scholarly journals Links between atmospheric blocking and North American winter cold spells in two generations of Canadian Earth System Model large ensembles

2021 ◽  
Author(s):  
Dae Il Jeong ◽  
Bin Yu ◽  
Alex J. Cannon
Keyword(s):  
North America ◽  
Internal Variability ◽  
Cold Spell ◽  
Earth System ◽  
Negative Consequences ◽  
Atmospheric Blocking ◽  
Large Ensembles ◽  
Two Generations ◽  
Winter Cold ◽  
The Impact

AbstractDue to the significant negative consequences of winter cold extremes, there is need to better understand and simulate the mechanisms driving their occurrence. The impact of atmospheric blocking on winter cold spells over North America is investigated using ERA-Interim and NCEP-DOE-R2 reanalyses for 1981–2010. Initial-condition large-ensembles of two generations of Canadian Earth System Models (CanESM5 and its predecessor, CanESM2) are evaluated in terms of their ability to represent the blocking-cold spell linkage and the associated internal-variability. The reanalysis datasets show that 72 and 58% of cold spells in southern and northern North America coincide with blocking occurring in the high-latitude Pacific-North America. Compared to the two reanalyses, CanESM2 and CanESM5 ensembles underestimate by 19.9 and 14.3% cold spell events coincident with blocking, due to significant under-representation of blocking frequency over the North Pacific (− 47.1 and − 29.0%), whereas biases in cold spell frequency are relatively small (6.6 and − 4.7%). In the reanalyses, regions with statistically significant above-normal cold spell frequency relative to climatology lie on the east and/or south flanks of blocking events, whereas those with below-normal frequency lie along the core or surrounding the blocking. The two ensembles reproduce the observed blocking-cold spell linkage over North America, despite underestimating the magnitude of blocking frequency. The two ensembles also reproduce the physical drivers that underpin the blocking-cold spell linkage. Spatial agreement with the reanalyses is found in the simulated patterns of temperature advection and surface heat flux forcing anomalies during blocking events. While CanESM5 shows an improved representation of the blocking climatology relative to CanESM2, both yield similar results in terms of the blocking-cold spell linkage and associated internal-variability.

scholarly journals Links between atmospheric blocking and North American winter cold spells in two generations of Canadian Earth System Model large ensembles

2021 ◽  
Author(s):  
Dae Il Jeong ◽  
Bin Yu ◽  
Alex J. Cannon
Keyword(s):  
North America ◽  
Internal Variability ◽  
Cold Spell ◽  
Earth System ◽  
Negative Consequences ◽  
Atmospheric Blocking ◽  
Large Ensembles ◽  
Two Generations ◽  
Winter Cold ◽  
The Impact

Abstract Due to the significant negative consequences of winter cold extremes, there is need to better understand and simulate the mechanisms driving their occurrence. The impact of atmospheric blocking on winter cold spells over North America is investigated using ERA-Interim and NCEP-DOE-R2 reanalyses for 1981–2010. Initial-condition large-ensembles of two generations of Canadian Earth System Models (CanESM5 and its predecessor, CanESM2) are evaluated in terms of their ability to represent the blocking-cold spell linkage and the associated internal-variability. The reanalysis datasets show that 72% and 58% of cold spells in southern and northern North America coincide with blocking occurring in the high-latitude Pacific-North America. Compared to the two reanalyses, CanESM2 and CanESM5 ensembles underestimate by 19.9% and 14.3% cold spell events coincident with blocking, due to significant under-representation of blocking frequency over the north Pacific (-47.1% and − 29.0%), whereas biases in cold spell frequency are relatively small (6.6% and − 4.7%). In the reanalyses, regions with statistically significant above-normal cold spell frequency relative to climatology lie on the east and/or south flanks of blocking events, whereas those with below-normal frequency lie along the core or surrounding the blocking. The two ensembles reproduce the observed blocking-cold spell linkage over North America, despite underestimating the magnitude of blocking frequency. The two ensembles also reproduce the physical drivers that underpin the blocking-cold spell linkage. Spatial agreement with the reanalyses is found in the simulated patterns of temperature advection and surface heat flux forcing anomalies during blocking events. While CanESM5 shows an improved representation of the blocking climatology relative to CanESM2, both yield similar results in terms of the blocking-cold spell linkage and associated internal-variability.

