scholarly journals Was the 2015 North Atlantic subpolar cold anomaly predictable?

2021 ◽  
pp. 1-69
Author(s):  
Elizabeth A. Maroon ◽  
Stephen G. Yeager ◽  
Gokhan Danabasoglu ◽  
Nan Rosenbloom
Keyword(s):  
North Atlantic ◽  
Initial Conditions ◽  
System Development ◽  
System Model ◽  
Decadal Prediction ◽  
Large Ensemble ◽  
Community Earth System Model ◽  
Future Prediction ◽  
Cold Anomaly ◽  
Extreme Cold

AbstractThe subpolar North Atlantic (SPNA) experienced extreme cold during 2015, an event often called the “cold blob”. The evolution of this event in the Community Earth System Model version 1 Decadal Prediction Large Ensemble (CESM1-DPLE) hindcast initialized in November 2014 is compared to observations. This CESM1-DPLE hindcast failed to predict cold conditions during 2015 despite already cold SPNA initial conditions and despite having high sea surface temperature skill in the SPNA in all other years. The goal of this paper is to understand what led to this prediction failure in order to provide insight for future decadal prediction efforts. Our analysis shows that strongly positive North Atlantic Oscillation (NAO) conditions during winter and spring 2015 likely sustained the cold blob but were not simulated in any CESM1-DPLE members. We examine the rarity of the 2015 event using the CESM1-DPLE’s uninitialized counterpart, the CESM1 Large Ensemble (CESM1-LE). Results from the CESM1-LE indicate that the exceptional state of the observed NAO in the winter of 2015 is at least part of the explanation for why this event was not encompassed in the CESM1-DPLE spread. To test another possibility — that deficiencies in the initial conditions degraded the prediction — we performed additional hindcasts using the CESM1-DPLE protocol but different initial conditions. Altering the initial conditions did not improve the simulation of the 2015 cold blob, and in some cases, degraded it. Given the difficulty of predicting this event, this case could be a useful testbed for future prediction system development.

scholarly journals Predicting Near-Term Changes in the Earth System: A Large Ensemble of Initialized Decadal Prediction Simulations Using the Community Earth System Model

2018 ◽  
Vol 99 (9) ◽  
pp. 1867-1886 ◽  
Author(s):  
S. G. Yeager ◽  
G. Danabasoglu ◽  
N. A. Rosenbloom ◽  
W. Strand ◽  
S. C. Bates ◽  
...  
Keyword(s):  
Earth System Model ◽  
Prediction Skill ◽  
System Model ◽  
Decadal Prediction ◽  
Earth System ◽  
Large Ensemble ◽  
Past Climate ◽  
Radiative Forcings ◽  
Community Earth System Model ◽  
Near Term

AbstractThe objective of near-term climate prediction is to improve our fore-knowledge, from years to a decade or more in advance, of impactful climate changes that can in general be attributed to a combination of internal and externally forced variability. Predictions initialized using observations of past climate states are tested by comparing their ability to reproduce past climate evolution with that of uninitialized simulations in which the same radiative forcings are applied. A new set of decadal prediction (DP) simulations has recently been completed using the Community Earth System Model (CESM) and is now available to the community. This new large-ensemble (LE) set (CESM-DPLE) is composed of historical simulations that are integrated forward for 10 years following initialization on 1 November of each year between 1954 and 2015. CESM-DPLE represents the “initialized” counterpart to the widely studied CESM Large Ensemble (CESM-LE); both simulation sets have 40-member ensembles, and they use identical model code and radiative forcings. Comparing CESM-DPLE to CESM-LE highlights the impacts of initialization on prediction skill and indicates that robust assessment and interpretation of DP skill may require much larger ensembles than current protocols recommend. CESM-DPLE exhibits significant and potentially useful prediction skill for a wide range of fields, regions, and time scales, and it shows widespread improvement over simpler benchmark forecasts as well as over a previous initialized system that was submitted to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The new DP system offers new capabilities that will be of interest to a broad community pursuing Earth system prediction research.

scholarly journals Low-Frequency North Atlantic Climate Variability in the Community Earth System Model Large Ensemble

2018 ◽  
Vol 31 (2) ◽  
pp. 787-813 ◽  
Author(s):  
Who M. Kim ◽  
Stephen Yeager ◽  
Ping Chang ◽  
Gokhan Danabasoglu
Keyword(s):  
Boundary Conditions ◽  
Time Scales ◽  
North Atlantic ◽  
Low Frequency ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Large Ensemble ◽  
Low Frequency Variability ◽  
Community Earth System Model

