scholarly journals A Simple, Coherent Framework for Partitioning Uncertainty in Climate Predictions

2011 ◽  
Vol 24 (17) ◽  
pp. 4634-4643 ◽  
Author(s):  
Stan Yip ◽  
Christopher A. T. Ferro ◽  
David B. Stephenson ◽  
Ed Hawkins
Keyword(s):  
Model Uncertainty ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
First Century ◽  
Uncertainty Model ◽  
Ensemble Mean ◽  
Scenario Uncertainty ◽  
Twenty First Century ◽  
Multimodel Ensembles

A simple and coherent framework for partitioning uncertainty in multimodel climate ensembles is presented. The analysis of variance (ANOVA) is used to decompose a measure of total variation additively into scenario uncertainty, model uncertainty, and internal variability. This approach requires fewer assumptions than existing methods and can be easily used to quantify uncertainty related to model–scenario interaction—the contribution to model uncertainty arising from the variation across scenarios of model deviations from the ensemble mean. Uncertainty in global mean surface air temperature is quantified as a function of lead time for a subset of the Coupled Model Intercomparison Project phase 3 ensemble and results largely agree with those published by other authors: scenario uncertainty dominates beyond 2050 and internal variability remains approximately constant over the twenty-first century. Both elements of model uncertainty, due to scenario-independent and scenario-dependent deviations from the ensemble mean, are found to increase with time. Estimates of model deviations that arise as by-products of the framework reveal significant differences between models that could lead to a deeper understanding of the sources of uncertainty in multimodel ensembles. For example, three models show a diverging pattern over the twenty-first century, while another model exhibits an unusually large variation among its scenario-dependent deviations.

scholarly journals Climate Model Dependence and the Ensemble Dependence Transformation of CMIP Projections

2015 ◽  
Vol 28 (6) ◽  
pp. 2332-2348 ◽  
Author(s):  
G. Abramowitz ◽  
C. H. Bishop
Keyword(s):  
Climate Model ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
First Century ◽  
Annual Rainfall ◽  
Model Code ◽  
Temperature And Precipitation ◽  
Ensemble Mean ◽  
Out Of Sample ◽  
Twenty First Century

Abstract Obtaining multiple estimates of future climate for a given emissions scenario is key to understanding the likelihood and uncertainty associated with climate-related impacts. This is typically done by collating model estimates from different research institutions internationally with the assumption that they constitute independent samples. Heuristically, however, several factors undermine this assumption: shared treatment of processes between models, shared observed data for evaluation, and even shared model code. Here, a “perfect model” approach is used to test whether a previously proposed ensemble dependence transformation (EDT) can improve twenty-first-century Coupled Model Intercomparison Project (CMIP) projections. In these tests, where twenty-first-century model simulations are used as out-of-sample “observations,” the mean-square difference between the transformed ensemble mean and “observations” is on average 30% less than for the untransformed ensemble mean. In addition, the variance of the transformed ensemble matches the variance of the ensemble mean about the “observations” much better than in the untransformed ensemble. Results show that the EDT has a significant effect on twenty-first-century projections of both surface air temperature and precipitation. It changes projected global average temperature increases by as much as 16% (0.2°C for B1 scenario), regional average temperatures by as much as 2.6°C (RCP8.5 scenario), and regional average annual rainfall by as much as 410 mm (RCP6.0 scenario). In some regions, however, the effect is minimal. It is also found that the EDT causes changes to temperature projections that differ in sign for different emissions scenarios. This may be as much a function of the makeup of the ensembles as the nature of the forcing conditions.

scholarly journals Uncertainty of ENSO-amplitude projections in CMIP5 and CMIP6 models

2021 ◽  
Author(s):  
Goratz Beobide-Arsuaga ◽  
Tobias Bayr ◽  
Annika Reintges ◽  
Mojib Latif
Keyword(s):  
Global Warming ◽  
Standard Deviation ◽  
Zonal Wind ◽  
Model Uncertainty ◽  
Internal Variability ◽  
First Century ◽  
Amplitude Change ◽  
Enso Dynamics ◽  
Scenario Uncertainty ◽  
Twenty First Century