scholarly journals The Governance Challenge within Socio-Technical Transition Processes: Public Bicycles and Smartphone-Based Bicycles in Guangzhou, China

2021 ◽  
Vol 13 (16) ◽  
pp. 9447
Author(s):  
Hongze Tan ◽  
Shengchen Du
Keyword(s):  
New Technologies ◽  
Urban China ◽  
Negative Consequences ◽  
Sustainable Mobility ◽  
Private Companies ◽  
Two Generations ◽  
The Government ◽  
Point To Point ◽  
Land Control ◽  
The Impact

In urban China, utilitarian cycling plays a significant role in achieving sustainable mobility. Within this context, different kinds of sharing-bicycle programs equipped with new technologies/devices emerge and extend. By comparing two generations of them in Guangzhou (China), this paper explores how new technologies impact existing modes of mobility governance. First, the technical innovations, e.g., app-based bicycle locks and micro-GPS equipment, contribute to liberating emerging private companies from existing governmental regulations based on land control. Second, the adoption of these innovations not only contributes to the accumulation of cultural and symbolic capitals based on a fashionable lifestyle but also links bicycles to personal point-to-point travel data that could be translated to economic capital. Third, the discrepancy between the dispositions of the government and private companies regarding the innovations opens an opportunity for the quick extension of sharing bicycles, which brings both positive and negative consequences on citizens’ daily travel and life. The absence of other civic actors in the decision-making process accelerates the negative consequences caused by the profit-driven fast extension of sharing bicycles and the governmental top-down governing logic. These findings provide academia with implications for understanding the impact of innovations on achieving sustainable mobility.

Interpreting Results from the NARCCAP and NA-CORDEX Ensembles in the Context of Uncertainty in Regional Climate Change Projections

2018 ◽  
Vol 99 (10) ◽  
pp. 2093-2106 ◽  
Author(s):  
Ambarish V. Karmalkar
Keyword(s):  
Climate Change ◽  
North America ◽  
General Circulation ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Internal Variability ◽  
General Circulation Models ◽  
Eastern United States ◽  
The Impact

AbstractTwo ensembles of dynamically downscaled climate simulations for North America—the North American Regional Climate Change Assessment Program (NARCCAP) and the Coordinated Regional Climate Downscaling Experiment (CORDEX) featuring simulations for North America (NA-CORDEX)—are analyzed to assess the impact of using a small set of global general circulation models (GCMs) and regional climate models (RCMs) on representing uncertainty in regional projections. Selecting GCMs for downscaling based on their equilibrium climate sensitivities is a reasonable strategy, but there are regions where the uncertainty is not fully captured. For instance, the six NA-CORDEX GCMs fail to span the full ranges produced by models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) in summer temperature projections in the western and winter precipitation projections in the eastern United States. Similarly, the four NARCCAP GCMs are overall poor at spanning the full CMIP3 ranges in seasonal temperatures. For the Southeast, the NA-CORDEX GCMs capture the uncertainty in summer but not in winter projections, highlighting one consequence of downscaling a subset of GCMs. Ranges produced by the RCMs are often wider than their driving GCMs but are sensitive to the experimental design. For example, the downscaled projections of summer precipitation are of opposite polarity in two RCM ensembles in some regions. Additionally, the ability of the RCMs to simulate observed temperature trends is affected by the internal variability characteristics of both the RCMs and driving GCMs, and is not systematically related to their historical performance. This has implications for adequately sampling the impact of internal variability on regional trends and for using model performance to identify credible projections. These findings suggest that a multimodel perspective on uncertainties in regional projections is integral to the interpretation of RCM results.

