Potential predictability of Southwest US rainfall : role of tropical and high-latitude variability

2021 ◽  
pp. 1-45
Keyword(s):  
Time Scale ◽  
Climate Model ◽  
Southern Oscillation ◽  
Seasonal Prediction ◽  
Seasonal Rainfall ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Potential Predictability ◽  
Subseasonal Variability ◽  
The Tropics

Abstract This study explores the potential predictability of Southwest US (SWUS) precipitation for the November-March season in a set of numerical experiments performed with the Whole Atmospheric Community Climate Model. In addition to the prescription of observed sea surface temperature and sea ice concentration, observed variability from the MERRA-2 reanalysis is prescribed in the tropics and/or the Arctic through nudging of wind and temperature. These experiments reveal how a perfect prediction of tropical and/or Arctic variability in the model would impact the prediction of seasonal rainfall over the SWUS, at various time scales. Imposing tropical variability improves the representation of the observed North Pacific atmospheric circulation, and the associated SWUS seasonal precipitation. This is also the case at the subseasonal time scale due to the inclusion of the Madden-Julian Oscillation (MJO) in the model. When additional nudging is applied in the Arctic, the model skill improves even further, suggesting that improving seasonal predictions in high latitudes may also benefit prediction of SWUS precipitation. An interesting finding of our study is that subseasonal variability represents a source of noise (i.e., limited predictability) for the seasonal time scale. This is because when prescribed in the model, subseasonal variability, mostly the MJO, weakens the El Niño Southern Oscillation (ENSO) teleconnection with SWUS precipitation. Such knowledge may benefit S2S and seasonal prediction as it shows that depending on the amount of subseasonal activity in the tropics on a given year, better skill may be achieved in predicting subseasonal rather than seasonal rainfall anomalies, and conversely.

scholarly journals Changes in Interannual Variability and Decadal Potential Predictability under Global Warming

2009 ◽  
Vol 22 (11) ◽  
pp. 3098-3109 ◽  
Author(s):  
G. J. Boer
Keyword(s):  
Global Warming ◽  
Standard Deviation ◽  
Time Scale ◽  
Climate Model ◽  
Decadal Variability ◽  
Climate Change Scenarios ◽  
Potential Predictability ◽  
Temperature And Precipitation ◽  
Mean Temperature ◽  
The Tropics

Abstract Global warming will result in changes in mean temperature and precipitation distributions and is also expected to affect interannual and longer time-scale internally generated variability as a consequence of changes in climate processes and feedbacks. Multimodel estimates of changes in the variability of annual mean temperature and precipitation and in the variability of decadal potential predictability are investigated based on the collection of coupled climate model simulations in the Coupled Model Intercomparison Project phase 3 (CMIP3) data archive. Pooled, multimodel standard deviations of annual mean temperature and precipitation for the unforced preindustrial control climates of the models show good resemblance to observation-based estimates. The internally generated variability of the unforced climate is compared with that of the warmer conditions for simulations with the B1 and A1B climate change scenarios with forcing stabilized at year 2100 values. The standard deviation of annual mean temperature generally decreases with global warming at extratropical latitudes, with the largest percentage decreases over the oceans and largest percentage increases in the tropics and subtropics, although the magnitudes of these increases are smaller. The standard deviation of annual mean precipitation increases almost everywhere, with larger increases in the tropics. Changes are generally larger for the more strongly forced, warmer A1B scenario than for the B1 scenario. The characterization of decadal variability changes in terms of potential predictability stems from the growing interest in producing forecasts for the next decade or several decades. The potential predictability identifies that fraction of the long time-scale variability that is, at least potentially and with enough information, predictable on decadal time scales. There is a general decrease in the internally generated decadal variability of temperature and its potential predictability in the warmer world. The decrease tends to be largest where the decadal potential predictability of the unforced control climate is largest over the high-latitude oceans. The potential predictability of precipitation is small to begin with and generally decreases further. Therefore, there is a potential decrease in the decadal potential predictability of the internally generated component in a warmer world.

