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Abstract
Sampling uncertainties in the voluntary observing ship (VOS)-based global ocean–atmosphere flux fields were estimated using the NCEP–NCAR reanalysis and ECMWF 40-yr Re-Analysis (ERA-40) as well as seasonal forecasts without data assimilation. Air–sea fluxes were computed from 6-hourly reanalyzed individual variables using state-of-the-art bulk formulas. Individual variables and computed fluxes were subsampled to simulate VOS-like sampling density. Random simulation of the number of VOS observations and simulation of the number of observations with contemporaneous sampling allowed for estimation of random and total sampling uncertainties respectively. Although reanalyses are dependent on VOS, constituting an important part of data assimilation input, it is assumed that the reanalysis fields adequately reproduce synoptic variability at the sea surface. Sampling errors were quantified by comparison of the regularly sampled (i.e., 6 hourly) and subsampled monthly fields of surface variables and fluxes. In poorly sampled regions random sampling errors amount to 2.5°–3°C for air temperature, 3 m s−1 for the wind speed, 2–2.5 g kg−1 for specific humidity, and 15%–20% of the total cloud cover. The highest random sampling errors in surface fluxes were found for the sensible and latent heat flux and range from 30 to 80 W m−2. Total sampling errors in poorly sampled areas may be higher than random ones by 60%. In poorly sampled subpolar latitudes of the Northern Hemisphere and throughout much of the Southern Ocean the total sampling uncertainty in the net heat flux can amount to 80–100 W m−2. The highest values of the uncertainties associated with the interpolation/extrapolation into unsampled grid boxes are found in subpolar latitudes of both hemispheres for the turbulent fluxes, where they can be comparable with the sampling errors. Simple dependencies of the sampling errors on the number of samples and the magnitude of synoptic variability were derived. Sampling errors estimated from different reanalyses and from seasonal forecasts yield qualitatively comparable spatial patterns, in which the actual values of uncertainties are controlled by the magnitudes of synoptic variability. Finally, estimates of sampling uncertainties are compared with the other errors in air–sea fluxes and the reliability of the estimates obtained is discussed.
Abstract
Sampling uncertainties in the voluntary observing ship (VOS)-based global ocean–atmosphere flux fields were estimated using the NCEP–NCAR reanalysis and ECMWF 40-yr Re-Analysis (ERA-40) as well as seasonal forecasts without data assimilation. Air–sea fluxes were computed from 6-hourly reanalyzed individual variables using state-of-the-art bulk formulas. Individual variables and computed fluxes were subsampled to simulate VOS-like sampling density. Random simulation of the number of VOS observations and simulation of the number of observations with contemporaneous sampling allowed for estimation of random and total sampling uncertainties respectively. Although reanalyses are dependent on VOS, constituting an important part of data assimilation input, it is assumed that the reanalysis fields adequately reproduce synoptic variability at the sea surface. Sampling errors were quantified by comparison of the regularly sampled (i.e., 6 hourly) and subsampled monthly fields of surface variables and fluxes. In poorly sampled regions random sampling errors amount to 2.5°–3°C for air temperature, 3 m s−1 for the wind speed, 2–2.5 g kg−1 for specific humidity, and 15%–20% of the total cloud cover. The highest random sampling errors in surface fluxes were found for the sensible and latent heat flux and range from 30 to 80 W m−2. Total sampling errors in poorly sampled areas may be higher than random ones by 60%. In poorly sampled subpolar latitudes of the Northern Hemisphere and throughout much of the Southern Ocean the total sampling uncertainty in the net heat flux can amount to 80–100 W m−2. The highest values of the uncertainties associated with the interpolation/extrapolation into unsampled grid boxes are found in subpolar latitudes of both hemispheres for the turbulent fluxes, where they can be comparable with the sampling errors. Simple dependencies of the sampling errors on the number of samples and the magnitude of synoptic variability were derived. Sampling errors estimated from different reanalyses and from seasonal forecasts yield qualitatively comparable spatial patterns, in which the actual values of uncertainties are controlled by the magnitudes of synoptic variability. Finally, estimates of sampling uncertainties are compared with the other errors in air–sea fluxes and the reliability of the estimates obtained is discussed.