scholarly journals Detectability of Decadal Anthropogenic Hydroclimate Changes over North America

2018 ◽  
Vol 31 (7) ◽  
pp. 2579-2597 ◽  
Author(s):  
Honghai Zhang ◽  
Thomas L. Delworth
Keyword(s):  
North America ◽  
Time Scales ◽  
Climate Models ◽  
Signal To Noise Ratio ◽  
Dominant Role ◽  
Internal Variability ◽  
Natural Variability ◽  
Large Ensembles ◽  
Current Century ◽  
Mean State

Regional hydroclimate changes on decadal time scales contain substantial natural variability. This presents a challenge for the detection of anthropogenically forced hydroclimate changes on these spatiotemporal scales because the signal of anthropogenic changes is modest, compared to the noise of natural variability. However, previous studies have shown that this signal-to-noise ratio can be greatly improved in a large model ensemble where each member contains the same signal but different noise. Here, using multiple state-of-the-art large ensembles from two climate models, the authors quantitatively assess the detectability of anthropogenically caused decadal shifts in precipitation-minus-evaporation (PmE) mean state against natural variability, focusing on North America during 2000–50. Anthropogenic forcing is projected to cause detectable (signal larger than noise) shifts in PmE mean state relative to the 1950–99 climatology over 50%–70% of North America by 2050. The earliest detectable signals include, during November–April, a moistening over northeastern North America and a drying over southwestern North America and, during May–October, a drying over central North America. Different processes are responsible for these signals. Changes in submonthly transient eddy moisture fluxes account for the northeastern moistening and central drying, while monthly atmospheric circulation changes explain the southwestern drying. These model findings suggest that despite the dominant role of natural internal variability on decadal time scales, anthropogenic shifts in PmE mean state can be detected over most of North America before the middle of the current century.

scholarly journals A weighting scheme to incorporate large ensembles in multi-model ensemble projections

2019 ◽  
Author(s):  
Anna Louise Merrifield ◽  
Lukas Brunner ◽  
Ruth Lorenz ◽  
Reto Knutti
Keyword(s):  
Internal Variability ◽  
Ensemble Member ◽  
Earth System Model ◽  
System Model ◽  
Initial Condition ◽  
Earth System ◽  
Model Ensemble ◽  
Single Model ◽  
Large Ensembles ◽  
Model Ensembles

Abstract. Multi-model ensembles can be used to estimate uncertainty in projections of regional climate, but this uncertainty often depends on the constituents of the ensemble. The dependence of uncertainty on ensemble composition is clear when single model initial condition large ensembles (SMILEs) are included within a multi-model ensemble. SMILEs introduce new information into a multi-model ensemble by representing region-scale internal variability, but also introduce redundant information, by virtue of a single model being represented by 50–100 outcomes. To preserve the contribution of internal variability and ensure redundancy does not overwhelm uncertainty estimates, a weighting approach is used to incorporate 50-members of the Community Earth System Model (CESM1.2.2), 50-members of the Canadian Earth System Model (CanESM2), and 100-members of the MPI Grand Ensemble (MPI-GE) into an 88-member Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble. The weight assigned to each multi-model ensemble member is based on the member's ability to reproduce observed climate (performance) and scaled by a measure of redundancy (dependence). Surface air temperature (SAT) and sea level pressure (SLP) diagnostics are used to determine the weights, and relationships between present and future diagnostic behavior are discussed. A new diagnostic, estimated forced trend, is proposed to replace a diagnostic with no clear emergent relationship, 50-year regional SAT trend. The influence of the weighting is assessed in estimates of Northern European winter and Mediterranean summer end-of-century warming in the CMIP5 and combined SMILE-CMIP5 multi-model ensembles. The weighting is shown to recover uncertainty obscured by SMILE redundancy, notably in Mediterranean summer. For each SMILE, the independence weight of each ensemble member as a function of the number of SMILE members included in the CMIP5 ensemble is assessed. The independence weight increases linearly with added members with a slope that depends on SMILE, region, and season. Finally, it is shown that the weighting method can be used to guide SMILE member selection if a subsetted ensemble with one member per model is sought. The weight a SMILE receives within a subsetted ensemble depends on which member is used to represent it, reinforcing the advantage of weighting and incorporating all initial condition ensemble members in multi-model ensembles.

scholarly journals How large does a large ensemble need to be?