Mechanisms for and Predictability of a Drastic Reduction in the Arctic Sea Ice: APPOSITE Data with Climate Model MIROC

2019 ◽  
Vol 32 (5) ◽  
pp. 1361-1380 ◽  
Author(s):  
J. Ono ◽  
H. Tatebe ◽  
Y. Komuro
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ensemble Prediction ◽  
Forecast Errors ◽  
Drastic Reduction ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Arctic Sea

Abstract The mechanisms for and predictability of a drastic reduction in the Arctic sea ice extent (SIE) are investigated using the Model for Interdisciplinary Research on Climate (MIROC) version 5.2. Here, a control (CTRL) with forcing fixed at year 2000 levels and perfect-model ensemble prediction (PRED) experiments are conducted. In CTRL, three (model years 51, 56, and 57) drastic SIE reductions occur during a 200-yr-long integration. In year 56, the sea ice moves offshore in association with a positive phase of the summer Arctic dipole anomaly (ADA) index and melts due to heat input through the increased open water area, and the SIE drastically decreases. This provides the preconditioning for the lowest SIE in year 57 when the Arctic Ocean interior is in a warm state and the spring sea ice volume has a large negative anomaly due to drastic ice reduction in the previous year. Although the ADA is one of the key mechanisms behind sea ice reduction, it does not always cause a drastic reduction. Our analysis suggests that wind direction favoring offshore ice motion is a more important factor for drastic ice reduction events. In years experiencing drastic ice reduction events, the September SIE can be skillfully predicted in PRED started from July, but not from April. This is because the forecast errors for the July sea level pressure and those for the sea ice concentration and sea ice thickness along the ice edge are large in PRED started from April.

Gridded Arctic sea ice concentration reconstruction for the first half of the 20th century based on different proxy data

2021 ◽  
Author(s):  
Vladimir Semenov ◽  
Tatiana Matveeva
Keyword(s):  
Sea Ice ◽  
20Th Century ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
The North ◽  
Arctic Sea ◽  
Ice Concentration

<p>Global warming in the recent decades has been accompanied by a rapid recline of the Arctic sea ice area most pronounced in summer (10% per decade). To understand the relative contribution of external forcing and natural variability to the modern and future sea ice area changes, it is necessary to evaluate a range of long-term variations of the Arctic sea ice area in the period before a significant increase in anthropogenic emissions of greenhouse gases into the atmosphere. Available observational data on the spatiotemporal dynamics of Arctic sea ice until 1950s are characterized by significant gaps and uncertainties. In the recent years, there have appeared several reconstructions of the early 20<sup>th</sup> century Arctic sea ice area that filled the gaps by analogue methods or utilized combined empirical data and climate model’s output. All of them resulted in a stronger that earlier believed negative sea ice area anomaly in the 1940s concurrent with the early 20<sup>th</sup> century warming (ETCW) peak. In this study, we reconstruct the monthly average gridded sea ice concentration (SIC) in the first half of the 20th century using the relationship between the spatiotemporal features of SIC variability, surface air temperature over the Northern Hemisphere extratropical continents, sea surface temperature in the North Atlantic and North Pacific, and sea level pressure. In agreement with a few previous results, our reconstructed data also show a significant negative anomaly of the Arctic sea ice area in the middle of the 20th century, however with some 15% to 30% stronger amplitude, about 1.5 million km<sup>2</sup> in September and 0.7 million km<sup>2</sup> in March. The reconstruction demonstrates a good agreement with regional Arctic sea ice area data when available and suggests that ETWC in the Arctic has been accompanied by a concurrent sea ice area decline of a magnitude that have been exceeded only in the beginning of the 21<sup>st</sup> century.</p>

Arctic Sea Ice Reemergence: The Role of Large-Scale Oceanic and Atmospheric Variability*

2015 ◽  
Vol 28 (14) ◽  
pp. 5477-5509 ◽  
Author(s):  
Mitchell Bushuk ◽  
Dimitrios Giannakis ◽  
Andrew J. Majda
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Spring Sea Ice ◽  
Phase Relationships

Abstract Arctic sea ice reemergence is a phenomenon in which spring sea ice anomalies are positively correlated with fall anomalies, despite a loss of correlation over the intervening summer months. This work employs a novel data analysis algorithm for high-dimensional multivariate datasets, coupled nonlinear Laplacian spectral analysis (NLSA), to investigate the regional and temporal aspects of this reemergence phenomenon. Coupled NLSA modes of variability of sea ice concentration (SIC), sea surface temperature (SST), and sea level pressure (SLP) are studied in the Arctic sector of a comprehensive climate model and in observations. It is found that low-dimensional families of NLSA modes are able to efficiently reproduce the prominent lagged correlation features of the raw sea ice data. In both the model and observations, these families provide an SST–sea ice reemergence mechanism, in which melt season (spring) sea ice anomalies are imprinted as SST anomalies and stored over the summer months, allowing for sea ice anomalies of the same sign to reappear in the growth season (fall). The ice anomalies of each family exhibit clear phase relationships between the Barents–Kara Seas, the Labrador Sea, and the Bering Sea, three regions that compose the majority of Arctic sea ice variability. These regional phase relationships in sea ice have a natural explanation via the SLP patterns of each family, which closely resemble the Arctic Oscillation and the Arctic dipole anomaly. These SLP patterns, along with their associated geostrophic winds and surface air temperature advection, provide a large-scale teleconnection between different regions of sea ice variability. Moreover, the SLP patterns suggest another plausible ice reemergence mechanism, via their winter-to-winter regime persistence.