AbstractThere is a long-standing debate on how the El Niño/Southern Oscillation (ENSO) amplitude may change during the twenty-first century in response to global warming. Here we identify the sources of uncertainty in the ENSO amplitude projections in models participating in the Coupled Model Intercomparison Phase 5 (CMIP5) and Phase 6 (CMIP6), and quantify scenario uncertainty, model uncertainty and uncertainty due to internal variability. The model projections exhibit a large spread, ranging from increasing standard deviation of up to 0.6 °C to diminishing standard deviation of up to − 0.4 °C by the end of the twenty-first century. The ensemble-mean ENSO amplitude change is close to zero. Internal variability is the main contributor to the uncertainty during the first three decades; model uncertainty dominates thereafter, while scenario uncertainty is relatively small throughout the twenty-first century. The total uncertainty increases from CMIP5 to CMIP6: while model uncertainty is reduced, scenario uncertainty is considerably increased. The models with “realistic” ENSO dynamics have been analyzed separately and categorized into models with too small, moderate and too large ENSO amplitude in comparison to instrumental observations. The smallest uncertainties are observed in the sub-ensemble exhibiting realistic ENSO dynamics and moderate ENSO amplitude. However, the global warming signal in ENSO-amplitude change is undetectable in all sub-ensembles. The zonal wind-SST feedback is identified as an important factor determining ENSO amplitude change: global warming signal in ENSO amplitude and zonal wind-SST feedback strength are highly correlated across the CMIP5 and CMIP6 models.

scholarly journals Uncertainty in Twenty-First-Century CMIP5 Sea Level Projections

2015 ◽  
Vol 28 (2) ◽  
pp. 838-852 ◽  
Author(s):  
Christopher M. Little ◽  
Radley M. Horton ◽  
Robert E. Kopp ◽  
Michael Oppenheimer ◽  
Stan Yip
Keyword(s):  
Sea Level ◽  
Model Uncertainty ◽  
Internal Variability ◽  
Climate System ◽  
First Century ◽  
The Arctic ◽  
Management Approach ◽  
Local Risk ◽  
Twenty First Century ◽  
Global And Local

Abstract The representative concentration pathway (RCP) simulations included in phase 5 of the Coupled Model Intercomparison Project (CMIP5) quantify the response of the climate system to different natural and anthropogenic forcing scenarios. These simulations differ because of 1) forcing, 2) the representation of the climate system in atmosphere–ocean general circulation models (AOGCMs), and 3) the presence of unforced (internal) variability. Global and local sea level rise projections derived from these simulations, and the emergence of distinct responses to the four RCPs depend on the relative magnitude of these sources of uncertainty at different lead times. Here, the uncertainty in CMIP5 projections of sea level is partitioned at global and local scales, using a 164-member ensemble of twenty-first-century simulations. Local projections at New York City (NYSL) are highlighted. The partition between model uncertainty, scenario uncertainty, and internal variability in global mean sea level (GMSL) is qualitatively consistent with that of surface air temperature, with model uncertainty dominant for most of the twenty-first century. Locally, model uncertainty is dominant through 2100, with maxima in the North Atlantic and the Arctic Ocean. The model spread is driven largely by 4 of the 16 AOGCMs in the ensemble; these models exhibit outlying behavior in all RCPs and in both GMSL and NYSL. The magnitude of internal variability varies widely by location and across models, leading to differences of several decades in the local emergence of RCPs. The AOGCM spread, and its sensitivity to model exclusion and/or weighting, has important implications for sea level assessments, especially if a local risk management approach is utilized.

Twenty-First-Century Snowfall and Snowpack Changes over the Southern California Mountains

2015 ◽  
Vol 29 (1) ◽  
pp. 91-110 ◽  
Author(s):  
Fengpeng Sun ◽  
Alex Hall ◽  
Marla Schwartz ◽  
Daniel B. Walton ◽  
Neil Berg
Keyword(s):  
General Circulation ◽  
Southern California ◽  
Snow Water Equivalent ◽  
Coupled Model ◽  
General Circulation Models ◽  
First Century ◽  
Climate Projections ◽  
Rcp Scenarios ◽  
Ensemble Mean ◽  
Twenty First Century

