scholarly journals Disagreement between predictions of the future behavior of the Arctic oscillation as simulated in Two different climate models: Implications for global warming

2000 ◽  
Vol 27 (12) ◽  
pp. 1755-1758 ◽  
Author(s):  
Eduardo Zorita ◽  
Fidel González-Rouco
Keyword(s):  
Global Warming ◽  
Arctic Oscillation ◽  
Climate Models ◽  
The Arctic ◽  
Future Behavior ◽  
The Future ◽  
The Arctic Oscillation

The Arctic Oscillation in Coupled Climate Models

2008 ◽  
Vol 51 (2) ◽  
pp. 223-239 ◽  
Author(s):  
Xiao-Ge XIN ◽  
Tian-Jun ZHOU ◽  
Ru-Cong YU
Keyword(s):  
Arctic Oscillation ◽  
Climate Models ◽  
The Arctic ◽  
Coupled Climate Models ◽  
The Arctic Oscillation

Internal variability of the Arctic Oscillation and its projections

2020 ◽  
Author(s):  
Annalisa Cherchi ◽  
Paolo Oliveri ◽  
Aarnout van Delden
Keyword(s):  
Northern Hemisphere ◽  
Arctic Oscillation ◽  
Internal Variability ◽  
The Arctic ◽  
Positive Phase ◽  
Cmip5 Models ◽  
Modes Of Variability ◽  
The Future ◽  
The Arctic Oscillation

<p>The Arctic Oscillation (AO) is one of the main modes of variability of the Northern Hemisphere winter, also referred as Northern Annular Mode (NAM). The positive phase of the AO is characterized by warming/cooling over Northern Eurasia and the United States and cooling over Canada, especially over eastern Canada. Its positive phase is also characterized by very dry conditions over the Mediterranean and wet conditions over Northern Europe. A positive trend of the AO is observed for the period 1951-2011 and it is captured in CMIP5 models only when GHG-only forcing are included. In CMIP5 models the change expected is mostly mitigated by the effects of the aerosols. When considering AR5 scenarios, the AO is projected to become more positive in the future, though with a large spread among the models.</p><p>Overall the spread in the representation of the AO variability and trend is large also in experiments with present-day conditions, likely associated with the large internal variability. Unique tools to identify and measure the role of the internal variability in the model representation of the large-scale modes of variability are large ensembles where multiple members are built with different initial conditions.</p><p>Here we use the NCAR Community Model Large Ensemble (CESM-LE) composing the historical period (1920-2005) to the future (2006-2100) in a RCP8.5 scenario to measure the role of the internal variability in shaping AO variability and changes. Potential predictability of the AO index is quantified in the historical and future periods, evidencing how the members spread remain large without specific trends in these characteristics. Preliminary results indicate that the internal variability has large influence on the AO changes and related implications for the Northern Hemisphere climate.</p>

scholarly journals Response of Arctic Freshwater to the Arctic Oscillation in Coupled Climate Models

2020 ◽  
Vol 33 (7) ◽  
pp. 2533-2555 ◽  
Author(s):  
Sam B. Cornish ◽  
Yavor Kostov ◽  
Helen L. Johnson ◽  
Camille Lique
Keyword(s):  
Arctic Ocean ◽  
Linear Response ◽  
Arctic Oscillation ◽  
Climate Models ◽  
Coupled Model ◽  
Step Change ◽  
The Arctic ◽  
Response Functions ◽  
The Arctic Ocean ◽  
The Arctic Oscillation

AbstractThe freshwater content (FWC) of the Arctic Ocean is intimately linked to the stratification—a physical characteristic of the Arctic Ocean with wide relevance for climate and biology. Here, we explore the relationship between atmospheric circulation and Arctic FWC across 12 different control-run simulations from phase 5 of the Coupled Model Intercomparison Project. Using multiple lagged regression, we seek to isolate the linear response of Arctic FWC to a step change in the strength of the Arctic Oscillation (AO) as well as the second and third orthogonal modes of SLP variability over the Arctic domain. There is broad agreement among models that a step change to a more anticyclonic AO leads to an increase in Arctic FWC, with an e-folding time scale of 5–10 yr. However, models differ widely in the degree to which a linear response to SLP variability can explain FWC changes. Although the mean states, time scales, and magnitudes of FWC variability may be broadly similar, the physical origins of variability are highly inconsistent among models. We perform a robustness test that incorporates a Monte Carlo approach to determine which response functions are most likely to represent causal, physical relationships within the models and which are artifacts of regression. Convolution with SLP reanalysis data shows that the four most robust response functions have some skill at reproducing observed accumulation of FWC during the late 1990s and 2000s, consistent with the idea that this change was largely wind driven.

scholarly journals Influence of the Arctic Oscillation on the vertical distribution of clouds as observed by the A-Train constellation of satellites

