scholarly journals Identifying the causes of the poor decadal climate prediction skill over the North Pacific

2012 ◽  
Vol 117 (D20) ◽  
Author(s):  
V. Guemas ◽  
F. J. Doblas-Reyes ◽  
F. Lienert ◽  
Y. Soufflet ◽  
H. Du
Keyword(s):  
North Pacific ◽  
Climate Prediction ◽  
Prediction Skill ◽  
The North ◽  
The Poor ◽  
Decadal Climate ◽  
The North Pacific ◽  
Decadal Climate Prediction

Exploring North Atlantic and North Pacific Decadal Climate Prediction Using Self-Organizing Maps

2020 ◽  
Author(s):  
Qinxue Gu ◽  
Melissa Gervais
Keyword(s):  
North Atlantic ◽  
North Pacific ◽  
Internal Variability ◽  
Climate Prediction ◽  
Self Organizing Maps ◽  
The North ◽  
Decadal Climate ◽  
The North Pacific ◽  
Decadal Climate Prediction ◽  
Self Organizing

<p>Decadal climate prediction can provide invaluable information for decisions made by government agencies and industry. Modes of internal variability of the ocean play an important role in determining the climate on decadal time scales. This study explores the possibility of using self-organizing maps (SOMs) to identify decadal climate variability with the ultimate goal of improving decadal climate prediction. SOM is applied to 11-year running mean winter Sea Surface Temperature (SST) in the North Pacific and North Atlantic within the Community Earth System Model 1850 pre-industrial simulation to identify patterns of internal variability in SSTs. Transition probability tables are calculated to identify preferred paths through the SOM with time.  Results show both persistence and preferred evolutions of SST depending on the initial SST pattern.  This method also provides a measure of the predictability of these SST patterns, with the North Atlantic being predictable at longer lead times than the North Pacific. In addition, decadal SST predictions using persistence and lagged transition probabilities are conducted.</p>

The decadal climate prediction skill with focus on the North Atlantic region

2020 ◽  
Author(s):  
Shuting Yang ◽  
Bo Christiansen
Keyword(s):  
North Atlantic ◽  
Large Degree ◽  
Climate Prediction ◽  
Prediction Skill ◽  
Lead Times ◽  
The North ◽  
Ensemble Mean ◽  
The North Atlantic ◽  
Decadal Climate ◽  
Decadal Climate Prediction

<p>The skill of the decadal climate prediction is analyzed based on recent ensemble experiments from the CMIP5 and CMIP6 decadal climate prediction projects (DCPP) and the Community Earth System Model (CESM) Large Ensemble (LENS) Project. The experiments are initialized every year at November 1 for the period of 1960-2005 in the CMIP5 DCPP experiments and 1960-2016 for the CMIP6 DCPP models as well as the CESM LENS decadal prediction. The CMIP5/6 ensemble has 10 members for each model and the CESM ensemble has 40 members. For the considered models un-initialized (historical) ensembles with the same forcings exist. The advantage of initialization is analyzed by comparing these two sets of experiments.<br><br>We find that the models agree that for lead-times between 4-10 years little effect of initialization is found except in the North Atlantic sub-polar gyre region (NASPG). This well-known result is found for all the models and is robust to temporal and spatial smoothing. In the sub-polar gyre region the ensemble mean of the forecast explains 30-40 % more of the observed variance than the ensemble mean of the historical non-initialized experiments even for lead-times of 10 years.<br><br>However, the skill in the NASPG seems to a large degree to be related to the shift towards warmer temperatures around 1996. Weak or no skill is found when the sub-periods before and after 1996 are considered. We further analyze the characteristics of other climate indicators than surface temperature as well as the NAO to understand the cause and implication of the prediction skill.</p>

Exploring North Atlantic and North Pacific Decadal Climate Prediction Using Self-Organizing Maps

2021 ◽  
Vol 34 (1) ◽  
pp. 123-141
Author(s):  
Qinxue Gu ◽  
Melissa Gervais
Keyword(s):  
Climate Variability ◽  
North Atlantic ◽  
North Pacific ◽  
Transition Probability ◽  
Internal Variability ◽  
Self Organizing Maps ◽  
The North ◽  
Decadal Climate ◽  
The North Pacific ◽  
Decadal Climate Prediction

AbstractDecadal climate prediction can provide invaluable information for decisions made by government agencies and industry. Modes of internal variability of the ocean play an important role in determining the climate on decadal time scales. This study explores the possibility of using self-organizing maps (SOMs) to identify decadal climate variability, measure theoretical decadal predictability, and conduct decadal predictions of internal climate variability within a long control simulation. SOM is applied to an 11-yr running-mean winter sea surface temperature (SST) in the North Pacific and North Atlantic Oceans within the Community Earth System Model 1850 preindustrial simulation to identify patterns of internal variability in SSTs. Transition probability tables are calculated to identify preferred paths through the SOM with time. Results show both persistence and preferred evolutions of SST depending on the initial SST pattern. This method also provides a measure of the predictability of these SST patterns, with the North Atlantic being predictable at longer lead times than the North Pacific. In addition, decadal SST predictions using persistence, a first-order Markov chain, and lagged transition probabilities are conducted. The lagged transition probability predictions have a reemergence of prediction skill around lag 15 for both domains. Although the prediction skill is very low, it does imply that the SOM has the ability to predict some aspects of the internal variability of the system beyond 10 years.