Abstract Future snowfall and snowpack changes over the mountains of Southern California are projected using a new hybrid dynamical–statistical framework. Output from all general circulation models (GCMs) in phase 5 of the Coupled Model Intercomparison Project archive is downscaled to 2-km resolution over the region. Variables pertaining to snow are analyzed for the middle (2041–60) and end (2081–2100) of the twenty-first century under two representative concentration pathway (RCP) scenarios: RCP8.5 (business as usual) and RCP2.6 (mitigation). These four sets of projections are compared with a baseline reconstruction of climate from 1981 to 2000. For both future time slices and scenarios, ensemble-mean total winter snowfall loss is widespread. By the mid-twenty-first century under RCP8.5, ensemble-mean winter snowfall is about 70% of baseline, whereas the corresponding value for RCP2.6 is somewhat higher (about 80% of baseline). By the end of the century, however, the two scenarios diverge significantly. Under RCP8.5, snowfall sees a dramatic further decline; 2081–2100 totals are only about half of baseline totals. Under RCP2.6, only a negligible further reduction from midcentury snowfall totals is seen. Because of the spread in the GCM climate projections, these figures are all associated with large intermodel uncertainty. Snowpack on the ground, as represented by 1 April snow water equivalent is also assessed. Because of enhanced snowmelt, the loss seen in snowpack is generally 50% greater than that seen in winter snowfall. By midcentury under RCP8.5, warming-accelerated spring snowmelt leads to snow-free dates that are about 1–3 weeks earlier than in the baseline period.

scholarly journals Quantifying the Uncertainty in Historical and Future Simulations of Northern Hemisphere Spring Snow Cover

2016 ◽  
Vol 29 (23) ◽  
pp. 8647-8663 ◽  
Author(s):  
Chad W. Thackeray ◽  
Christopher G. Fletcher ◽  
Lawrence R. Mudryk ◽  
Chris Derksen
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Climate Model ◽  
Coupled Model ◽  
Internal Variability ◽  
Winter Precipitation ◽  
First Century ◽  
Internal Climate Variability ◽  
Snow Cover Extent ◽  
Twenty First Century

Abstract Projections of twenty-first-century Northern Hemisphere (NH) spring snow cover extent (SCE) from two climate model ensembles are analyzed to characterize their uncertainty. Phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel ensemble exhibits variability resulting from both model differences and internal climate variability, whereas spread generated from a Canadian Earth System Model–Large Ensemble (CanESM-LE) experiment is solely a result of internal variability. The analysis shows that simulated 1981–2010 spring SCE trends are slightly weaker than observed (using an ensemble of snow products). Spring SCE is projected to decrease by −3.7% ± 1.1% decade−1 within the CMIP5 ensemble over the twenty-first century. SCE loss is projected to accelerate for all spring months over the twenty-first century, with the exception of June (because most snow in this month has melted by the latter half of the twenty-first century). For 30-yr spring SCE trends over the twenty-first century, internal variability estimated from CanESM-LE is substantial, but smaller than intermodel spread from CMIP5. Additionally, internal variability in NH extratropical land warming trends can affect SCE trends in the near future (R2 = 0.45), while variability in winter precipitation can also have a significant (but lesser) impact on SCE trends. On the other hand, a majority of the intermodel spread is driven by differences in simulated warming (dominant in March–May) and snow cover available for melt (dominant in June). The strong temperature–SCE linkage suggests that model uncertainty in projections of SCE could be potentially reduced through improved simulation of spring season warming over land.

scholarly journals The changing nature of hydroclimatic risks across South Africa

2021 ◽  
Vol 168 (3-4) ◽  
Author(s):  
Adam Schlosser ◽  
Andrei Sokolov ◽  
Ken Strzepek ◽  
Tim Thomas ◽  
Xiang Gao ◽  
...  
Keyword(s):  
South Africa ◽  
Air Temperature ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
First Century ◽  
Near Surface ◽  
Climate Targets ◽  
Twenty First Century

AbstractWe present results from large ensembles of projected twenty-first century changes in seasonal precipitation and near-surface air temperature for the nation of South Africa. These ensembles are a result of combining Monte Carlo projections from a human-Earth system model of intermediate complexity with pattern-scaled responses from climate models of the Coupled Model Intercomparison Project Phase 5 (CMIP5). These future ensemble scenarios consider a range of global actions to abate emissions through the twenty-first century. We evaluate distributions of surface-air temperature and precipitation change over three sub-national regions: western, central, and eastern South Africa. In all regions, we find that without any emissions or climate targets in place, there is a greater than 50% likelihood that mid-century temperatures will increase threefold over the current climate’s two-standard deviation range of variability. However, scenarios that consider more aggressive climate targets all but eliminate the risk of these salient temperature increases. A preponderance of risk toward decreased precipitation (3 to 4 times higher than increased) exists for western and central South Africa. Strong climate targets abate evolving regional hydroclimatic risks. Under a target to limit global climate warming to 1.5 °C by 2100, the risk of precipitation changes within South Africa toward the end of this century (2065–2074) is commensurate to the risk during the 2030s without any global climate target. Thus, these regional hydroclimate risks over South Africa could be delayed by 30 years and, in doing so, provide invaluable lead-time for national efforts to prepare, fortify, and/or adapt.

