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Abstract
The Arctic climate is governed by complex interactions and feedback mechanisms between the atmosphere, ocean, and solar radiation. One of its characteristic features, the Arctic sea ice, is very vulnerable to anthropogenically caused warming. Production and melting of sea ice is influenced by several physical processes. The authors show that the northward ocean heat transport is an important factor in the simulation of the sea ice extent in the current general circulation models. Those models that transport more energy to the Arctic show a stronger future warming, in the Arctic as well as globally. Larger heat transport to the Arctic, in particular in the Barents Sea, reduces the sea ice cover in this area. More radiation is then absorbed during summer months and is radiated back to the atmosphere in winter months. This process leads to an increase in the surface temperature and therefore to a stronger polar amplification. The models that show a larger global warming agree better with the observed sea ice extent in the Arctic. In general, these models also have a higher spatial resolution.
These results suggest that higher resolution and greater complexity are beneficial in simulating the processes relevant in the Arctic and that future warming in the high northern latitudes is likely to be near the upper range of model projections, consistent with recent evidence that many climate models underestimate Arctic sea ice decline.
Abstract
The Arctic climate is governed by complex interactions and feedback mechanisms between the atmosphere, ocean, and solar radiation. One of its characteristic features, the Arctic sea ice, is very vulnerable to anthropogenically caused warming. Production and melting of sea ice is influenced by several physical processes. The authors show that the northward ocean heat transport is an important factor in the simulation of the sea ice extent in the current general circulation models. Those models that transport more energy to the Arctic show a stronger future warming, in the Arctic as well as globally. Larger heat transport to the Arctic, in particular in the Barents Sea, reduces the sea ice cover in this area. More radiation is then absorbed during summer months and is radiated back to the atmosphere in winter months. This process leads to an increase in the surface temperature and therefore to a stronger polar amplification. The models that show a larger global warming agree better with the observed sea ice extent in the Arctic. In general, these models also have a higher spatial resolution.
These results suggest that higher resolution and greater complexity are beneficial in simulating the processes relevant in the Arctic and that future warming in the high northern latitudes is likely to be near the upper range of model projections, consistent with recent evidence that many climate models underestimate Arctic sea ice decline.
Abstract
Climate projections have been remarkably difficult to constrain by comparing the simulated climatological state from different models with observations, in particular for small ensembles with structurally different models. In this study, the relationship between climate sensitivity and different measures of the present-day climatology is investigated. First, it is shown that 1) a variable proposed earlier that is based on interannual variation of seasonal temperature and 2) the seasonal cycle amplitude are unable to constrain the range of climate sensitivity beyond what was initially covered by the ensemble. Second, it is illustrated how model calibration helps to reveal potentially useful relationships but might also complicate the interpretation of multimodel results. As a consequence, when ensembles are small, when models are neither independently developed nor structurally identical, when observations are likely to have been used in the model development and evaluation process, and when the interpretation of the relationships across models in terms of well-understood physical processes is not obvious, care should be taken when using relationships across models to constrain model projections. This study demonstrates the pitfalls that might occur if emergent statistical relationships are prematurely interpreted as an effective constraint on projected global or regional climate change.
Abstract
Climate projections have been remarkably difficult to constrain by comparing the simulated climatological state from different models with observations, in particular for small ensembles with structurally different models. In this study, the relationship between climate sensitivity and different measures of the present-day climatology is investigated. First, it is shown that 1) a variable proposed earlier that is based on interannual variation of seasonal temperature and 2) the seasonal cycle amplitude are unable to constrain the range of climate sensitivity beyond what was initially covered by the ensemble. Second, it is illustrated how model calibration helps to reveal potentially useful relationships but might also complicate the interpretation of multimodel results. As a consequence, when ensembles are small, when models are neither independently developed nor structurally identical, when observations are likely to have been used in the model development and evaluation process, and when the interpretation of the relationships across models in terms of well-understood physical processes is not obvious, care should be taken when using relationships across models to constrain model projections. This study demonstrates the pitfalls that might occur if emergent statistical relationships are prematurely interpreted as an effective constraint on projected global or regional climate change.