2020 ◽  
Author(s):  
Sebastian Milinski ◽  
Nicola Maher ◽  
Dirk Olonscheck
Keyword(s):  
North America ◽  
Sea Ice ◽  
Surface Temperature ◽  
Internal Variability ◽  
Forced Response ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ensemble Size ◽  
Large Ensemble ◽  
Large Ensembles

<p>Initial-condition large ensembles with ensemble sizes ranging from 30 to 100 members have become a commonly used tool to quantify the forced response and internal variability in various components of the climate system. However, there is no consensus on the ideal or even sufficient ensemble size for a large ensemble.</p><p>Here, we introduce an objective method to estimate the required ensemble size. This method can be applied to any given application. We demonstrate its use on the examples that represent typical applications of large ensembles: quantifying the forced response, quantifying internal variability, and detecting a forced change in internal variability.</p><p>We analyse forced trends in global mean surface temperature, local surface temperature and precipitation in the MPI Grand Ensemble (Maher et al., 2019). We find that 10 ensemble members are sufficient to quantify the forced response in historical surface temperature over the ocean, but more than 50 members are necessary over land at higher latitudes. </p><p>Next, we apply our method to identify the required ensemble size to sample internal variability of surface temperature over central North America and over the Niño 3.4 region. A moderate ensemble size of 10 members is sufficient to quantify variability over North America, while a large ensemble with close to 50 members is necessary for the Niño 3.4 region.</p><p>Finally, we use the example of September Arctic sea ice area to investigate forced changes in internal variability. In a strong warming scenario, the variability in sea ice area is increasing because more open water near the coastlines allows for more variability compared to a mostly ice-covered Arctic Ocean (Goosse et al., 2009; Olonscheck and Notz, 2017). We show that at least 5 ensemble members are necessary to detect an increase in sea ice variability in a 1% CO<sub>2</sub> experiment. To also quantify the magnitude of the forced change in variability, more than 50 members are necessary.</p><p>These numbers might be highly model dependent. Therefore, the suggested method can also be used with a long control run to estimate the required ensemble size for a model that does not provide a large number of realisations. Therefore, our analysis framework does not only provide valuable information before running a large ensemble, but can also be used to test the robustness of results based on small ensembles or individual realisations.</p><p><em><strong>References</strong><br>Goosse, H., O. Arzel, C. M. Bitz, A. de Montety, and M. Vancoppenolle (2009), Increased variability of the Arctic summer ice extent in a warmer climate, Geophys. Res. Lett., 36(23), 401–5, doi:10.1029/2009GL040546.</em></p><p><em>Olonscheck, D., and D. Notz (2017), Consistently Estimating Internal Climate Variability from Climate Model Simulations, J Climate, 30(23), 9555–9573, doi:10.1175/JCLI-D-16-0428.1.</em></p><p><em>Milinski, S., N. Maher, and D. Olonscheck (2019), How large does a large ensemble need to be? Earth Syst. Dynam. Discuss., 2019, 1–19, doi:10.5194/esd-2019-70.</em></p>

scholarly journals Fldgen v1.0: an emulator with internal variability and space–time correlation for Earth system models