scholarly journals Surface Air Temperature Changes over the Twentieth and Twenty-First Centuries in China Simulated by 20 CMIP5 Models

2014 ◽  
Vol 27 (11) ◽  
pp. 3920-3937 ◽  
Author(s):  
Liang Chen ◽  
Oliver W. Frauenfeld
Keyword(s):  
Coupled Model ◽  
Surface Air Temperature ◽  
Earth System Model ◽  
First Century ◽  
System Model ◽  
The Tibetan Plateau ◽  
Earth System ◽  
Temperature Trends ◽  
Intercomparison Project ◽  
Twenty First Century

Abstract Historical temperature variability over China during the twentieth century and projected changes under three emission scenarios for the twenty-first century are evaluated on the basis of a multimodel ensemble of 20 GCMs from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and two observational datasets. Changes relative to phase 3 of the Coupled Model Intercomparison Project (CMIP3) are assessed, and the performance of individual GCMs is also quantified. Compared with observations, GCMs have substantial cold biases over the Tibetan Plateau, especially in the cold season. The timing and location of these biases also correspond to the greatest disagreement among the individual models, indicating GCMs’ limitations in reproducing climatic features in this complex terrain. The CMIP5 multimodel ensemble shows better agreement with observations than CMIP3 in terms of the temperature biases. Both CMIP3 and CMIP5 capture the climatic warming over the twentieth century. However, the magnitude of the annual mean temperature trends is underestimated. There is also limited agreement in the spatial and seasonal patterns of temperature trends over China. Based on six statistical measures, four individual models—the Max Planck Institute Earth System Model, low resolution (MPI-ESM-LR), Second Generation Canadian Earth System Model (CanESM2), Model for Interdisciplinary Research on Climate, Earth System Model (MIROC-ESM), and Community Climate System Model, version 4 (CCSM4)—best represent surface air temperature variability over China. The future temperature projections indicate that the representative concentration pathway (RCP) 8.5 and RCP 4.5 scenarios exhibit a gradual increase in annual temperature during the twenty-first century at a rate of 0.60° and 0.27°C (10 yr)−1, respectively. As the lowest-emission mitigation scenario, RCP 2.6 projects the lowest rate of temperature increase [0.10°C (10 yr)−1]. By the end of the twenty-first century, temperature is projected to increase by 1.7°–5.7°C, with larger warming over northern China and the Tibetan Plateau.

ENSO amplitude uncertainty under global warming in CMIP5 models

2020 ◽  
Author(s):  
Goratz Beobide ◽  
Tobias Bayr ◽  
Annika Reintges ◽  
Mojib Latif
Keyword(s):  
Global Warming ◽  
Zonal Wind ◽  
Coupled Model ◽  
Correlation Coefficients ◽  
Internal Variability ◽  
Mean Amplitude ◽  
Cmip5 Models ◽  
Amplitude Change ◽  
Uncertainty Model ◽  
Scenario Uncertainty

<p>The possible change of ENSO amplitude during the 21<sup>st</sup> century in response to global warming has been analyzed in models participating in the Coupled Model Intercomparison Phase 5 (CMIP5). Three types of uncertainties are investigated: scenario uncertainty, model uncertainty, and uncertainty due to internal variability.</p><p>The ENSO response obtained from the CMIP5 models is highly uncertain, leading to an ensemble-mean amplitude change of close to zero until the end of the 21<sup>st</sup> century, with an uncertainty exceeding 0.3 °C. The internal variability is the main contributor to the uncertainty during the first two decades of the projections. The inter-model differences dominate thereafter, while scenario uncertainty is relatively small throughout the whole 21<sup>st</sup> century. The zonal wind-SST feedback has been identified as an important factor of ENSO amplitude change: the global warming signal in the ENSO amplitude and zonal wind-SST feedback are highly correlated across the CMIP5 models, with correlation coefficients of 0.87, 0.84 and 0.78 for the RCP4.5, RCP6.0 and RCP8.5 scenarios, respectively.</p><p>The CMIP5 models with realistic ENSO dynamics have been analyzed separately. In this sub-ensemble, the global warming signal is strengthened with a mean ENSO amplitude decrease of approximately 0.1°C by the end of the 21<sup>st</sup> century. When only considering models with large decadal ENSO amplitude variability, the decrease in ENSO amplitude amounts to 0.1°C and 0.2°C for the RCP4.5 and RCP8.5 scenarios, respectively.</p>