Abstract
About 20 global climate models have been run for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) to predict climate change due to anthropogenic activities. Evaluating these models is an important step to establish confidence in climate projections. Model evaluation, however, is often performed on a gridpoint basis despite the fact that models are known to often be unreliable at such small spatial scales. In this study, the annual mean values of surface air temperature and precipitation are analyzed. Using a spatial smoothing technique with a variable-scale parameter it is shown that the intermodel spread, as well as model errors from observations, is reduced as the characteristic smoothing scale increases. At the same time, the ability to reproduce small-scale features is reduced and the simulated patterns become fuzzy. Depending on the variable of interest, the location, and the way that data are aggregated, different optimal smoothing scales from the gridpoint size to about 2000 km are found to give good agreement with present-day observation yet retain most regional features of the climate signal. Higher model resolution surprisingly does not imply much better agreement with temperature observations, in particular with stronger smoothing, and resolving smaller scales therefore does not necessarily seem to improve the simulation of large-scale climate features. Similarities in mean temperature and precipitation fields for a pair of models in the ensemble persist locally for about a century into the future, providing some justification for subtracting control errors in the models. Large-scale to global errors, however, are not well preserved over time, consistent with a poor constraint of the present-day climate on the simulated global temperature and precipitation response.
Abstract
About 20 global climate models have been run for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) to predict climate change due to anthropogenic activities. Evaluating these models is an important step to establish confidence in climate projections. Model evaluation, however, is often performed on a gridpoint basis despite the fact that models are known to often be unreliable at such small spatial scales. In this study, the annual mean values of surface air temperature and precipitation are analyzed. Using a spatial smoothing technique with a variable-scale parameter it is shown that the intermodel spread, as well as model errors from observations, is reduced as the characteristic smoothing scale increases. At the same time, the ability to reproduce small-scale features is reduced and the simulated patterns become fuzzy. Depending on the variable of interest, the location, and the way that data are aggregated, different optimal smoothing scales from the gridpoint size to about 2000 km are found to give good agreement with present-day observation yet retain most regional features of the climate signal. Higher model resolution surprisingly does not imply much better agreement with temperature observations, in particular with stronger smoothing, and resolving smaller scales therefore does not necessarily seem to improve the simulation of large-scale climate features. Similarities in mean temperature and precipitation fields for a pair of models in the ensemble persist locally for about a century into the future, providing some justification for subtracting control errors in the models. Large-scale to global errors, however, are not well preserved over time, consistent with a poor constraint of the present-day climate on the simulated global temperature and precipitation response.
Abstract
A zonally averaged three-basin ocean–atmosphere model is used to investigate mean steric sea level rise in global warming scenarios. It is shown that if the North Atlantic deep water formation stops due to global warming, steric sea level rise is much larger for the same global mean atmospheric temperature increase than if the thermohaline circulation remains near the present state. In the equilibrium, global mean steric sea level rise depends linearly on the global mean atmospheric temperature increase. The influence of different subgrid-scale ocean mixing parameterizations on steric sea level rise is investigated.
Abstract
A zonally averaged three-basin ocean–atmosphere model is used to investigate mean steric sea level rise in global warming scenarios. It is shown that if the North Atlantic deep water formation stops due to global warming, steric sea level rise is much larger for the same global mean atmospheric temperature increase than if the thermohaline circulation remains near the present state. In the equilibrium, global mean steric sea level rise depends linearly on the global mean atmospheric temperature increase. The influence of different subgrid-scale ocean mixing parameterizations on steric sea level rise is investigated.
Abstract
Most ocean–atmosphere models predict a reduction of the thermohaline circulation for a warmer climate in the near future. Although a reduction in the Atlantic Ocean circulation appears to be a robust result, the question remains open whether the climate system could possibly cross a critical threshold leading to a complete shutdown of the North Atlantic deep-water formation. Ensemble simulations with an ocean–atmosphere climate model of reduced complexity are performed to investigate the range of possible future climate evolutions when the climate system is close to such a threshold. It is found that the sensitivity of the ocean circulation to perturbations increases rapidly when approaching the bifurcation point, thereby severely limiting the predictability of future climate. At the bifurcation point, different response types such as linear responses, nonlinear transitions, or resonance behavior are observed. Close to the threshold, thermohaline shutdowns can occur thousands of years after the warming has stopped. A characterization of the probability for the different response types reveals a more complex picture for the future evolution of the ocean circulation than previously assumed. These results raise fundamental questions of how far the large differences in projections of the Atlantic circulation response to global warming are caused by different representations of processes, parameterizations, and/or resolution in individual models and whether the predictability of the Atlantic circulation becomes inherently limited when approaching a bifurcation point.