2012 ◽  
Vol 12 (21) ◽  
pp. 10535-10544 ◽  
Author(s):  
A. Devasthale ◽  
M. Tjernström ◽  
M. Caian ◽  
M. A. Thomas ◽  
B. H. Kahn ◽  
...  
Keyword(s):  
Vertical Distribution ◽  
Large Scale ◽  
Arctic Oscillation ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Dominant Mode ◽  
The Arctic ◽  
Dipole Structure ◽  
The Arctic Oscillation

Abstract. The main purpose of this study is to investigate the influence of the Arctic Oscillation (AO), the dominant mode of natural variability over the northerly high latitudes, on the spatial (horizontal and vertical) distribution of clouds in the Arctic. To that end, we use a suite of sensors onboard NASA's A-Train satellites that provide accurate observations of the distribution of clouds along with information on atmospheric thermodynamics. Data from three independent sensors are used (AQUA-AIRS, CALIOP-CALIPSO and CPR-CloudSat) covering two time periods (winter half years, November through March, of 2002–2011 and 2006–2011, respectively) along with data from the ERA-Interim reanalysis. We show that the zonal vertical distribution of cloud fraction anomalies averaged over 67–82° N to a first approximation follows a dipole structure (referred to as "Greenland cloud dipole anomaly", GCDA), such that during the positive phase of the AO, positive and negative cloud anomalies are observed eastwards and westward of Greenland respectively, while the opposite is true for the negative phase of AO. By investigating the concurrent meteorological conditions (temperature, humidity and winds), we show that differences in the meridional energy and moisture transport during the positive and negative phases of the AO and the associated thermodynamics are responsible for the conditions that are conducive for the formation of this dipole structure. All three satellite sensors broadly observe this large-scale GCDA despite differences in their sensitivities, spatio-temporal and vertical resolutions, and the available lengths of data records, indicating the robustness of the results. The present study also provides a compelling case to carry out process-based evaluation of global and regional climate models.

scholarly journals Influence of the Arctic Oscillation on the vertical distribution of clouds as observed by the A-Train constellation of satellites

2012 ◽  
Vol 12 (4) ◽  
pp. 10305-10329 ◽  
Author(s):  
A. Devasthale ◽  
M. Tjernström ◽  
M. Caian ◽  
M. A. Thomas ◽  
B. H. Kahn ◽  
...  
Keyword(s):  
Vertical Distribution ◽  
Large Scale ◽  
Arctic Oscillation ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Dominant Mode ◽  
The Arctic ◽  
Dipole Structure ◽  
The Arctic Oscillation

Abstract. The main purpose of this study is to investigate the influence of the Arctic Oscillation (AO), the dominant mode of natural variability over the northerly high latitudes, on the spatial (horizontal and vertical) distribution of clouds in the Arctic. To that end, we use a suite of sensors onboard NASA's A-Train satellites that provide accurate observations of the distribution of clouds along with information on atmospheric thermodynamics. Data from three independent sensors are used (AIRS-AQUA, CALIOP-CALIPSO and CPR-CloudSAT) covering two time periods (winter half years of 2002–2011 and 2006–2011, respectively) along with data from the ERA-Interim reanalysis. We show that the zonal vertical distribution of cloud fraction anomalies averaged over 67° N–82°; N to a first approximation follows a dipole structure (referred to as "Greenland cloud dipole anomaly", GCDA), such that during the positive phase of the AO, positive and negative cloud anomalies are observed eastwards and westward of Greenland, respectively, while the opposite is true for the negative phase of AO. By investigating the concurrent meteorological conditions (temperature, humidity and winds), we show that differences in the meridional energy and moisture transport during the positive and negative phases of the AO and the associated thermodynamics are responsible for the conditions that are conducive for the formation of this dipole structure. All three satellite sensors broadly observe this large-scale GCDA despite differences in their sensitivities, spatio-temporal and vertical resolutions, and the available lengths of data records, indicating the robustness of the results. The present study also provides a compelling case to carry out process-based evaluation of global and regional climate models.

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.

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

2019 ◽  
Vol 12 (8) ◽  
pp. 3759-3772 ◽  
Author(s):  
Manu Anna Thomas ◽  
Abhay Devasthale ◽  
Tristan L'Ecuyer ◽  
Shiyu Wang ◽  
Torben Koenigk ◽  
...  
Keyword(s):  
General Circulation ◽  
Arctic Oscillation ◽  
Climate Models ◽  
Global Climate ◽  
General Circulation Models ◽  
Specific Pattern ◽  
Surface Radiation ◽  
Radiation Budget ◽  
The Arctic ◽  
The Arctic Oscillation

Abstract. A realistic representation of snowfall in general circulation models (GCMs) of global climate is important to accurately simulate snow cover, surface albedo, high-latitude precipitation and thus the surface radiation budget. Hence, in this study, we evaluate snowfall in a range of climate models run at two different resolutions by comparing to the latest estimates of snowfall from the CloudSat Cloud Profiling Radar over the northern latitudes. We also evaluate whether 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 differ 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 10th and 50th percentiles, representing the light and median snowfall, are overestimated by up to factors 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 90th percentiles, on the other hand, is positively skewed, underestimating the snowfall estimates by up to 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 holds 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