A Predictability Measure Applied to Seasonal Predictions of the Arctic Oscillation

2007 ◽  
Vol 20 (18) ◽  
pp. 4733-4750 ◽  
Author(s):  
Youmin Tang ◽  
Hai Lin ◽  
Jacques Derome ◽  
Michael K. Tippett
Keyword(s):  
Confidence Level ◽  
General Circulation ◽  
Arctic Oscillation ◽  
A Priori ◽  
Circulation Model ◽  
The Arctic ◽  
Ensemble Prediction ◽  
Central Pacific ◽  
The Arctic Oscillation ◽  
Sst Anomalies

Abstract In this study, ensemble seasonal predictions of the Arctic Oscillation (AO) were conducted for 51 winters (1948–98) using a simple global atmospheric general circulation model. A means of estimating a priori the predictive skill of the AO ensemble predictions was developed based on the relative entropy (R) of information theory, which is a measure of the difference between the forecast and climatology probability density functions (PDFs). Several important issues related to the AO predictability, such as the dominant precursors of forecast skill and the degree of confidence that can be placed in an individual forecast, were addressed. It was found that R is a useful measure of the confidence that can be placed on dynamical predictions of the AO. When R is large, the prediction is likely to have a high confidence level whereas when R is small, the prediction skill is more variable. A small R is often accompanied by a relatively weak AO index. The value of R is dominated by the predicted ensemble mean. The relationship identified here, between model skills and the R of an ensemble prediction, offers a practical means of estimating the confidence level of a seasonal forecast of the AO using the dynamical model. Through an analysis of the global sea surface temperature (SST) forcing, it was found that the winter AO-related R is correlated significantly with the amplitude of the SST anomalies over the tropical central Pacific and the North Pacific during the previous October. A large value of R is usually associated with strong SST anomalies in the two regions, whereas a poor prediction with a small R indicates that SST anomalies are likely weak in these two regions and the observed AO anomaly in the specific winter is likely caused by atmospheric internal dynamics.

scholarly journals A Numerical Sensitivity Study of the Influence of Siberian Snow on the Northern Annular Mode

2012 ◽  
Vol 25 (2) ◽  
pp. 592-607 ◽  
Author(s):  
Y. Peings ◽  
D. Saint-Martin ◽  
H. Douville
Keyword(s):  
General Circulation ◽  
Arctic Oscillation ◽  
Polar Vortex ◽  
Circulation Model ◽  
Sensitivity Study ◽  
The Arctic ◽  
Annular Mode ◽  
Northern Annular Mode ◽  
Model Free ◽  
The Arctic Oscillation

Abstract The climate version of the general circulation model Action de Recherche Petite Echelle Grande Echelle (ARPEGE-Climat) is used to explore the relationship between the autumn Siberian snow and the subsequent winter northern annular mode by imposing snow anomalies over Siberia. As the model presents some biases in the representation of the polar vortex, a nudging methodology is used to obtain a more realistic but still interactive extratropical stratosphere in the model. Free and nudged sensitivity experiments are compared to discuss the dependence of the results on the northern stratosphere climatology. For each experiment, a positive snow mass anomaly imposed from October to March over Siberia leads to significant impacts on the winter atmospheric circulation in the extratropics. In line with previous studies, the model response resembles the negative phase of the Arctic Oscillation. The well-documented stratospheric pathway between snow and the Arctic Oscillation operates in the nudged experiment, while a more zonal propagation of the signal is found in the free experiment. Thus, the study provides two main findings: it supports the influence of Siberian snow on the winter extratropical circulation and highlights the importance of the northern stratosphere representation in the models to capture this teleconnection. These findings could have important implications for seasonal forecasting, as most of the operational models present biases similar to those of the ARPEGE-Climat model.

scholarly journals Seasonal Forecasting with a Simple General Circulation Model: Predictive Skill in the AO and PNA

2005 ◽  
Vol 18 (4) ◽  
pp. 597-609 ◽  
Author(s):  
Jacques Derome ◽  
Hai Lin ◽  
Gilbert Brunet
Keyword(s):  
General Circulation ◽  
Initial Conditions ◽  
Circulation Model ◽  
Atmospheric Model ◽  
The Arctic ◽  
Small Perturbations ◽  
Predictive Skill ◽  
The Pacific ◽  
The Arctic Oscillation ◽  
Environmental Prediction

Abstract A primitive equation dry atmospheric model is used to perform ensemble seasonal predictions. The predictions are done for 51 winter seasons [December–January–February (DJF)] from 1948 to 1998. Ensembles of 24 forecasts are produced, with initial conditions of 1 December plus small perturbations. The model uses a forcing field that is calculated empirically from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalyses. The forcing used to forecast a given winter is the sum of its winter climatological forcing plus an anomaly. The anomalous forcing is obtained as that of the month prior to the start of the forecast (November), which is also calculated from NCEP data. The predictions are thus made without using any information about the season to be predicted. The ensemble-mean predictions for the 51 winters are verified against the NCEP–NCAR reanalyses. Comparisons are made with the results obtained with a full GCM. It is found that the skill of the simple GCM is comparable in many ways to that of the full GCM. The skill in predicting the amplitude of the main patterns of Northern Hemisphere mean-seasonal variability, the Arctic Oscillation (AO) and the Pacific–North American (PNA) pattern is also discussed. The simple GCM has skill not only in predicting the PNA pattern during winters with strong ENSO forcing, but it also has skill in predicting the AO in winters without appreciable ENSO forcing.

