The seasonal footprinting mechanism in large ensemble simulations of the second generation Canadian earth system model: uncertainty due to internal climate variability

2020 ◽  
Vol 55 (9-10) ◽  
pp. 2523-2541
Author(s):  
Shangfeng Chen ◽  
Bin Yu
Keyword(s):  
Climate Variability ◽  
North Pacific ◽  
Second Generation ◽  
Southern Oscillation ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Central Pacific ◽  
Internal Climate Variability ◽  
Enso Events

Abstract Previous studies indicated that the wintertime North Pacific Oscillation (NPO) could exert marked impacts on the following winter El Niño-Southern Oscillation (ENSO) via the seasonal footprinting mechanism (SFM). Here, we examine this winter NPO-ENSO relationship in a 50-member ensemble of historical simulations conducted with the Canadian Centre for Climate Modeling and Analysis second generation Canadian Earth System Model (CanESM2) over the period of 1950–2005. The observed NPO pattern, featured by a meridional dipole atmospheric anomaly over the North Pacific, can be well reproduced by all of the 50 ensemble members. The multi-member ensemble (MME) mean can well simulate the observed NPO-ENSO relationship, as well as the SFM process. However, there exists a large spread of the results among the 50 members due to internal climate variability. Internal climate variability influences the winter NPO-ENSO relationship through modulating the subtropical center of the NPO. Specifically, the ensemble members with high NPO-ENSO correlations tend to have strong atmospheric anomalies over the subtropical North Pacific in winter. The atmospheric circulation anomaly brings strong sea surface temperature and precipitation anomalies in the tropical central Pacific and westerly wind anomalies over the tropical western Pacific in the following spring. These anomalies sustain in the following seasons and eventually lead to ENSO events in the following winter.

scholarly journals Impact of internal climate variability on the relationship between spring northern tropical Atlantic SST anomalies and succedent winter ENSO: the role of the North Pacific Oscillation

2021 ◽  
pp. 1-80
Author(s):  
Shangfeng Chen ◽  
Wen Chen ◽  
Bin Yu ◽  
Zhibo Li
Keyword(s):  
Climate Variability ◽  
North Pacific ◽  
Climate Modeling ◽  
System Model ◽  
Tropical Atlantic ◽  
Earth System ◽  
North Pacific Oscillation ◽  
Ensemble Simulation ◽  
Internal Climate Variability ◽  
Northern Tropical Atlantic

AbstractPrevious studies suggested that spring sea surface temperature anomalies (SSTAs) in the northern tropical Atlantic (NTA) have a marked influence on the succedent winter El Niño-Southern Oscillation (ENSO). In this study, we examine the spring NTA SSTA-winter ENSO connection in a 50-member large ensemble simulation conducted with the Canadian Centre for Climate Modeling and Analysis second generation Canadian Earth System Model (CanESM2) and a 100-member ensemble simulation conducted with the Max Planck Institute Earth System Model (MPI-ESM). The observed out-of-phase relation of spring NTA SSTA with winter ENSO can be captured by the multi-member ensemble means of the large ensemble simulations from both models. However, the relation shows a large diversity among different ensemble members attributing to the internal climate variability. The preceding winter North Pacific Oscillation (NPO) is suggested to be an important source of the internal climate variability that modulates the spring NTA SSTA-ENSO connection. The modulation of the winter NPO on the subsequent spring NTA SSTA-winter ENSO relation is seen in both climate modeling and observational datasets. When winter NPO and spring NTA SSTA indices have the same (opposite) sign, the linkage between the spring NTA SSTA and the following winter ENSO tends to be weak (strong). The NPO modulates the spring NTA SSTA-winter ENSO relation mainly via changing the zonal wind anomalies over the tropical western-to-central Pacific induced by the spring NTA SSTA. In addition, our analysis indicates that winter NPO may have a marked effect on the predictability of winter ENSO based on the condition of spring NTA SSTA.

The Community Earth System Model (CESM) Large Ensemble Project: A Community Resource for Studying Climate Change in the Presence of Internal Climate Variability

2015 ◽  
Vol 96 (8) ◽  
pp. 1333-1349 ◽  
Author(s):  
J. E. Kay ◽  
C. Deser ◽  
A. Phillips ◽  
A. Mai ◽  
C. Hannay ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Model Error ◽  
Earth System Model ◽  
First Century ◽  
System Model ◽  
Earth System ◽  
Large Ensemble ◽  
Internal Climate Variability ◽  
Community Earth System Model