There is observational and modeling evidence that low-frequency variability in the North Atlantic has significant implications for the global climate, particularly for the climate of the Northern Hemisphere. This study explores the representation of low-frequency variability in the Atlantic region in historical large ensemble and preindustrial control simulations performed with the Community Earth System Model (CESM). Compared to available observational estimates, it is found that the simulated variability in Atlantic meridional overturning circulation (AMOC), North Atlantic sea surface temperature (NASST), and Sahel rainfall is underestimated on multidecadal time scales but comparable on interannual to decadal time scales. The weak multidecadal North Atlantic variability appears to be closely related to weaker-than-observed multidecadal variations in the simulated North Atlantic Oscillation (NAO), as the AMOC and consequent NASST variability is impacted, to a great degree, by the NAO. Possible reasons for this weak multidecadal NAO variability are explored with reference to solutions from two atmosphere-only simulations with different lower boundary conditions and vertical resolution. Both simulations consistently reveal weaker-than-observed multidecadal NAO variability despite more realistic boundary conditions and better resolved dynamics than coupled simulations. The authors thus conjecture that the weak multidecadal NAO variability in CESM is likely due to deficiencies in air–sea coupling, resulting from shortcomings in the atmospheric model or coupling details.

A large ensemble decadal prediction system with MPI-ESM

2021 ◽  
Author(s):  
Sebastian Brune ◽  
Vimal Koul ◽  
David Marcolino Nielsen ◽  
Laura Hövel ◽  
Holger Pohlmann ◽  
...  
Keyword(s):  
North Atlantic ◽  
Prediction Skill ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Decadal Prediction ◽  
Ensemble Size ◽  
Large Ensemble ◽  
Ensemble Prediction System ◽  
Atlantic Sector ◽  
Ensemble Mean

<p>Current state-of-the-art decadal ensemble prediction systems are run with an ensemble size of 10 to 40 members, their retrospective forecasts of the past are used to assess the system's prediction skill. Here, we present an attempt for a large ensemble decadal prediction system for the time period 1960-today, with an ensemble size of 80 members, based on the low resolution version of the Max Planck Institute Earth system model (MPI-ESM-LR). The ensemble is forced with CMIP6 conditions and initialized every year in November through a weakly coupled assimilation using atmospheric reanalyses via nudging and observed oceanic temperature and salinity profiles via a 16-member ensemble Kalman filter. To generate ensemble members beyond 16, we use additional physical perturbations at stratospheric height. The analysis of our large ensemble prediction system presented here aims for answering two questions: (1) How does the ensemble mean deterministic prediction skill for global and North Atlantic key climate indices change with ensemble size? (2) How well may the 80-member ensemble serve as a basis for a robust statistical analysis of probabilities of extremes in the North Atlantic sector? Preliminary results for global and regional air surface temperature show that in terms of ensemble mean ACC and full ensemble CPRSS with reference data, the 80-member ensemble leads to similar prediction skill as the 16-member ensemble. This indicates that the additional ensemble members may lead to a better sampling of the distribution of model trajectories, paving the way for a more robust statistical probabilistic analysis.</p>

scholarly journals CESM1(WACCM) Stratospheric Aerosol Geoengineering Large Ensemble Project

2018 ◽  
Vol 99 (11) ◽  
pp. 2361-2371 ◽  
Author(s):  
Simone Tilmes ◽  
Jadwiga H. Richter ◽  
Ben Kravitz ◽  
Douglas G. MacMartin ◽  
Michael J. Mills ◽  
...  
Keyword(s):  
Climate Model ◽  
Regional Climate ◽  
Stratospheric Aerosol ◽  
System Model ◽  
Large Ensemble ◽  
Tropical Precipitation ◽  
Regional Impacts ◽  
Unique Model ◽  
Community Earth System Model ◽  
And Shifts

AbstractThis paper describes the Stratospheric Aerosol Geoengineering Large Ensemble (GLENS) project, which promotes the use of a unique model dataset, performed with the Community Earth System Model, with the Whole Atmosphere Community Climate Model as its atmospheric component [CESM1(WACCM)], to investigate global and regional impacts of geoengineering. The performed simulations were designed to achieve multiple simultaneous climate goals, by strategically placing sulfur injections at four different locations in the stratosphere, unlike many earlier studies that targeted globally averaged surface temperature by placing injections in regions at or around the equator. This advanced approach reduces some of the previously found adverse effects of stratospheric aerosol geoengineering, including uneven cooling between the poles and the equator and shifts in tropical precipitation. The 20-member ensemble increases the ability to distinguish between forced changes and changes due to climate variability in global and regional climate variables in the coupled atmosphere, land, sea ice, and ocean system. We invite the broader community to perform in-depth analyses of climate-related impacts and to identify processes that lead to changes in the climate system as the result of a strategic application of stratospheric aerosol geoengineering.