scholarly journals Enhanced Seasonal Prediction of European Winter Warming following Volcanic Eruptions

2009 ◽  
Vol 22 (23) ◽  
pp. 6168-6180 ◽  
Author(s):  
A. G. Marshall ◽  
A. A. Scaife ◽  
S. Ineson
Keyword(s):  
Climate Model ◽  
Initial Conditions ◽  
Polar Vortex ◽  
Volcanic Eruptions ◽  
Seasonal Prediction ◽  
The Arctic ◽  
Extended Model ◽  
Similar Response ◽  
Volcanic Aerosol ◽  
The Impact

Abstract The impact of explosive volcanic eruptions on the atmospheric circulation at high northern latitudes is assessed in two versions of the Met Office Hadley Centre’s atmospheric climate model. The standard version of the model extends to an altitude of around 40 km, while the extended version has enhanced stratospheric resolution and reaches 85-km altitude. Seasonal hindcasts initialized on 1 December produce a strengthening of the winter polar vortex and anomalous warming over northern Europe characteristic of the positive phase of the Arctic Oscillation (AO) when forced with volcanic aerosol following the 1963 Mount Agung, 1982 El Chichón, and 1991 Mount Pinatubo eruptions, as is observed. The AO signal in the extended model is of comparable strength to that in the standard model, showing that there is little impact from both increasing the vertical resolution in the stratosphere and extending the model domain to near the mesopause. The presence of this signal in the models, however, is likely due to the persistence of the observed signal from the initial conditions, because a similar set of experiments initiated with the same conditions, but with no volcanic aerosol forcing, exhibits a similar response as the forced runs. This suggests that the model has limited fidelity in capturing the response to volcanic aerosols on its own, consistent with previous studies on the impact of volcanic forcing in long climate simulations, but does support the premise that seasonal winter forecasts are substantially improved with the inclusion of stratospheric information.

scholarly journals Seasonal Prediction Skill of Northern Extratropical Surface Temperature Driven by the Stratosphere

2017 ◽  
Vol 30 (12) ◽  
pp. 4463-4475 ◽  
Author(s):  
Liwei Jia ◽  
Xiaosong Yang ◽  
Gabriel Vecchi ◽  
Richard Gudgel ◽  
Thomas Delworth ◽  
...  
Keyword(s):  
Climate Model ◽  
Lower Troposphere ◽  
Polar Vortex ◽  
Seasonal Prediction ◽  
The Arctic ◽  
Northern Eurasia ◽  
Stratospheric Polar Vortex ◽  
Near Surface ◽  
Predictive Skill

This study explores the role of the stratosphere as a source of seasonal predictability of surface climate over Northern Hemisphere extratropics both in the observations and climate model predictions. A suite of numerical experiments, including climate simulations and retrospective forecasts, are set up to isolate the role of the stratosphere in seasonal predictive skill of extratropical near-surface land temperature. It is shown that most of the lead-0-month spring predictive skill of land temperature over extratropics, particularly over northern Eurasia, stems from stratospheric initialization. It is further revealed that this predictive skill of extratropical land temperature arises from skillful prediction of the Arctic Oscillation (AO). The dynamical connection between the stratosphere and troposphere is also demonstrated by the significant correlation between the stratospheric polar vortex and sea level pressure anomalies, as well as the migration of the stratospheric zonal wind anomalies to the lower troposphere.

scholarly journals The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2)

2020 ◽  
Vol 35 (4) ◽  
pp. 1317-1343 ◽  
Author(s):  
Hai Lin ◽  
William J. Merryfield ◽  
Ryan Muncaster ◽  
Gregory C. Smith ◽  
Marko Markovic ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Southern Oscillation ◽  
Weather Prediction ◽  
Coupled Model ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Boreal Winter ◽  
Prediction System ◽  
Sea Ice Concentration

