Using a nested single-model large ensemble to assess the internal variability of the North Atlantic Oscillation and its climatic implications for Central Europe

2020 ◽  
Author(s):  
Andrea Böhnisch ◽  
Ralf Ludwig ◽  
Martin Leduc
Keyword(s):  
North Atlantic ◽  
Climate Model ◽  
Regional Climate ◽  
Internal Variability ◽  
Single Model ◽  
Large Ensemble ◽  
Central European ◽  
The North ◽  
Temperature And Precipitation ◽  
The North Atlantic Oscillation

<p>The ClimEx-project ("Climate change and hydrological extreme events"; www.climex-project.org) provides a single-model initial-condition ensemble that is unprecedented in terms of size, resolution and domain coverage: 50 members of the Canadian Earth System Model version 2 (CanESM2 Large Ensemble, 2.8° spatial resolution) are downscaled using the Canadian Regional Climate Model version 5 (CRCM5 Large Ensemble, 0.11° spatial and up to hourly temporal resolution) over two domains, Europe and northeastern North America. The high-resolution climate information serves as input for hydrological simulations to investigate the impact of internal variability and climate change on hydrometeorological extremes.</p><p>This study evaluates the downscaling of a teleconnection which affects northern hemisphere climate variability, the North Atlantic Oscillation (NAO), within the nested single-model large ensemble of the ClimEx project. The overall goal of this study is to assess whether the range of NAO internal variability is represented consistently between the driving global climate model (GCM, i.e., the CanESM2) and the nested regional climate model (RCM, i.e., the CRCM5).</p><p>The NAO pressure dipole is quantified in the CanESM2-LE; responses of mean surface air temperature and total precipitation sum to changes in the NAO index are evaluated within a Central European domain in both the CanESM2-LE and the CRCM5-LE. NAO–response relationships are expressed via Pearson correlation coefficients and the change per unit index change for historical (1981–2010) and future (2070–2099) winters.</p><p>Results show that statistically robust NAO patterns are found in the CanESM2-LE under current forcing conditions, and reproductions of the NAO flow pattern present in the CanESM2-LE produce plausible temperature and precipitation responses in the high-resolution CRCM5-LE. The NAO–response relationship is more strongly evolved in the CRCM5-LE than in the CanESM2-LE, but the inter-member spread shows no significant differences: thus internal variability expressed as inter-member spread can be seen as being represented consistently between the GCM and RCM. NAO–response relationships weaken in the future period in both the CanESM2-LE and CRCM5-LE, suggesting that the NAO influence on Central European temperature and precipitation decreases.</p><p>The results stress the advantages of a single-model ensemble regarding the evaluation of internal variability. They also strengthen the validity of the nested ensemble for further impact modelling using RCM data only, since important large-scale teleconnections present in the driving GCM propagate properly to the fine scale dynamics in the RCM.</p>

scholarly journals Using a nested single-model large ensemble to assess the internal variability of the North Atlantic Oscillation and its climatic implications for central Europe

2020 ◽  
Vol 11 (3) ◽  
pp. 617-640 ◽  
Author(s):  
Andrea Böhnisch ◽  
Ralf Ludwig ◽  
Martin Leduc
Keyword(s):  
Regional Climate Model ◽  
North Atlantic ◽  
Large Scale ◽  
Climate Model ◽  
Regional Climate ◽  
Internal Variability ◽  
Single Model ◽  
Central European ◽  
The North ◽  
The North Atlantic Oscillation

Abstract. Central European weather and climate are closely related to atmospheric mass advection triggered by the North Atlantic Oscillation (NAO), which is a relevant index for quantifying internal climate variability on multi-annual timescales. It remains unclear, however, how large-scale circulation variability affects local climate characteristics when downscaled using a regional climate model. In this study, 50 members of a single-model initial-condition large ensemble (LE) of a nested regional climate model are analyzed for a NAO–climate relationship. The overall goal of the study is to assess whether the range of NAO internal variability is represented consistently between the driving global climate model (GCM; the Canadian Earth System Model version 2 – CanESM2) and the nested regional climate model (RCM; the Canadian Regional Climate Model version 5 – CRCM5). Responses of mean surface air temperature and total precipitation to changes in the NAO index value are examined in a central European domain in both CanESM2-LE and CRCM5-LE via Pearson correlation coefficients and the change per unit index change for historical (1981–2010) and future (2070–2099) winters. Results show that statistically robust NAO patterns are found in the CanESM2-LE under current forcing conditions. NAO flow pattern reproductions in the CanESM2-LE trigger responses in the high-resolution CRCM5-LE that are comparable to reanalysis data. NAO–response relationships weaken in the future period, but their inter-member spread shows no significant change. The results stress the value of single-model ensembles for the evaluation of internal variability by pointing out the large differences of NAO–response relationships among individual members. They also strengthen the validity of the nested ensemble for further impact modeling using RCM data only, since important large-scale teleconnections present in the driving data propagate properly to the fine-scale dynamics in the RCM.

scholarly journals Using a nested single-model large ensemble to assess the internal variability of the North Atlantic Oscillation and its climatic implications for Central Europe