2019 ◽  
Vol 12 (4) ◽  
pp. 1477-1489 ◽  
Author(s):  
Robert Link ◽  
Abigail Snyder ◽  
Cary Lynch ◽  
Corinne Hartin ◽  
Ben Kravitz ◽  
...  
Keyword(s):  
Deterministic Model ◽  
Internal Variability ◽  
R Package ◽  
Time Correlation ◽  
Temperature Fields ◽  
Earth System ◽  
System Models ◽  
Large Ensembles ◽  
The Mean ◽  
Earth System Models

Abstract. Earth system models (ESMs) are the gold standard for producing future projections of climate change, but running them is difficult and costly, and thus researchers are generally limited to a small selection of scenarios. This paper presents a technique for detailed emulation of the Earth system model (ESM) temperature output, based on the construction of a deterministic model for the mean response to global temperature. The residuals between the mean response and the ESM output temperature fields are used to construct variability fields that are added to the mean response to produce the final product. The method produces grid-level output with spatially and temporally coherent variability. Output fields include random components, so the system may be run as many times as necessary to produce large ensembles of fields for applications that require them. We describe the method, show example outputs, and present statistical verification that it reproduces the ESM properties it is intended to capture. This method, available as an open-source R package, should be useful in the study of climate variability and its contribution to uncertainties in the interactions between human and Earth systems.

A weighting scheme to incorporate large ensembles in multi-model ensemble projections

2020 ◽  
Author(s):  
Anna Merrifield ◽  
Lukas Brunner ◽  
Ruth Lorenz ◽  
Reto Knutti
Keyword(s):  
Internal Variability ◽  
Ensemble Member ◽  
Earth System Model ◽  
System Model ◽  
Initial Condition ◽  
Earth System ◽  
Model Ensemble ◽  
Single Model ◽  
Large Ensembles ◽  
Model Ensembles

<p>Multi-model ensembles can be used to estimate uncertainty in projections of regional climate, but this uncertainty often depends on the constituents of the ensemble. The dependence of uncertainty on ensemble composition is clear when single model initial condition large ensembles (SMILEs) are included within a multi-model ensemble. SMILEs introduce new information into a multi-model ensemble by representing region-scale internal variability, but also introduce redundant information, by virtue of a single model being represented by 50–100 outcomes. To preserve the contribution of internal variability and ensure redundancy does not overwhelm uncertainty estimates, a weighting approach is used to incorporate 50-members of the Community Earth System Model (CESM1.2.2), 50-members of the Canadian Earth System Model (CanESM2), and 100-members of the MPI Grand Ensemble (MPI-GE) into an 88-member Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble. The weight assigned to each multi-model ensemble member is based on the member's ability to reproduce observed climate (performance) and scaled by a measure of historical redundancy (dependence). Surface air temperature (SAT) and sea level pressure (SLP) diagnostics are used to determine the weights, and relationships between present and future diagnostic behavior are discussed. A new diagnostic, estimated forced trend, is proposed to replace a diagnostic with no clear emergent relationship, 50-year regional SAT trend.</p><p>The influence of the weighting is assessed in estimates of Northern European winter and Mediterranean summer end-of-century warming in the CMIP5 and combined SMILE-CMIP5 multi-model ensembles. The weighting is shown to recover uncertainty obscured by SMILE redundancy, notably in Mediterranean summer. For each SMILE, the independence weight of each ensemble member as a function of the number of SMILE members included in the CMIP5 ensemble is assessed. The independence weight increases linearly with added members with a slope that depends on SMILE, region, and season. Finally, it is shown that the weighting method can be used to guide SMILE member selection if a subsetted ensemble with one member per model is sought. The weight a SMILE receives within a subsetted ensemble depends on which member is used to represent it, reinforcing the advantage of weighting and incorporating all initial condition ensemble members in multi-model ensembles.</p>

scholarly journals An investigation of weighting schemes suitable for incorporating large ensembles into multi-model ensembles