ABSTRACT
The impact of Arctic sea ice decline on the weather and climate in midlatitudes is still much debated, with observations suggesting a strong link and models a much weaker link. In this study, we use the atmospheric model OpenIFS in a set of model experiments following the protocol outlined in the Polar Amplification Model Intercomparison Project (PAMIP) to investigate whether the simulated atmospheric response to future changes in Arctic sea ice fundamentally depends on model resolution. More specifically, we increase the horizontal resolution of the model from 125 to 39 km with 91 vertical levels; in a second step, resolution is further increased to 16 km with 137 levels in the vertical. The model does produce a response to sea ice decline with a weaker midlatitude Atlantic jet and increased blocking in the high-latitude Atlantic, but no sensitivity to resolution can be detected with 100 members. Furthermore, we find that the ensemble convergence toward the mean is not impacted by the model resolutions considered here.
ABSTRACT
The impact of Arctic sea ice decline on the weather and climate in midlatitudes is still much debated, with observations suggesting a strong link and models a much weaker link. In this study, we use the atmospheric model OpenIFS in a set of model experiments following the protocol outlined in the Polar Amplification Model Intercomparison Project (PAMIP) to investigate whether the simulated atmospheric response to future changes in Arctic sea ice fundamentally depends on model resolution. More specifically, we increase the horizontal resolution of the model from 125 to 39 km with 91 vertical levels; in a second step, resolution is further increased to 16 km with 137 levels in the vertical. The model does produce a response to sea ice decline with a weaker midlatitude Atlantic jet and increased blocking in the high-latitude Atlantic, but no sensitivity to resolution can be detected with 100 members. Furthermore, we find that the ensemble convergence toward the mean is not impacted by the model resolutions considered here.
Abstract
The authors report a hypothesis for the dynamical mechanisms responsible for the strengthening of the Southern Hemisphere circumpolar winds from the lower stratosphere to the surface due to the ozone hole. A general circulation model forced by stratospheric ozone depletion representative of the ozone hole period successfully reproduced these observed changes. Investigation of the dynamical characteristics of the model therefore provides some insight into the actual mechanisms. From this the authors suggest the following: 1) An initial (radiative) strengthening of the lower-stratospheric winds as a result of ozone depletion conditions the polar vortex so that fewer planetary waves propagate up from the troposphere, resulting in weaker planetary wave driving. 2) This causes further strengthening of the vortex, which results in an additional reduction in upward-propagating planetary waves and initiates a positive feedback mechanism in which the weaker wave driving and the associated strengthened winds are drawn downward to the tropopause. 3) In the troposphere the midlatitude jet shifts poleward in association with increases in the synoptic wave fluxes of heat and momentum, which are the result of a positive feedback mechanism consisting of two components: 4) increases in low-level baroclinicity, and the subsequent generation of baroclinic activity (associated with a poleward heat flux), are collocated with the jet latitudinal position, and 5) strengthening anticyclonic shear increases the refraction of wave activity equatorward (associated with a poleward momentum flux). Finally, 6) confinement of planetary waves in the high-latitude troposphere is an important step to couple the stratospheric changes to the tropospheric response.
Abstract
The authors report a hypothesis for the dynamical mechanisms responsible for the strengthening of the Southern Hemisphere circumpolar winds from the lower stratosphere to the surface due to the ozone hole. A general circulation model forced by stratospheric ozone depletion representative of the ozone hole period successfully reproduced these observed changes. Investigation of the dynamical characteristics of the model therefore provides some insight into the actual mechanisms. From this the authors suggest the following: 1) An initial (radiative) strengthening of the lower-stratospheric winds as a result of ozone depletion conditions the polar vortex so that fewer planetary waves propagate up from the troposphere, resulting in weaker planetary wave driving. 2) This causes further strengthening of the vortex, which results in an additional reduction in upward-propagating planetary waves and initiates a positive feedback mechanism in which the weaker wave driving and the associated strengthened winds are drawn downward to the tropopause. 3) In the troposphere the midlatitude jet shifts poleward in association with increases in the synoptic wave fluxes of heat and momentum, which are the result of a positive feedback mechanism consisting of two components: 4) increases in low-level baroclinicity, and the subsequent generation of baroclinic activity (associated with a poleward heat flux), are collocated with the jet latitudinal position, and 5) strengthening anticyclonic shear increases the refraction of wave activity equatorward (associated with a poleward momentum flux). Finally, 6) confinement of planetary waves in the high-latitude troposphere is an important step to couple the stratospheric changes to the tropospheric response.
