dynamics, and second by the improvement in the initial field. Finally, geographic-dependent parameter optimization further improves the prediction skill across all the regions. Our study suggests that parameter optimization using ensemble data assimilation may provide an effective strategy to improve climate models and their real-world climate predictions in the future.

scholarly journals Atlantic Niño/Niña Prediction Skills in NMME Models

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 803
Author(s):  
Ran Wang ◽  
Lin Chen ◽  
Tim Li ◽  
Jing-Jia Luo
Keyword(s):  
Sea Surface ◽  
Prediction Skill ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Equatorial Atlantic ◽  
Multimodel Ensemble ◽  
Spring Predictability Barrier ◽  
Anomaly Correlation Coefficient ◽  
Earth’S Climate ◽  
Atlantic Niño

The Atlantic Niño/Niña, one of the dominant interannual variability in the equatorial Atlantic, exerts prominent influence on the Earth’s climate, but its prediction skill shown previously was unsatisfactory and limited to two to three months. By diagnosing the recently released North American Multimodel Ensemble (NMME) models, we find that the Atlantic Niño/Niña prediction skills are improved, with the multi-model ensemble (MME) reaching five months. The prediction skills are season-dependent. Specifically, they show a marked dip in boreal spring, suggesting that the Atlantic Niño/Niña prediction suffers a “spring predictability barrier” like ENSO. The prediction skill is higher for Atlantic Niña than for Atlantic Niño, and better in the developing phase than in the decaying phase. The amplitude bias of the Atlantic Niño/Niña is primarily attributed to the amplitude bias in the annual cycle of the equatorial sea surface temperature (SST). The anomaly correlation coefficient scores of the Atlantic Niño/Niña, to a large extent, depend on the prediction skill of the Niño3.4 index in the preceding boreal winter, implying that the precedent ENSO may greatly affect the development of Atlantic Niño/Niña in the following boreal summer.

scholarly journals Tropical Cyclone Count Forecasting Using a Dynamical Seasonal Prediction System: Sensitivity to Improved Ocean Initialization

2011 ◽  
Vol 24 (12) ◽  
pp. 2963-2982 ◽  
Author(s):  
Andrea Alessandri ◽  
Andrea Borrelli ◽  
Silvio Gualdi ◽  
Enrico Scoccimarro ◽  
Simona Masina
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Wind Shear ◽  
Large Scale ◽  
Initial Conditions ◽  
North Pacific Ocean ◽  
Convective Available Potential Energy ◽  
Seasonal Prediction ◽  
Boreal Summer ◽  
Prediction System

Abstract This study investigates the predictability of tropical cyclone (TC) seasonal count anomalies using the Centro Euro-Mediterraneo per i Cambiamenti Climatici–Istituto Nazionale di Geofisica e Vulcanologia (CMCC-INGV) Seasonal Prediction System (SPS). To this aim, nine-member ensemble forecasts for the period 1992–2001 for two starting dates per year were performed. The skill in reproducing the observed TC counts has been evaluated after the application of a TC location and tracking detection method to the retrospective forecasts. The SPS displays good skill in predicting the observed TC count anomalies, particularly over the tropical Pacific and Atlantic Oceans. The simulated TC activity exhibits realistic geographical distribution and interannual variability, thus indicating that the model is able to reproduce the major basic mechanisms that link the TCs’ occurrence with the large-scale circulation. TC count anomalies prediction has been found to be sensitive to the subsurface assimilation in the ocean for initialization. Comparing the results with control simulations performed without assimilated initial conditions, the results indicate that the assimilation significantly improves the prediction of the TC count anomalies over the eastern North Pacific Ocean (ENP) and northern Indian Ocean (NI) during boreal summer. During the austral counterpart, significant progresses over the area surrounding Australia (AUS) and in terms of the probabilistic quality of the predictions also over the southern Indian Ocean (SI) were evidenced. The analysis shows that the improvement in the prediction of anomalous TC counts follows the enhancement in forecasting daily anomalies in sea surface temperature due to subsurface ocean initialization. Furthermore, the skill changes appear to be in part related to forecast differences in convective available potential energy (CAPE) over the ENP and the North Atlantic Ocean (ATL), in wind shear over the NI, and in both CAPE and wind shear over the SI.

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