2019 ◽  
Author(s):  
Andrea Böhnisch ◽  
Ralf Ludwig ◽  
Martin Leduc
Keyword(s):  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Pearson Correlation ◽  
Internal Variability ◽  
Single Model ◽  
Large Ensemble ◽  
Central European ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Abstract. Central European weather and climate is closely related to atmospheric mass advection triggered by the North Atlantic Oscillation (NAO) which is a relevant index for quantifying natural variability on multi-annual time scales. It remains unclear, though, how large-scale circulation variability affects local climate characteristics when downscaled using a regional climate model. In this study, 50 members of a single-model initial-condition large ensemble (LE) (http://www.climex-project.org/) are analyzed for a climate–NAO relationship, especially its inter-member spread and its transfer from the driving model CanESM2 into the driven model CRCM5. The NAO pressure dipole is quantified in the CanESM2-LE by an extended station-based index; responses of mean surface air temperature and total precipitation to changes in the index value are determined for a Central European domain (CEUR) in both the CanESM2-LE and CRCM5-LE. NAO–response relationships are expressed via Pearson correlation coefficients (strength) and the change per unit index change for historical (1981–2010) and future (2070–2099) winters. Results show that (a) statistically robust NAO patterns are found in the CanESM2-LE under current forcing conditions and (b) impulses from the NAO in the CanESM2-LE produce correct responses in the high-resolution CRCM5-LE. Relationships weaken in the future period, but the amplitude of their inter-member spread shows no significant change. Among others, the results strengthen the validity of the climate module in the ClimEx model chain for further impact modelling and stress the importance of single-model ensembles for evaluating internal variability.

Applying machine learning for drought prediction using a large ensemble of climate simulations

2021 ◽  
Author(s):  
Elizaveta Felsche ◽  
Ralf Ludwig
Keyword(s):  
Machine Learning ◽  
Regional Climate Model ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
North Atlantic Oscillation Index ◽  
Climate Model ◽  
Regional Climate ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

<p>There is strong scientific and social interest to understand the factors leading to extreme events in order to improve the management of risks associated with hazards like droughts. Recent events like the summer 2018 drought in Germany already had severe und unexpected impacts, e.g. forest fires and crop failures; in order to increase preparedness robust prediction tools are  urgently required. In this study, machine learning methods are applied to predict the occurrence of a drought with lead times of one to three months. The approach takes into account a list of thirty atmospheric and soil variables<strong> </strong>as predictor input parameters from a single regional climate model initial condition large ensemble (CRCM5-LE). The data was produced the context of the ClimEx project by Ouranos with the Canadian Regional Climate Model (CRCM5) driven by 50 members of the Canadian Earth System Model (CanESM2) for the Bavarian and Quebec domains.</p><p>Drought occurrence was defined using the Standardized Precipitation Index. The training and test datasets were chosen from the current climatology (1955-2005) for the Munich and Lisbon subdomain within the CRCM5-LE. The best performing machine learning algorithms managed to obtain a correct classification of drought or no drought for a lead time of one month for around 60 % of the events of each class for the both domains. Explainable AI methods like feature importance and shapley values were applied to gain a better understanding of the trained algorithms. Physical variables like the North Atlantic Oscillation Index and air pressure one month before the event proved to be of high importance for the prediction. The study showed that better accuracies can be obtained for the Lisbon domain, due to the stronger influence of the North Atlantic Oscillation Index on Portugal’s climate.</p>

scholarly journals Future shift in winter streamflow modulated by internal variability of climate in southern Ontario

2019 ◽  
Author(s):  
Olivier Champagne ◽  
Altaf Arain ◽  
Martin Leduc ◽  
Paulin Coulibaly ◽  
Shawn McKenzie
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Internal Variability ◽  
Early Spring ◽  
Large Ensemble ◽  
Fluvial Systems ◽  
Southern Ontario ◽  
Temperature And Precipitation

Abstract. Fluvial systems in southern Ontario are regularly affected by widespread early-spring flood events primarily caused by rain-on-snow events. Recent studies have shown an increase in winter floods in this region due to increasing winter temperature and precipitation. Streamflow simulations are associated with uncertainties tied to the internal variability of climate. These uncertainties can be assessed using hydrological models fed by downscaled Global Climate Model Large Ensemble (GCM-LE) data. The Canadian Regional Climate Model Large Ensemble (CRCM5-LE), a dynamically downscaled version of a GCM-LE, was developed to simulate climate variability over northeastern North America under different future climate scenarios. In this study, CRCM5-LE temperature and precipitation projections under RCP 8.5 scenario were used as input in the Precipitation Runoff Modelling System (PRMS) to simulate near future (2040s) streamflow for four watersheds in southern Ontario. Model simulations show that 14 % of the ensemble project a high (low) increase of streamflow volume in January-February. Streamflow increases may be driven by rain and snowmelt modulation caused by the development of high (low) pressure anomalies in North America’s East Coast. Additionally, the streamflow may be enhanced by high pressure circulation patterns directly over the Great Lakes creating warm conditions and increasing snowmelt and rainfall/snowfall ratio (16 %). These results are important to assess the internal variability of the hydrological projections and to inform society of increased winter streamflow.