Future Changes in Northern Hemisphere Snowfall

2013 ◽  
Vol 26 (20) ◽  
pp. 7813-7828 ◽  
Author(s):  
John P. Krasting ◽  
Anthony J. Broccoli ◽  
Keith W. Dixon ◽  
John R. Lanzante
Keyword(s):  
Twentieth Century ◽  
Northern Hemisphere ◽  
Climate Models ◽  
Global Climate ◽  
Noise Analysis ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Global Climate Models ◽  
First Century ◽  
Twenty First Century

Abstract Using simulations performed with 18 coupled atmosphere–ocean global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5), projections of the Northern Hemisphere snowfall under the representative concentration pathway (RCP4.5) scenario are analyzed for the period 2006–2100. These models perform well in simulating twentieth-century snowfall, although there is a positive bias in many regions. Annual snowfall is projected to decrease across much of the Northern Hemisphere during the twenty-first century, with increases projected at higher latitudes. On a seasonal basis, the transition zone between negative and positive snowfall trends corresponds approximately to the −10°C isotherm of the late twentieth-century mean surface air temperature, such that positive trends prevail in winter over large regions of Eurasia and North America. Redistributions of snowfall throughout the entire snow season are projected to occur—even in locations where there is little change in annual snowfall. Changes in the fraction of precipitation falling as snow contribute to decreases in snowfall across most Northern Hemisphere regions, while changes in total precipitation typically contribute to increases in snowfall. A signal-to-noise analysis reveals that the projected changes in snowfall, based on the RCP4.5 scenario, are likely to become apparent during the twenty-first century for most locations in the Northern Hemisphere. The snowfall signal emerges more slowly than the temperature signal, suggesting that changes in snowfall are not likely to be early indicators of regional climate change.

scholarly journals Atlantic Meridional Overturning Circulation (AMOC) in CMIP5 Models: RCP and Historical Simulations

2013 ◽  
Vol 26 (18) ◽  
pp. 7187-7197 ◽  
Author(s):  
Wei Cheng ◽  
John C. H. Chiang ◽  
Dongxiao Zhang
Keyword(s):  
North Atlantic ◽  
Coupled Model ◽  
Meridional Overturning Circulation ◽  
First Century ◽  
Model Intercomparison ◽  
The North ◽  
Intercomparison Project ◽  
Meridional Overturning ◽  
The North Atlantic ◽  
Twenty First Century

Abstract The Atlantic meridional overturning circulation (AMOC) simulated by 10 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) for the historical (1850–2005) and future climate is examined. The historical simulations of the AMOC mean state are more closely matched to observations than those of phase 3 of the Coupled Model Intercomparison Project (CMIP3). Similarly to CMIP3, all models predict a weakening of the AMOC in the twenty-first century, though the degree of weakening varies considerably among the models. Under the representative concentration pathway 4.5 (RCP4.5) scenario, the weakening by year 2100 is 5%–40% of the individual model's historical mean state; under RCP8.5, the weakening increases to 15%–60% over the same period. RCP4.5 leads to the stabilization of the AMOC in the second half of the twenty-first century and a slower (then weakening rate) but steady recovery thereafter, while RCP8.5 gives rise to a continuous weakening of the AMOC throughout the twenty-first century. In the CMIP5 historical simulations, all but one model exhibit a weak downward trend [ranging from −0.1 to −1.8 Sverdrup (Sv) century−1; 1 Sv ≡ 106 m3 s−1] over the twentieth century. Additionally, the multimodel ensemble–mean AMOC exhibits multidecadal variability with a ~60-yr periodicity and a peak-to-peak amplitude of ~1 Sv; all individual models project consistently onto this multidecadal mode. This multidecadal variability is significantly correlated with similar variations in the net surface shortwave radiative flux in the North Atlantic and with surface freshwater flux variations in the subpolar latitudes. Potential drivers for the twentieth-century multimodel AMOC variability, including external climate forcing and the North Atlantic Oscillation (NAO), and the implication of these results on the North Atlantic SST variability are discussed.

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