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 Causes and Evolution of Winter Polynyas over North of Greenland

2021 ◽  
Author(s):  
Younjoo J. Lee ◽  
Wieslaw Maslowski ◽  
John J. Cassano ◽  
Jaclyn Clement Kinney ◽  
Anthony P. Craig ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
Open Water ◽  
Internal Variability ◽  
System Model ◽  
Sea Ice Thickness ◽  
Study Region ◽  
Community Earth System Model

Abstract. During the 42-year period (1979–2020) of satellite measurements, only three winter polynyas have ever been observed north of Greenland and they all occurred in the last decade, i.e. February of 2011, 2017 and 2018. The 2018 polynya was unparalleled by its magnitude and duration compared to the two previous events. Combined with the limited weather station and remotely-sensed sea ice data, a fully-coupled Regional Arctic System Model (RASM) hindcast simulation was utilized to examine the causality and evolution of these recent extreme events. We found that neither the accompanying anomalous warm surface air intrusion nor the ocean below had an impact on the development of these winter open water episodes in the study region (i.e., no significant ice melting). Instead, the extreme atmospheric wind forcing resulted in greater sea ice deformation and transport offshore, accounting for the majority of sea ice loss. Our analysis suggests that strong southerly winds (i.e., northward wind with speeds of greater than 10 m/s) blowing persistently for at least 2 days or more, were required over the study region to mechanically redistribute some of the thickest sea ice out of the region and thus to create open water areas (a latent heat polynya). In order to assess the role of internal variability versus external forcing of such events, we additionally simulated and examined results from two RASM ensembles forced with output from the Community Earth System Model (CESM) Decadal Prediction Large Ensemble (DPLE) simulations. Out of 100 winters in each of the two ensembles, initialized 30 years apart, one in December 1985 and another in December 2015, respectively, 17 and 14 winter polynyas were produced over north of Greenland. The frequency of polynya occurrence and no apparent sensitivity to the initial sea ice thickness in the study area point to internal variability of atmospheric forcing as a dominant cause of winter polynyas north of Greenland. We assert that dynamical downscaling using a high-resolution regional climate model offers a robust tool for process-level examination in space and time, synthesis with limited observations and probabilistic forecast of Arctic events, such as the ones being investigated here and elsewhere.

scholarly journals Regional Climate Simulations With the Community Earth System Model

2018 ◽  
Vol 10 (6) ◽  
pp. 1245-1265 ◽  
Author(s):  
A. Gettelman ◽  
P. Callaghan ◽  
V. E. Larson ◽  
C. M. Zarzycki ◽  
J. T. Bacmeister ◽  
...  
Keyword(s):  
Regional Climate ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Climate Simulations ◽  
Community Earth System Model

scholarly journals A comprehensive assessment of tropical stratospheric upwelling in the specified dynamics Community Earth System Model 1.2.2 – Whole Atmosphere Community Climate Model (CESM (WACCM))

2020 ◽  
Vol 13 (2) ◽  
pp. 717-734 ◽  
Author(s):  
Nicholas A. Davis ◽  
Sean M. Davis ◽  
Robert W. Portmann ◽  
Eric Ray ◽  
Karen H. Rosenlof ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Earth System Model ◽  
System Model ◽  
Trace Gas ◽  
Earth System ◽  
Mean Meridional Circulation ◽  
Community Earth System Model ◽  
The Mean ◽  
Wave Momentum

Abstract. Specified dynamics (SD) schemes relax the circulation in climate models toward a reference meteorology to simulate historical variability. These simulations are widely used to isolate the dynamical contributions to variability and trends in trace gas species. However, it is not clear if trends in the stratospheric overturning circulation are properly reproduced by SD schemes. This study assesses numerous SD schemes and modeling choices in the Community Earth System Model (CESM) Whole Atmosphere Chemistry Climate Model (WACCM) to determine a set of best practices for reproducing interannual variability and trends in tropical stratospheric upwelling estimated by reanalyses. Nudging toward the reanalysis meteorology as is typically done in SD simulations does not accurately reproduce lower-stratospheric upwelling trends present in the underlying reanalysis. In contrast, nudging to anomalies from the climatological winds or anomalies from the zonal-mean winds and temperatures better reproduces trends in lower-stratospheric upwelling, possibly because these schemes do not disrupt WACCM's climatology. None of the schemes substantially alter the structure of upwelling trends – instead, they make the trends more or less AMIP-like. An SD scheme's performance in simulating the acceleration of the shallow branch of the mean meridional circulation from 1980 to 2017 hinges on its ability to simulate the downward shift of subtropical lower-stratospheric wave momentum forcing. Key to this is not nudging the zonal-mean temperature field. Gravity wave momentum forcing, which drives a substantial fraction of the upwelling in WACCM, cannot be constrained by nudging and presents an upper limit on the performance of these schemes.

scholarly journals Representation of the Community Earth System Model (CESM1) CAM4-chem within the Chemistry-ClimateModel Initiative (CCMI)

2016 ◽  
Author(s):  
S. Tilmes ◽  
J.-F. Lamarque ◽  
L. K. Emmons ◽  
D. E. Kinnison ◽  
D. Marsh ◽  
...  
Keyword(s):  
Climate Model ◽  
Evaluation Studies ◽  
Surface Ozone ◽  
Atmospheric Model ◽  
Horizontal Resolution ◽  
Earth System Model ◽  
System Model ◽  
Good Representation ◽  
Earth System ◽  
Community Earth System Model