scholarly journals Trends in compound extremes of air temperature and precipitation in Eastern Europe

2021 ◽  
Author(s):  
Elena Vyshkvarkova ◽  
Olga Sukhonos
Keyword(s):  
Eastern Europe ◽  
North Atlantic Oscillation ◽  
Air Temperature ◽  
North Atlantic ◽  
Large Scale ◽  
The North ◽  
Temperature And Precipitation ◽  
Atmosphere System ◽  
Daily Data ◽  
The North Atlantic Oscillation

Abstract The spatial distribution of compound extremes of air temperature and precipitation was studied over the territory of Eastern Europe for the period 1950–2018 during winter and spring. Using daily data on air temperature and precipitation, we calculated the frequency and trends of the four indices – cold/dry, cold/wet, warm/dry and warm/wet. Also, we studying the connection between these indices and large-scale processes in the ocean-atmosphere system such as North Atlantic Oscillation, East Atlantic Oscillation and Scandinavian Oscillation. The results have shown that positive trends in the region are typical of the combinations with the temperatures above the 75th percentile, i.e., the warm extremes in winter and spring. Negative trends were obtained for the cold extremes. Statistically significant increase in the number of days with warm extremes was observed in the northern parts of the region in winter and spring. The analysis of the impacts of the large-scale processes in oceans-atmosphere system showed that the North Atlantic Oscillation index has a strong positive and statistically significant correlation with the warm indices of compound extremes in the northern part of Eastern Europe in winter, while the Scandinavian Oscillation shows the opposite picture.

scholarly journals The Linear Sensitivity of the North Atlantic Oscillation and Eddy-Driven Jet to SSTs

2019 ◽  
Vol 32 (19) ◽  
pp. 6491-6511 ◽  
Author(s):  
Hugh S. Baker ◽  
Tim Woollings ◽  
Chris E. Forest ◽  
Myles R. Allen
Keyword(s):  
Indian Ocean ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
High Sensitivity ◽  
Internal Variability ◽  
The North ◽  
The Indian Ocean ◽  
The North Atlantic ◽  
The North Atlantic Oscillation ◽  
The Impact

Abstract The North Atlantic Oscillation (NAO) and eddy-driven jet contain a forced component arising from sea surface temperature (SST) variations. Due to large amounts of internal variability, it is not trivial to determine where and to what extent SSTs force the NAO and jet. A linear statistical–dynamic method is employed with a large climate ensemble to compute the sensitivities of the winter and summer NAO and jet speed and latitude to the SSTs. Key regions of sensitivity are identified in the Indian and Pacific basins, and the North Atlantic tripole. Using the sensitivity maps and a long observational SST dataset, skillful reconstructions of the NAO and jet time series are made. The ability to skillfully forecast both the winter and summer NAO using only SST anomalies is also demonstrated. The linear approach used here allows precise attribution of model forecast signals to SSTs in particular regions. Skill comes from the Atlantic and Pacific basins on short lead times, while the Indian Ocean SSTs may contribute to the longer-term NAO trend. However, despite the region of high sensitivity in the Indian Ocean, SSTs here do not provide significant skill on interannual time scales, which highlights the limitations of the imposed SST approach. Given the impact of the NAO and jet on Northern Hemisphere weather and climate, these results provide useful information that could be used for improved attribution and forecasting.

scholarly journals Accelerating changes in ice mass within Greenland, and the ice sheet’s sensitivity to atmospheric forcing

2019 ◽  
Vol 116 (6) ◽  
pp. 1934-1939 ◽  
Author(s):  
Michael Bevis ◽  
Christopher Harig ◽  
Shfaqat A. Khan ◽  
Abel Brown ◽  
Frederik J. Simons ◽  
...  
Keyword(s):  
Global Positioning System ◽  
Climate Model ◽  
Regional Climate ◽  
Atmospheric Forcing ◽  
Surface Mass ◽  
The North ◽  
Global Positioning ◽  
The North Atlantic Oscillation ◽  
Gravity Recovery ◽  
Gps Observations

From early 2003 to mid-2013, the total mass of ice in Greenland declined at a progressively increasing rate. In mid-2013, an abrupt reversal occurred, and very little net ice loss occurred in the next 12–18 months. Gravity Recovery and Climate Experiment (GRACE) and global positioning system (GPS) observations reveal that the spatial patterns of the sustained acceleration and the abrupt deceleration in mass loss are similar. The strongest accelerations tracked the phase of the North Atlantic Oscillation (NAO). The negative phase of the NAO enhances summertime warming and insolation while reducing snowfall, especially in west Greenland, driving surface mass balance (SMB) more negative, as illustrated using the regional climate model MAR. The spatial pattern of accelerating mass changes reflects the geography of NAO-driven shifts in atmospheric forcing and the ice sheet’s sensitivity to that forcing. We infer that southwest Greenland will become a major future contributor to sea level rise.

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