scholarly journals Forcing of the Arctic Oscillation by Eurasian Snow Cover

2011 ◽  
Vol 24 (24) ◽  
pp. 6528-6539 ◽  
Author(s):  
Robert J. Allen ◽  
Charles S. Zender
Keyword(s):  
Twentieth Century ◽  
Snow Cover ◽  
General Circulation ◽  
Arctic Oscillation ◽  
General Circulation Models ◽  
The Arctic ◽  
Positive Trend ◽  
Eurasian Snow Cover ◽  
Eurasian Snow ◽  
The Arctic Oscillation

Abstract Throughout much of the latter half of the twentieth century, the dominant mode of Northern Hemisphere (NH) extratropical wintertime circulation variability—the Arctic Oscillation (AO)—exhibited a positive trend, with decreasing high-latitude sea level pressure (SLP) and increasing midlatitude SLP. General circulation models (GCMs) show that this trend is related to several factors, including North Atlantic SSTs, greenhouse gas/ozone-induced stratospheric cooling, and warming of the Indo-Pacific warm pool. Over the last approximately two decades, however, the AO has been decreasing, with 2009/10 featuring the most negative AO since 1900. Observational and idealized modeling studies suggest that snow cover, particularly over Eurasia, may be important. An observed snow–AO mechanism also exists, involving the vertical propagation of a Rossby wave train into the stratosphere, which induces a negative AO response that couples to the troposphere. Similar to other GCMs, the authors show that transient simulations with the Community Atmosphere Model, version 3 (CAM3) yield a snow–AO relationship inconsistent with observations and dissimilar AO trends. However, Eurasian snow cover and its interannual variability are significantly underestimated. When the albedo effects of snow cover are prescribed in CAM3 (CAM PS) using satellite-based snow cover fraction data, a snow–AO relationship similar to observations develops. Furthermore, the late-twentieth-century increase in the AO, and particularly the recent decrease, is reproduced by CAM PS. The authors therefore conclude that snow cover has helped force the observed AO trends and that it may play an important role in future AO trends.

scholarly journals Memory effects of Eurasian land processes cause enhanced cooling in response to sea ice loss

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Tetsu Nakamura ◽  
Koji Yamazaki ◽  
Tomonori Sato ◽  
Jinro Ukita
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Arctic Oscillation ◽  
Circulation Model ◽  
Climate Impacts ◽  
Memory Effects ◽  
Temperature Fields ◽  
The Arctic ◽  
Eurasian Continent

Abstract Amplified Arctic warming and its relevance to mid-latitude cooling in winter have been intensively studied. Observational evidence has shown strong connections between decreasing sea ice and cooling over the Siberian/East Asian regions. However, the robustness of such connections remains a matter of discussion because modeling studies have shown divergent and controversial results. Here, we report a set of general circulation model experiments specifically designed to extract memory effects of land processes that can amplify sea ice–climate impacts. The results show that sea ice–induced cooling anomalies over the Eurasian continent are memorized in the snow amount and soil temperature fields, and they reemerge in the following winters to enhance negative Arctic Oscillation-like anomalies. The contribution from this memory effect is similar in magnitude to the direct effect of sea ice loss. The results emphasize the essential role of land processes in understanding and evaluating the Arctic–mid-latitude climate linkage.

scholarly journals The ground-based FTIR network's potential for investigating the atmospheric water cycle

2010 ◽  
Vol 10 (7) ◽  
pp. 3427-3442 ◽  
Author(s):  
M. Schneider ◽  
K. Yoshimura ◽  
F. Hase ◽  
T. Blumenstock
Keyword(s):  
General Circulation ◽  
Water Cycle ◽  
Circulation Model ◽  
Hadley Circulation ◽  
Reanalysis Data ◽  
The Arctic ◽  
Atmospheric Water ◽  
Arctic Oscillation Index ◽  
Northern Atlantic ◽  
The Arctic Oscillation

Abstract. We present tropospheric H216O and HD16O/H216O vapour profiles measured by ground-based FTIR (Fourier Transform Infrared) spectrometers between 1996 and 2008 at a northern hemispheric subarctic and subtropical site (Kiruna, Northern Sweden, 68° N and Izaña, Tenerife Island, 28° N, respectively). We compare these measurements to an isotope incorporated atmospheric general circulation model (AGCM). If the model is nudged towards meteorological fields of reanalysis data the agreement is very satisfactory on time scales ranging from daily to inter-annual. Taking the Izaña and Kiruna measurements as an example we document the FTIR network's unique potential for investigating the atmospheric water cycle. At the subarctic site we find strong correlations between the FTIR data, on the one hand, and the Arctic Oscillation index and the northern Atlantic sea surface temperature, on the other hand. The Izaña FTIR measurements reveal the importance of the Hadley circulation and the Northern Atlantic Oscillation index for the subtropical middle/upper tropospheric water balance. We document where the AGCM is able to capture these complexities of the water cycle and where it fails.