Abstract
Most ocean–atmosphere models predict a reduction of the thermohaline circulation for a warmer climate in the near future. Although a reduction in the Atlantic Ocean circulation appears to be a robust result, the question remains open whether the climate system could possibly cross a critical threshold leading to a complete shutdown of the North Atlantic deep-water formation. Ensemble simulations with an ocean–atmosphere climate model of reduced complexity are performed to investigate the range of possible future climate evolutions when the climate system is close to such a threshold. It is found that the sensitivity of the ocean circulation to perturbations increases rapidly when approaching the bifurcation point, thereby severely limiting the predictability of future climate. At the bifurcation point, different response types such as linear responses, nonlinear transitions, or resonance behavior are observed. Close to the threshold, thermohaline shutdowns can occur thousands of years after the warming has stopped. A characterization of the probability for the different response types reveals a more complex picture for the future evolution of the ocean circulation than previously assumed. These results raise fundamental questions of how far the large differences in projections of the Atlantic circulation response to global warming are caused by different representations of processes, parameterizations, and/or resolution in individual models and whether the predictability of the Atlantic circulation becomes inherently limited when approaching a bifurcation point.
Abstract
In a recent paper, Schwartz et al. suggest that 1) over the last century the earth has warmed less than expected, and they discuss several factors that could explain the discrepancy, including climate sensitivity estimates and aerosol forcing. Schwartz et al. then continue to 2) estimate the allowed carbon emissions for stabilization of global temperature, and find that given the uncertainty in the climate sensitivity even the sign of these allowed carbon emissions is unknown, implying that past emissions may already have committed the earth to 2°C warming for a best-estimate value of climate sensitivity of 3 K. Both of these conclusions in the Schwartz et al. study are revisited herein, and it is shown that 1) in contrast to Schwartz et al., current assessments of climate sensitivity, radiative forcing, and thermal disequilibrium do not support the claim of a discrepancy between expected and observed warming; and 2) the allowed emissions estimated by Schwartz et al. are in conflict with results from a hierarchy of climate–carbon cycle models and are strongly underestimated due to erroneous assumptions about the behavior of the carbon cycle and a confusion of the relevant time scales.
Abstract
In a recent paper, Schwartz et al. suggest that 1) over the last century the earth has warmed less than expected, and they discuss several factors that could explain the discrepancy, including climate sensitivity estimates and aerosol forcing. Schwartz et al. then continue to 2) estimate the allowed carbon emissions for stabilization of global temperature, and find that given the uncertainty in the climate sensitivity even the sign of these allowed carbon emissions is unknown, implying that past emissions may already have committed the earth to 2°C warming for a best-estimate value of climate sensitivity of 3 K. Both of these conclusions in the Schwartz et al. study are revisited herein, and it is shown that 1) in contrast to Schwartz et al., current assessments of climate sensitivity, radiative forcing, and thermal disequilibrium do not support the claim of a discrepancy between expected and observed warming; and 2) the allowed emissions estimated by Schwartz et al. are in conflict with results from a hierarchy of climate–carbon cycle models and are strongly underestimated due to erroneous assumptions about the behavior of the carbon cycle and a confusion of the relevant time scales.
Abstract
Recent studies have suggested that significant parts of the observed warming in the early and the late twentieth century were caused by multidecadal internal variability centered in the Atlantic and Pacific Oceans. Here, a novel approach is used that searches for segments of unforced preindustrial control simulations from global climate models that best match the observed Atlantic and Pacific multidecadal variability (AMV and PMV, respectively). In this way, estimates of the influence of AMV and PMV on global temperature that are consistent both spatially and across variables are made. Combined Atlantic and Pacific internal variability impacts the global surface temperatures by up to 0.15°C from peak-to-peak on multidecadal time scales. Internal variability contributed to the warming between the 1920s and 1940s, the subsequent cooling period, and the warming since then. However, variations in the rate of warming still remain after removing the influence of internal variability associated with AMV and PMV on the global temperatures. During most of the twentieth century, AMV dominates over PMV for the multidecadal internal variability imprint on global and Northern Hemisphere temperatures. Less than 10% of the observed global warming during the second half of the twentieth century is caused by internal variability in these two ocean basins, reinforcing the attribution of most of the observed warming to anthropogenic forcings.