AbstractThe second version of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2) was implemented operationally at Environment and Climate Change Canada (ECCC) in July 2019. Like its predecessors, CanSIPSv2 applies a multimodel ensemble approach with two coupled atmosphere–ocean models, CanCM4i and GEM-NEMO. While CanCM4i is a climate model, which is upgraded from CanCM4 of the previous CanSIPSv1 with improved sea ice initialization, GEM-NEMO is a newly developed numerical weather prediction (NWP)-based global atmosphere–ocean coupled model. In this paper, CanSIPSv2 is introduced, and its performance is assessed based on the reforecast of 30 years from 1981 to 2010, with 10 ensemble members of 12-month integrations for each model. Ensemble seasonal forecast skill of 2-m air temperature, 500-hPa geopotential height, precipitation rate, sea surface temperature, and sea ice concentration is assessed. Verification is also performed for the Niño-3.4, the Pacific–North American pattern (PNA), the North Atlantic Oscillation (NAO), and the Madden–Julian oscillation (MJO) indices. It is found that CanSIPSv2 outperforms the previous CanSIPSv1 system in many aspects. Atmospheric teleconnections associated with the El Niño–Southern Oscillation (ENSO) are reasonably well captured by the two CanSIPSv2 models, and a large part of the seasonal forecast skill in boreal winter can be attributed to the ENSO impact. The two models are also able to simulate the Northern Hemisphere teleconnection associated with the tropical MJO, which likely provides another source of skill on the subseasonal to seasonal time scale.

scholarly journals Seasonal prediction and predictability of regional Antarctic sea ice

2021 ◽  
pp. 1-68
Author(s):  
Mitchell Bushuk ◽  
Michael Winton ◽  
F. Alexander Haumann ◽  
Thomas Delworth ◽  
Feiyu Lu ◽  
...  
Keyword(s):  
Sea Ice ◽  
Heat Content ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
The Arctic ◽  
Ice Thickness ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice

AbstractCompared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen and Bellingshausen, Indian, and West Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently-developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal timescales.

scholarly journals Long Time-Scale Teleconnection Patterns in the Northern Atlantic and Pacific

2012 ◽  
Vol 25 (1) ◽  
pp. 414-422
Author(s):  
George J. Boer
Keyword(s):  
Time Scale ◽  
Climate Model ◽  
Potential Predictability ◽  
Teleconnection Patterns ◽  
Statistical Robustness ◽  
Model Control ◽  
Long Time ◽  
Total Variability ◽  
Northern Atlantic ◽  
Hadley Centre

Abstract Long time-scale teleconnection patterns, with common features in both the northern Atlantic and Pacific regions, are identified. The teleconnection patterns arise in an investigation of the internally generated variability in a multimodel ensemble of coupled climate model control simulations. The large amount of data involved offers statistical robustness and the benefits of combining results across models. Maxima of decadal potential predictability identify regions where long time-scale variability is an appreciable fraction of the total variability and serve as index regions for the teleconnection analysis. Annual, 5-yr, and decadal mean temperatures over these Atlantic and Pacific index regions are correlated with corresponding temperatures and precipitation rates over the globe. The resulting teleconnection patterns are reasonably similar despite the different long time-scale variability mechanisms thought to exist in the two ocean basins. Although lacking statistical robustness, some aspects of the temperature teleconnection patterns are obtained based on the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) dataset. The similarity of the teleconnection patterns in the two northern ocean regions suggests that common variability mechanisms may be involved.

The Impact of Poleward Moisture and Sensible Heat Flux on Arctic Winter Sea Ice Variability*

2015 ◽  
Vol 28 (13) ◽  
pp. 5030-5040 ◽  
Author(s):  
Hyo-Seok Park ◽  
Sukyoung Lee ◽  
Seok-Woo Son ◽  
Steven B. Feldstein ◽  
Yu Kosaka
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Model Simulation ◽  
Early Stage ◽  
Sensible Heat Flux ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Surface Warming ◽  
The Impact

Abstract The surface warming in recent decades has been most rapid in the Arctic, especially during the winter. Here, by utilizing global reanalysis and satellite datasets, it is shown that the northward flux of moisture into the Arctic during the winter strengthens the downward infrared radiation (IR) by 30–40 W m−2 over 1–2 weeks. This is followed by a decline of up to 10% in sea ice concentration over the Greenland, Barents, and Kara Seas. A climate model simulation indicates that the wind-induced sea ice drift leads the decline of sea ice thickness during the early stage of the strong downward IR events, but that within one week the cumulative downward IR effect appears to be dominant. Further analysis indicates that strong downward IR events are preceded several days earlier by enhanced convection over the tropical Indian and western Pacific Oceans. This finding suggests that sea ice predictions can benefit from an improved understanding of tropical convection and ensuing planetary wave dynamics.

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