Memory effects of Eurasian land processes cause enhanced cooling in response to sea ice loss

2020 ◽  
Author(s):  
Tetsu Nakamura ◽  
Koji Yamazaki ◽  
Tomonori Sato ◽  
Jinro Ukita
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Arctic Oscillation ◽  
Circulation Model ◽  
Climate Impacts ◽  
Memory Effects ◽  
Temperature Fields ◽  
The Arctic ◽  
Eurasian Continent

<p>Amplified Arctic warming and its relevance to mid-latitude cooling in winter have been intensively studied. Observational evidence has shown strong connections between decreasing sea ice and cooling over the Siberian/East Asian regions. However, the robustness of such connections remains a matter of discussion because modeling studies have shown divergent and controversial results. Here, we report a set of general circulation model experiments specifically designed to extract memory effects of land processes that can amplify sea ice–climate impacts. The results show that sea ice–induced cooling anomalies over the Eurasian continent are memorized in the snow amount and soil temperature fields, and they reemerge in the following winters to enhance negative Arctic Oscillation-like anomalies. The contribution from this memory effect is similar in magnitude to the direct effect of sea ice loss. The results emphasize the essential role of land processes in understanding and evaluating the Arctic–mid-latitude climate linkage.</p>

scholarly journals Sea ice and climate in 20th- and 21st-century simulations with a global atmosphere-ocean-ice model

2001 ◽  
Vol 33 ◽  
pp. 521-524 ◽  
Author(s):  
John W. Weatherly ◽  
Julie M. Arblaster
Keyword(s):  
Sea Ice ◽  
Greenhouse Gases ◽  
General Circulation ◽  
Circulation Model ◽  
Warming Trend ◽  
Solar Flux ◽  
Global Temperature ◽  
The Arctic ◽  
The North ◽  
The Arctic Oscillation

AbstractA global atmosphere-ocean-sea-ice general circulation model (GCM) is used in simulations of climate with greenhouse gas concentrations and sulfate aerosols prescribed from observational data (1870−1995) and future projections (1995−2100). Simulations that include the variability in solar flux from 1870 through 1995 are also performed. The variation in solar flux of ± 2 W m−2 produces a global temperature change of ± 0.2°C in the model. The more recent simulated warming trend produced by increasing greenhouse gases exceeds this solar-flux warming, although the solar flux contributes to some of the simulated present-day warm temperatures. The future increases in greenhouse gases produce an increase in global temperature of 1.2°C over 70 years, with significant decreases in Arctic ice thickness and area. The model exhibits an atmospheric pressure mode similar to the Arctic Oscillation, with different correlation indices between the North Atlantic and North Pacific pressure anomalies.

scholarly journals Snowfall distribution and its response to the Arctic Oscillation: An evaluation of HighResMIP models in the Arctic using CPR/CloudSat observations

2019 ◽  
Author(s):  
Manu Anna Thomas ◽  
Abhay Devasthale ◽  
Tristan L'Ecuyer ◽  
Shiyu Wang ◽  
Torben Koenigk ◽  
...  
Keyword(s):  
General Circulation ◽  
Arctic Oscillation ◽  
Climate Models ◽  
General Circulation Models ◽  
Specific Pattern ◽  
Radiation Budget ◽  
The Arctic ◽  
Northern Latitudes ◽  
Realistic Representation ◽  
The Arctic Oscillation

Abstract. A realistic representation of snowfall in the general circulation models (GCM) is important to accurately simulate snow cover, surface albedo, high latitude precipitation and thus the radiation budget. Hence, in this study, we evaluate snowfall in a range of climate models run at two different resolutions using the latest estimates of snowfall from CloudSat Cloud Profiling Radar over the northern latitudes. We also evaluate if the finer resolution versions of the GCMs simulate the accumulated snowfall better than their coarse resolution counterparts. As the Arctic Oscillation (AO) is the prominent mode of natural variability in the polar latitudes, the snowfall variability associated with the different phases of the AO is examined in both models and in our observational reference. We report that the statistical distributions of snowfall vary considerably between the models and CloudSat observations. While CloudSat shows an exponential distribution of snowfall, the models show a Gaussian distribution that is heavily positively skewed. As a result, the 10 and 50 percentiles, representing the light and median snowfall, are overestimated by a factor of 3 and 1.5 respectively in the models investigated here. The overestimations are strongest during the winter months compared to autumn and spring. The extreme snowfall represented by the 90 percentiles, on the other hand, is positively skewed underestimating the snowfall estimates by a factor of 2 in the models in winter compared to the CloudSat estimates. Though some regional improvements can be seen with increased spatial resolution within a particular model, it is not easy to identify a specific pattern that hold across all models. The characteristic snowfall variability associated with the positive phase of AO over Greenland Sea and central Eurasian Arctic is well captured by the models.

Sign in / Sign up

Export Citation Format

Share Document