Abstract
Recent studies have suggested that significant parts of the observed warming in the early and the late twentieth century were caused by multidecadal internal variability centered in the Atlantic and Pacific Oceans. Here, a novel approach is used that searches for segments of unforced preindustrial control simulations from global climate models that best match the observed Atlantic and Pacific multidecadal variability (AMV and PMV, respectively). In this way, estimates of the influence of AMV and PMV on global temperature that are consistent both spatially and across variables are made. Combined Atlantic and Pacific internal variability impacts the global surface temperatures by up to 0.15°C from peak-to-peak on multidecadal time scales. Internal variability contributed to the warming between the 1920s and 1940s, the subsequent cooling period, and the warming since then. However, variations in the rate of warming still remain after removing the influence of internal variability associated with AMV and PMV on the global temperatures. During most of the twentieth century, AMV dominates over PMV for the multidecadal internal variability imprint on global and Northern Hemisphere temperatures. Less than 10% of the observed global warming during the second half of the twentieth century is caused by internal variability in these two ocean basins, reinforcing the attribution of most of the observed warming to anthropogenic forcings.
Abstract
Climate projections from phase 5 of the Coupled Model Intercomparison Project (CMIP5) ensemble show a decrease in interannual surface temperature variability over high latitudes with a large intermodel spread, in particular over the areas of sea ice retreat. Here relationships are found between the models’ present-day performance in sea ice–related metrics and future changes in temperature variability. These relations, so-called emergent constraints, can produce ensembles of models calibrated with present-day observations with a narrower spread across their members than across the full ensemble. The underlying assumption is that models in better agreement with observations or reanalyses in a carefully selected metric probably have a more realistic representation of local processes, and therefore are more reliable for projections. Thus, the reliability of this method depends on the availability of high-quality observations or reanalyses. This work represents a step toward formalization of the emergent constraints framework, as so far there is no consensus on how the constraints should be best implemented. The authors quantify the reduction in spread from emerging constraints for various metrics and their combinations, different emission scenarios, and seasons. Some of the general features of emerging constraints are discussed, and how to effectively aggregate information across metrics and seasons to achieve the largest reduction in model spread. It is demonstrated, based on the case of temperature variability, that a robust constraint can be obtained by combining relevant metrics across all seasons. Such a constraint results in a strongly reduced spread across model projections, which is consistent with a process understanding of variability changes due to sea ice retreat.
Abstract
Climate projections from phase 5 of the Coupled Model Intercomparison Project (CMIP5) ensemble show a decrease in interannual surface temperature variability over high latitudes with a large intermodel spread, in particular over the areas of sea ice retreat. Here relationships are found between the models’ present-day performance in sea ice–related metrics and future changes in temperature variability. These relations, so-called emergent constraints, can produce ensembles of models calibrated with present-day observations with a narrower spread across their members than across the full ensemble. The underlying assumption is that models in better agreement with observations or reanalyses in a carefully selected metric probably have a more realistic representation of local processes, and therefore are more reliable for projections. Thus, the reliability of this method depends on the availability of high-quality observations or reanalyses. This work represents a step toward formalization of the emergent constraints framework, as so far there is no consensus on how the constraints should be best implemented. The authors quantify the reduction in spread from emerging constraints for various metrics and their combinations, different emission scenarios, and seasons. Some of the general features of emerging constraints are discussed, and how to effectively aggregate information across metrics and seasons to achieve the largest reduction in model spread. It is demonstrated, based on the case of temperature variability, that a robust constraint can be obtained by combining relevant metrics across all seasons. Such a constraint results in a strongly reduced spread across model projections, which is consistent with a process understanding of variability changes due to sea ice retreat.