Abstract While internal climate variability is known to affect climate projections, its influence is often underappreciated and confused with model error. Why? In general, modeling centers contribute a small number of realizations to international climate model assessments [e.g., phase 5 of the Coupled Model Intercomparison Project (CMIP5)]. As a result, model error and internal climate variability are difficult, and at times impossible, to disentangle. In response, the Community Earth System Model (CESM) community designed the CESM Large Ensemble (CESM-LE) with the explicit goal of enabling assessment of climate change in the presence of internal climate variability. All CESM-LE simulations use a single CMIP5 model (CESM with the Community Atmosphere Model, version 5). The core simulations replay the twenty to twenty-first century (1920–2100) 30 times under historical and representative concentration pathway 8.5 external forcing with small initial condition differences. Two companion 1000+-yr-long preindustrial control simulations (fully coupled, prognostic atmosphere and land only) allow assessment of internal climate variability in the absence of climate change. Comprehensive outputs, including many daily fields, are available as single-variable time series on the Earth System Grid for anyone to use. Early results demonstrate the substantial influence of internal climate variability on twentieth- to twenty-first-century climate trajectories. Global warming hiatus decades occur, similar to those recently observed. Internal climate variability alone can produce projection spread comparable to that in CMIP5. Scientists and stakeholders can use CESM-LE outputs to help interpret the observational record, to understand projection spread and to plan for a range of possible futures influenced by both internal climate variability and forced climate change.

scholarly journals Major modes of short-term climate variability in the newly developed NUIST Earth System Model (NESM)

2015 ◽  
Vol 32 (5) ◽  
pp. 585-600 ◽  
Author(s):  
Jian Cao ◽  
Bin Wang ◽  
Baoqiang Xiang ◽  
Juan Li ◽  
Tianjie Wu ◽  
...  
Keyword(s):  
Climate Variability ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Short Term

scholarly journals Tropical climate variability in the Community Earth System Model: Data Assimilation Research Testbed

2019 ◽  
Vol 54 (1-2) ◽  
pp. 793-806 ◽  
Author(s):  
Jonathan Eliashiv ◽  
Aneesh C. Subramanian ◽  
Arthur J. Miller
Keyword(s):  
Data Assimilation ◽  
Climate Variability ◽  
Tropical Climate ◽  
Earth System Model ◽  
The Other ◽  
System Model ◽  
Earth System ◽  
Ocean Atmosphere ◽  
Community Earth System Model ◽  
Free Running

AbstractA new prototype coupled ocean–atmosphere Ensemble Kalman Filter reanalysis product, the Community Earth System Model using the Data Assimilation Research Testbed (CESM-DART), is studied by comparing its tropical climate variability to other reanalysis products, available observations, and a free-running version of the model. The results reveal that CESM-DART produces fields that are comparable in overall performance with those of four other uncoupled and coupled reanalyses. The clearest signature of differences in CESM-DART is in the analysis of the Madden–Julian Oscillation (MJO) and other tropical atmospheric waves. MJO energy is enhanced over the free-running CESM as well as compared to the other products, suggesting the importance of the surface flux coupling at the ocean–atmosphere interface in organizing convective activity. In addition, high-frequency Kelvin waves in CESM-DART are reduced in amplitude compared to the free-running CESM run and the other products, again supportive of the oceanic coupling playing a role in this difference. CESM-DART also exhibits a relatively low bias in the mean tropical precipitation field and mean sensible heat flux field. Conclusive evidence of the importance of coupling on data assimilation performance will require additional detailed direct comparisons with identically formulated, uncoupled data assimilation runs.

scholarly journals Progress towards a probabilistic Earth system model: examining the impact of stochasticity in the atmosphere and land component of EC-Earth v3.2

2019 ◽  
Vol 12 (7) ◽  
pp. 3099-3118 ◽  
Author(s):  
Kristian Strommen ◽  
Hannah M. Christensen ◽  
Dave MacLeod ◽  
Stephan Juricke ◽  
Tim N. Palmer
Keyword(s):  
Climate Model ◽  
Weather Forecasting ◽  
Southern Oscillation ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Beneficial Impact ◽  
The Impact ◽  
Mean State

Abstract. We introduce and study the impact of three stochastic schemes in the EC-Earth climate model: two atmospheric schemes and one stochastic land scheme. These form the basis for a probabilistic Earth system model in atmosphere-only mode. Stochastic parametrization have become standard in several operational weather-forecasting models, in particular due to their beneficial impact on model spread. In recent years, stochastic schemes in the atmospheric component of a model have been shown to improve aspects important for the models long-term climate, such as El Niño–Southern Oscillation (ENSO), North Atlantic weather regimes, and the Indian monsoon. Stochasticity in the land component has been shown to improve the variability of soil processes and improve the representation of heatwaves over Europe. However, the raw impact of such schemes on the model mean is less well studied. It is shown that the inclusion of all three schemes notably changes the model mean state. While many of the impacts are beneficial, some are too large in amplitude, leading to significant changes in the model's energy budget and atmospheric circulation. This implies that in order to maintain the benefits of stochastic physics without shifting the mean state too far from observations, a full re-tuning of the model will typically be required.