scholarly journals The MJO Cycle Forcing of the North Atlantic Circulation: Intervention Experiments with the Community Earth System Model

2015 ◽  
Vol 72 (2) ◽  
pp. 660-681 ◽  
Author(s):  
David M. Straus ◽  
Erik Swenson ◽  
Cara-Lyn Lappen
Keyword(s):  
Rossby Wave ◽  
North Atlantic ◽  
System Model ◽  
Earth System ◽  
Wave Source ◽  
The North ◽  
Rossby Wave Source ◽  
Community Earth System Model ◽  
The North Atlantic ◽  
Vorticity Flux

Abstract A three-dimensional evolution of Madden–Julian oscillation (MJO) diabatic heating for October–March from satellite data is constructed: the heating propagates eastward for three cycles, modulated by the likelihood for a given MJO phase to occur on a given calendar day. This heating is added to the temperature tendencies of each member of an ensemble of 48 (1 October–31 March) simulations with the Community Earth System Model. The leading two most predictable modes of the planetary wave vertically integrated total (added plus model generated) heating capture 81% of the ensemble-mean variance and form an eastward-propagating oscillation with very high signal-to-noise ratio. The two most predictable modes of the extratropical Northern Hemisphere 200-hPa height form an oscillation, as do those of the 300-hPa height tendency due to synoptic vorticity flux convergence, the 200-hPa Rossby wave source, and the envelope transient kinetic energy. The North Atlantic Oscillation (NAO+) occurs 15–25 days after the MJO convection crosses the 90°E meridian, supported by synoptic vorticity flux convergence and a distinct pattern of Rossby wave source. The daily North Atlantic circulation anomalies are categorized into four circulation regimes with a cluster analysis. The NAO+ and NAO− are equally likely in the control model runs, but the NAO+ is 10% more likely in the model runs with heating, compared to a difference of 14% in reanalyses. The daily occurrence of the NAO+ regime in the heating ensemble shows maxima at times when the leading two optimal modes of height also indicate NAO+ but also shows maxima at other times.

scholarly journals CNOP-P-Based Parameter Sensitivity Analysis for North Atlantic Oscillation in Community Earth System Model Using Intelligence Algorithms

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Bin Mu ◽  
Jing Li ◽  
Shijin Yuan ◽  
Xiaodan Luo ◽  
Guokun Dai
Keyword(s):  
Sensitivity Analysis ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Solar Irradiance ◽  
Total Solar Irradiance ◽  
Earth System Model ◽  
System Model ◽  
Model Parameters ◽  
Earth System ◽  
Community Earth System Model

Model error, which results from model parameters, can cause the nonnegligible uncertainty in the North Atlantic Oscillation (NAO) simulation. Conditional nonlinear optimal perturbation related to parameter (CNOP-P) is a powerful approach to investigate the range of uncertainty caused by model parameters under a specific constraint. In this paper, we adopt intelligence algorithms to implement the CNOP-P method and conduct the sensitivity analysis of parameter combinations for NAO events in the Community Earth System Model (CESM). Among 28 model parameters of the atmospheric component, the most sensitive parameter combination for the NAO + consists of parameter for deep convection (cldfrc_dp1), minimum relative humidity for low stable clouds (cldfrc_rhminl), and the total solar irradiance (solar_const). As for the NAO − , the parameter set that can trigger the largest variation of the NAO index (NAOI) is comprised of the constant for evaporation of precip (cldwat_conke), characteristic adjustment time scale (hkconv_cmftau), and the total solar irradiance (solar_const). The most prominent uncertainties of the NAOI ( Δ NAOI ) caused by these two combinations achieve 2.12 for NAO + and −2.72 for NAO − , respectively. In comparison, the maximum level of the NAOI variation resulting from single parameters reaches 1.45 for NAO + and −1.70 for NAO − . It is indicated that the nonlinear impact of multiple parameters would be more intense than the single parameter. These results present factors that are closely related to NAO events and also provide the direction of optimizing model parameters. Moreover, the intelligence algorithms adopted in this work are proved to be adequate to explore the nonlinear interaction of parameters on the model simulation.

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