Abstract. The Community Earth System Model, CESM1 CAM4-chem has been used to perform the Chemistry Climate Model Initiative (CCMI) reference and sensitivity simulations. In this model, the Community Atmospheric Model Version 4 (CAM4) is fully coupled to tropospheric and stratospheric chemistry. Details and specifics of each configuration, including new developments and improvements are described. CESM1 CAM4-chem is a low top model that reaches up to approximately 40 km and uses a horizontal resolution of 1.9° latitude and 2.5° longitude. For the specified dynamics experiments, the model is nudged to Modern-Era Retrospective Analysis For Research And Applications (MERRA) reanalysis. We summarize the performance of the three reference simulations suggested by CCMI, with a focus on the observed period. Comparisons with elected datasets are employed to demonstrate the general performance of the model. We highlight new datasets that are suited for multi-model evaluation studies. Most important improvements of the model are the treatment of stratospheric aerosols and the corresponding adjustments for radiation and optics, the updated chemistry scheme including improved polar chemistry and stratospheric dynamics, and improved dry deposition rates. These updates lead to a very good representation of tropospheric ozone within 20 % of values from available observations for most regions. In particular, the trend and magnitude of surface ozone has been much improved compared to earlier versions of the model. Furthermore, stratospheric column ozone of the Southern Hemisphere in winter and spring is reasonably well represented. All experiments still underestimate CO most significantly in Northern Hemisphere spring and show a significant underestimation of hydrocarbons based on surface observations.

scholarly journals Applying machine learning for drought prediction using data from a large ensemble of climate simulations

2021 ◽  
Author(s):  
Elizaveta Felsche ◽  
Ralf Ludwig
Keyword(s):  
Machine Learning ◽  
Lead Time ◽  
Climate Model ◽  
Regional Climate ◽  
Standardized Precipitation Index ◽  
Machine Learning Algorithms ◽  
Large Ensemble ◽  
The North ◽  
Soil Variables ◽  
Drought Prediction

Abstract. 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. In this study, artificial neural networks are applied to predict the occurrence of a drought in two contrasting European domains, Munich and Lisbon, with a lead time of one month. The approach takes into account a list of 30 atmospheric and soil variables as input parameters from a single-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). Drought occurrence was defined using the Standardized Precipitation Index. 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 55–60 % of the events of each class for both domains. Explainable AI methods like SHapley Additive exPlanations (SHAP) were applied to gain a better understanding of the trained algorithms. 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 seasonality has a high influence on goodness of drought prediction, especially for the Lisbon domain.

scholarly journals New Version of Popular Climate Model Released

Eos ◽  
2018 ◽  
Vol 99 ◽  
Author(s):  
Lucas Joel
Keyword(s):  
Climate Model ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Community Earth System Model

After spending months addressing a big glitch, researchers released the second version of the Community Earth System Model.

scholarly journals Internal Model Variability of the Regional Coupled System Model GCOAST-AHOI

Atmosphere ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 227 ◽  
Author(s):  
Ha Thi Minh Ho-Hagemann ◽  
Stefan Hagemann ◽  
Sebastian Grayek ◽  
Ronny Petrik ◽  
Burkhardt Rockel ◽  
...  
Keyword(s):  
Internal Model ◽  
Climate Model ◽  
Extreme Event ◽  
Regional Climate ◽  
Coupled System ◽  
Internal Variability ◽  
Atmospheric Model ◽  
Ocean Model ◽  
System Model ◽  
Model Variability

Simulations of a Regional Climate Model (RCM) driven by identical lateral boundary conditions but initialized at different times exhibit the phenomenon of so-called internal model variability (or in short, Internal Variability—IV), which is defined as the inter-member spread between members in an ensemble of simulations. Our study investigates the effects of air-sea coupling on IV of the regional atmospheric model COSMO-CLM (CCLM) of the new regional coupled system model GCOAST-AHOI (Geesthacht Coupled cOAstal model SysTem: Atmosphere, Hydrology, Ocean and Sea Ice). We specifically address physical processes parameterized in CCLM, which may cause a large IV during an extreme event, and where this IV is affected by the air-sea coupling. Two six-member ensemble simulations were conducted with GCOAST-AHOI and the stand-alone CCLM (CCLM_ctr) for a period of 1 September–31 December 2013 over Europe. IV is expressed by spreads within the two sets of ensembles. Analyses focus on specific events during this period, especially on the storm Christian occurring from 27 to 29 October 2013 in northern Europe. Results show that simulations of CCLM_ctr vary largely amongst ensemble members during the storm. By analyzing two members of CCLM_ctr with opposite behaviors, we found that the large uncertainty in CCLM_ctr is caused by a combination of two factors (1) uncertainty in parameterization of cloud-radiation interaction in the atmospheric model. and (2) lack of an active two-way air-sea interaction. When CCLM is two-way coupled with the ocean model, the ensemble means of GCOAST-AHOI and CCLM_ctr are relatively similar, but the spread is reduced remarkably in GCOAST-AHOI, not only over the ocean where the coupling is done but also over land due to the land-sea interactions.

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