Abstract
Projected changes in temperature extremes, such as regional changes in the intensity and frequency of hot extremes, differ strongly across climate models. This study shows that this disagreement can be partly explained by discrepancies in the representation of the present-day temperature distribution, motivating the evaluation of models with observations. By evaluating climate models on carefully selected metrics, the models that are more likely to be reliable for long-term projections of temperature extremes are identified. The study found that frequencies of hot extremes are likely to increase at a higher rate than the multimodel mean estimate over large parts of the Northern Hemisphere and Australia. This implies that a higher degree of adaptation is required for a given global temperature target. It also found that projected changes in the intensity of hot extremes can be constrained in several regions, including Australia, central North America, and north Asia. In many other regions, large internal variability can often hamper model evaluation. For both aspects—the intensity and the frequency of hot extremes—the total area over which the constraints can be implemented is limited by the quality and completeness of observations. Thereby, this study highlights the importance of long-term, high-quality, and easily accessible observational records for model evaluation, which are vital to ultimately reduce uncertainties in projections of temperature extremes.
Abstract
Projected changes in temperature extremes, such as regional changes in the intensity and frequency of hot extremes, differ strongly across climate models. This study shows that this disagreement can be partly explained by discrepancies in the representation of the present-day temperature distribution, motivating the evaluation of models with observations. By evaluating climate models on carefully selected metrics, the models that are more likely to be reliable for long-term projections of temperature extremes are identified. The study found that frequencies of hot extremes are likely to increase at a higher rate than the multimodel mean estimate over large parts of the Northern Hemisphere and Australia. This implies that a higher degree of adaptation is required for a given global temperature target. It also found that projected changes in the intensity of hot extremes can be constrained in several regions, including Australia, central North America, and north Asia. In many other regions, large internal variability can often hamper model evaluation. For both aspects—the intensity and the frequency of hot extremes—the total area over which the constraints can be implemented is limited by the quality and completeness of observations. Thereby, this study highlights the importance of long-term, high-quality, and easily accessible observational records for model evaluation, which are vital to ultimately reduce uncertainties in projections of temperature extremes.
Abstract
The influence of atmospheric circulation on winter temperature and precipitation trends over Europe in the period 2006–50 is investigated in a 21-member initial condition ensemble from a fully coupled global climate model and in a multimodel framework consisting of 40 different models. Five versions of a dynamical adjustment method based on empirical orthogonal function analysis of sea level pressure are introduced, and their performance in removing the effect of atmospheric circulation on temperature and precipitation is tested. The differences in atmospheric circulation as simulated by different models in their control runs and under the historical and representative concentration pathway 8.5 (RCP8.5) forcing scenarios are investigated. Dynamical adjustment is applied to the multimodel ensemble to demonstrate that a substantial fraction of the uncertainty in projected European temperature and precipitation trends is explained by atmospheric circulation variability. A statistically significant response of sea level pressure to anthropogenic forcing is identified in the multimodel ensemble under the RCP8.5 scenario. This forced response in atmospheric circulation is associated with a dynamical contribution to the long-term multimodel mean temperature and precipitation trends. The results highlight the importance of accounting for the impact of atmospheric circulation variability on trends in regional climate projections.
Abstract
The influence of atmospheric circulation on winter temperature and precipitation trends over Europe in the period 2006–50 is investigated in a 21-member initial condition ensemble from a fully coupled global climate model and in a multimodel framework consisting of 40 different models. Five versions of a dynamical adjustment method based on empirical orthogonal function analysis of sea level pressure are introduced, and their performance in removing the effect of atmospheric circulation on temperature and precipitation is tested. The differences in atmospheric circulation as simulated by different models in their control runs and under the historical and representative concentration pathway 8.5 (RCP8.5) forcing scenarios are investigated. Dynamical adjustment is applied to the multimodel ensemble to demonstrate that a substantial fraction of the uncertainty in projected European temperature and precipitation trends is explained by atmospheric circulation variability. A statistically significant response of sea level pressure to anthropogenic forcing is identified in the multimodel ensemble under the RCP8.5 scenario. This forced response in atmospheric circulation is associated with a dynamical contribution to the long-term multimodel mean temperature and precipitation trends. The results highlight the importance of accounting for the impact of atmospheric circulation variability on trends in regional climate projections.