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.

Central Pacific El Niño and decadal climate change in the North Pacific Ocean

2010 ◽  
Vol 3 (11) ◽  
pp. 762-765 ◽  
Author(s):  
E. Di Lorenzo ◽  
K. M. Cobb ◽  
J. C. Furtado ◽  
N. Schneider ◽  
B. T. Anderson ◽  
...  
Keyword(s):  
Climate Change ◽  
Pacific Ocean ◽  
North Pacific ◽  
El Nino ◽  
North Pacific Ocean ◽  
Central Pacific ◽  
Central Pacific El Niño ◽  
The North ◽  
Decadal Climate ◽  
The North Pacific

scholarly journals New species of deep-sea Antipatharians from the North Pacific (Cnidaria: Anthozoa: Antipatharia), Part 2

Zootaxa ◽  
2021 ◽  
Vol 4999 (5) ◽  
pp. 401-422
Author(s):  
DENNIS M. OPRESKO ◽  
TINA N. MOLODTSOVA
Keyword(s):  
New Species ◽  
North Pacific ◽  
Deep Sea ◽  
Type Material ◽  
Poor Condition ◽  
Incertae Sedis ◽  
The North ◽  
The Poor ◽  
The Family ◽  
The North Pacific

Five new species of deep-sea antipatharian corals are described from the North Pacific primarily collected off the coast of Alaska and on adjacent seamounts. All the species are referred to the family Schizopathidae. Described as new are: Alternatipathes mirabilis, Bathypathes ptiloides, Bathypathes tiburonae, Bathypathes alaskensis, and Parantipathes pluma. Illustrations of the type material of Bathypathes patula, B. patula var. plenispina and B. tenuis are provided for comparative proposes. Bathypathes patula var. plenispina is here recognized as a species distinct from B. patula, and B. tenuis is considered incertae sedis due to the poor condition of the type material.

scholarly journals Improved decadal climate prediction in the North Atlantic using EnOI-assimilated initial condition

2017 ◽  
Vol 62 (16) ◽  
pp. 1142-1147 ◽  
Author(s):  
Min Wei ◽  
Qingquan Li ◽  
Xiaoge Xin ◽  
Wei Zhou ◽  
Zhenyu Han ◽  
...  
Keyword(s):  
North Atlantic ◽  
Climate Prediction ◽  
Initial Condition ◽  
The North ◽  
The North Atlantic ◽  
Decadal Climate ◽  
Decadal Climate Prediction

scholarly journals Improvement of Initialized Decadal Predictions over the North Pacific Ocean by Systematic Anomaly Pattern Correction

2014 ◽  
Vol 27 (13) ◽  
pp. 5148-5162 ◽  
Author(s):  
Hye-Mi Kim ◽  
Yoo-Geun Ham ◽  
Adam A. Scaife
Keyword(s):  
Pacific Ocean ◽  
Surface Temperature ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Systematic Errors ◽  
Prediction Skill ◽  
Coupled Models ◽  
The North ◽  
High Prediction ◽  
The North Pacific

The prediction skill and errors in surface temperature anomalies in initialized decadal hindcasts from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are assessed using six ocean–atmosphere coupled models initialized every year from 1961 to 2008. The initialized hindcasts show relatively high prediction skill over the regions where external forcing dominates, indicating that a large portion of the prediction skill is due to the long-term trend. After removing the linear trend, high prediction skill is shown near the centers of action of the dominant decadal climate oscillations, such as the Pacific decadal oscillation (PDO) and Atlantic multidecadal oscillation (AMO). Low prediction skill appears over the tropical and eastern North Pacific Ocean where the predicted anomaly patterns associated with the PDO are systematically different in model and observations. By statistically correcting those systematic errors using a stepwise pattern projection method (SPPM) based on the data in an independent training period, the prediction skill of sea surface temperature (SST) is greatly enhanced over the North Pacific Ocean. The SST prediction skill over the North Pacific Ocean after the SPPM error correction is as high as that over the North Atlantic Ocean. In addition, the prediction skill in a single model after correction exceeds the skill of the multimodel ensemble (MME) mean before correction, implying that the MME method is not as effective in addressing systematic errors as the SPPM correction.

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