Model Description and Evaluation of FIO Earth System Model (FIO-ESM) version 2.0

2021 ◽  
Author(s):  
Ying Bao ◽  
Zhenya Song ◽  
Fangli Qiao
Keyword(s):  
Surface Wave ◽  
Climate Model ◽  
Southern Oscillation ◽  
Earth System Model ◽  
Heat Fluxes ◽  
Meridional Overturning Circulation ◽  
System Model ◽  
Model Description ◽  
Earth System ◽  
Version 2.0

<p>The First Institute of Oceanography Earth System Model (FIO-ESM) version 2.0 was developed and participated in the Climate Model Intercomparison Project phase 6 (CMIP6). In comparison with FIO-ESM v1.0, all component models of FIO-ESM v2.0 are updated, and their resolutions are fined. In addition to the non-breaking surface wave-induced mixing (Bv), which has also been included in FIO-ESM v1.0, there are three more distinctive physical processes in FIO-ESM v2.0, including the effect of surface wave Stokes drifts on air-sea momentum and heat fluxes, the effect of wave-induce sea spray on air-sea heat fluxes and the effect of sea surface temperature (SST) diurnal cycle on air-sea heat and gas fluxes. The FIO-ESM v2.0 has conducted the CMIP6 Diagnostic, Evaluation and Characterization of Klima (DECK) , historical and futrue scenario experiments. The results of pre-industrial run show the stability of the climate model. The historical simulation of FIO-ESM v2.0 for 1850-2014 is evaluated, including the surface air temperature (SAT), precipitation, SST, Atlantic Meridional Overturning Circulation (AMOC), El Niño-Southern Oscillation (ENSO), etc. The climate changes with respect to SAT and SST global warming and decreasing AMOC are well reproduced by FIO-ESM v2.0. The correlation coefficient of the global annual mean SAT anomaly can reach 0.92 with observations. In particular, the large warm SST bias at the east coast of tropical Pacific from FIO-ESM v1.0, which is a common challenge for all climate models, is dramatically reduced in FIO-ESM v2.0 and the ENSO period within the range of 2-7 years is well reproduced with the largest variation of SST anomalies occurring in boreal winter, which is consistent with observations.</p>

Performance of the Taiwan Earth System Model in Simulating Climate Variability Compared with Observations and CMIP6 Model Simulations

2020 ◽  
Author(s):  
Yi-Chi Wang ◽  
Huang-Hsiung Hsu ◽  
Chao-An Chen ◽  
Wan-Ling Tseng ◽  
Pei-Chun Hsu ◽  
...  
Keyword(s):  
Climate Variability ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Model Simulations

Influences of Temperature and Precipitation on Historical and Future Snowpack Variability over the Northern Hemisphere in the Second Generation Canadian Earth System Model

2017 ◽  
Vol 30 (12) ◽  
pp. 4633-4656 ◽  
Author(s):  
Reinel Sospedra-Alfonso ◽  
William J. Merryfield
Keyword(s):  
Northern Hemisphere ◽  
Second Generation ◽  
Snow Accumulation ◽  
Earth System Model ◽  
System Model ◽  
Climate Projections ◽  
Earth System ◽  
Temperature And Precipitation ◽  
Snow Season ◽  
Primary Driver

This study examines the changing roles of temperature and precipitation on snowpack variability in the Northern Hemisphere for Second Generation Canadian Earth System Model (CanESM2) historical (1850–2005) and future (2006–2100) climate simulations. The strength of the linear relationship between monthly snow water equivalent (SWE) in January–April and precipitation P or temperature T predictors is found to be a sigmoidal function of the mean temperature over the snow season up to the indicated month. For P predictors, the strength of this relationship increases for colder snow seasons, whereas for T predictors it increases for warmer snow seasons. These behaviors are largely explained by the daily temperature percentiles below freezing during the snow accumulation period. It is found that there is a threshold temperature (−5±1°C, depending on month in the snow season and largely independent of emission scenario), representing a crossover point below which snow seasons are sufficiently cold that P is the primary driver of snowpack amount and above which T is the primary driver. This isotherm allows one to delineate the snow-climate regions and elevation zones in which snow-cover amounts are more vulnerable to a warming climate. As climate projections indicate that seasonal isotherms shift northward and toward higher elevations, regions where snowpack amount is mainly driven by precipitation recede, whereas temperature-sensitive snow-covered areas extend to higher latitudes and/or elevations, with resulting impacts on ecosystems and society.

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