2020 ◽  
Vol 11 (3) ◽  
pp. 807-834 ◽  
Author(s):  
Anna Louise Merrifield ◽  
Lukas Brunner ◽  
Ruth Lorenz ◽  
Iselin Medhaug ◽  
Reto Knutti
Keyword(s):  
Coupled Model ◽  
Internal Variability ◽  
System Model ◽  
Initial Condition ◽  
Earth System ◽  
Model Intercomparison ◽  
Model Ensemble ◽  
Single Model ◽  
Large Ensembles ◽  
Model Ensembles

Abstract. Multi-model ensembles can be used to estimate uncertainty in projections of regional climate, but this uncertainty often depends on the constituents of the ensemble. The dependence of uncertainty on ensemble composition is clear when single-model initial condition large ensembles (SMILEs) are included within a multi-model ensemble. SMILEs allow for the quantification of internal variability, a non-negligible component of uncertainty on regional scales, but may also serve to inappropriately narrow uncertainty by giving a single model many additional votes. In advance of the mixed multi-model, the SMILE Coupled Model Intercomparison version 6 (CMIP6) ensemble, we investigate weighting approaches to incorporate 50 members of the Community Earth System Model (CESM1.2.2-LE), 50 members of the Canadian Earth System Model (CanESM2-LE), and 100 members of the MPI Grand Ensemble (MPI-GE) into an 88-member Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble. The weights assigned are based on ability to reproduce observed climate (performance) and scaled by a measure of redundancy (dependence). Surface air temperature (SAT) and sea level pressure (SLP) predictors are used to determine the weights, and relationships between present and future predictor behavior are discussed. The estimated residual thermodynamic trend is proposed as an alternative predictor to replace 50-year regional SAT trends, which are more susceptible to internal variability. Uncertainty in estimates of northern European winter and Mediterranean summer end-of-century warming is assessed in a CMIP5 and a combined SMILE–CMIP5 multi-model ensemble. Five different weighting strategies to account for the mix of initial condition (IC) ensemble members and individually represented models within the multi-model ensemble are considered. Allowing all multi-model ensemble members to receive either equal weight or solely a performance weight (based on the root mean square error (RMSE) between members and observations over nine predictors) is shown to lead to uncertainty estimates that are dominated by the presence of SMILEs. A more suitable approach includes a dependence assumption, scaling either by 1∕N, the number of constituents representing a “model”, or by the same RMSE distance metric used to define model performance. SMILE contributions to the weighted ensemble are smallest (<10 %) when a model is defined as an IC ensemble and increase slightly (<20 %) when the definition of a model expands to include members from the same institution and/or development stream. SMILE contributions increase further when dependence is defined by RMSE (over nine predictors) amongst members because RMSEs between SMILE members can be as large as RMSEs between SMILE members and other models. We find that an alternative RMSE distance metric, derived from global SAT and hemispheric SLP climatology, is able to better identify IC members in general and SMILE members in particular as members of the same model. Further, more subtle dependencies associated with resolution differences and component similarities are also identified by the global predictor set.

Conflicts in Biafra, Katanga and Cabinda as a result of the geopolitical legacy of colonialism

2020 ◽  
Vol 2020 (10-3) ◽  
pp. 238-246
Author(s):  
Olga Dzhenchakova
Keyword(s):  
Democratic Republic ◽  
Economic Factor ◽  
Colonial Period ◽  
Sub Saharan Africa ◽  
Negative Consequences ◽  
Post Colonial ◽  
Republic Of The Congo ◽  
Sub Saharan ◽  
The Republic ◽  
The Impact

The article considers the impact of the colonial past of some countries in sub-Saharan Africa and its effect on their development during the post-colonial period. The negative consequences of the geopolitical legacy of colonialism are shown on the example of three countries: Nigeria, the Democratic Republic of the Congo and the Republic of Angola, expressed in the emergence of conflicts in these countries based on ethno-cultural, religious and socio-economic contradictions. At the same time, the focus is made on the economic factor and the consequences of the consumer policy of the former metropolises pursuing their mercantile interests were mixed.

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