Abstract
The freshwater stored in the Arctic Ocean is an important component of the global climate system. Currently the Arctic liquid freshwater content (FWC) has reached a record high since the beginning of the last century. In this study we use numerical simulations to investigate the impact of sea ice decline on the Arctic liquid FWC and its spatial distribution. The global unstructured-mesh ocean general circulation model Finite Element Sea Ice–Ocean Model (FESOM) with 4.5-km horizontal resolution in the Arctic region is applied. The simulations show that sea ice decline increases the FWC by freshening the ocean through sea ice meltwater and modifies upper ocean circulation at the same time. The two effects together significantly increase the freshwater stored in the Amerasian basin and reduce its amount in the Eurasian basin. The salinification of the upper Eurasian basin is mainly caused by the reduction in the proportion of Pacific Water and the increase in that of Atlantic Water (AW). Consequently, the sea ice decline did not significantly contribute to the observed rapid increase in the Arctic total liquid FWC. However, the changes in the Arctic freshwater spatial distribution indicate that the influence of sea ice decline on the ocean environment is remarkable. Sea ice decline increases the amount of Barents Sea branch AW in the upper Arctic Ocean, thus reducing its supply to the deeper Arctic layers. This study suggests that all the dynamical processes sensitive to sea ice decline should be taken into account when understanding and predicting Arctic changes.
Abstract
The freshwater stored in the Arctic Ocean is an important component of the global climate system. Currently the Arctic liquid freshwater content (FWC) has reached a record high since the beginning of the last century. In this study we use numerical simulations to investigate the impact of sea ice decline on the Arctic liquid FWC and its spatial distribution. The global unstructured-mesh ocean general circulation model Finite Element Sea Ice–Ocean Model (FESOM) with 4.5-km horizontal resolution in the Arctic region is applied. The simulations show that sea ice decline increases the FWC by freshening the ocean through sea ice meltwater and modifies upper ocean circulation at the same time. The two effects together significantly increase the freshwater stored in the Amerasian basin and reduce its amount in the Eurasian basin. The salinification of the upper Eurasian basin is mainly caused by the reduction in the proportion of Pacific Water and the increase in that of Atlantic Water (AW). Consequently, the sea ice decline did not significantly contribute to the observed rapid increase in the Arctic total liquid FWC. However, the changes in the Arctic freshwater spatial distribution indicate that the influence of sea ice decline on the ocean environment is remarkable. Sea ice decline increases the amount of Barents Sea branch AW in the upper Arctic Ocean, thus reducing its supply to the deeper Arctic layers. This study suggests that all the dynamical processes sensitive to sea ice decline should be taken into account when understanding and predicting Arctic changes.
Abstract
An increasing number of observations have contributed to the performance of numerical weather prediction systems. Accordingly, it is important to evaluate the impact of these observations on forecast accuracy. While the observing system experiment (OSE) requires considerable computational resources, the adjoint-derived method can evaluate the impact of all observational components at a lower cost. In this study, the effect of observations on forecasts is evaluated by the adjoint-derived method using the Weather Research and Forecasting Model, its adjoint model, and a corresponding three-dimensional variational data assimilation system in East Asia and the western North Pacific for the 2008 typhoon season. Radiance observations had the greatest total impact on forecasts, but conventional wind observations had the greatest impact per observation. For each observation type, the total impact was greatest for radiosonde and each Advanced Microwave Sounding Unit (AMSU)-A satellite, followed by surface synoptic observation from a land station (SYNOP), Quick Scatterometer (QuikSCAT), atmospheric motion vector (AMV) wind from a geostationary satellite (GEOAMV), and aviation routine weather reports (METARs). The fraction of beneficial observations was approximately 60%–70%, which is higher than that reported in previous studies. For several analyses of Typhoons Sinlaku (200813) and Jangmi (200815), dropsonde soundings taken near the typhoon had similar or greater observation impacts than routine radiosonde soundings. The sensitivity to the error covariance parameter indicates that reducing (increasing) observation (background) error covariance helps to reduce forecast error in the current analysis framework. The observation impact from OSEs is qualitatively similar to that from the adjoint method for major observation types. This study confirms that radiosonde observations provide primary information on the atmospheric state as in situ observations and that satellite radiances are an essential component of atmospheric observation systems.
Abstract
An increasing number of observations have contributed to the performance of numerical weather prediction systems. Accordingly, it is important to evaluate the impact of these observations on forecast accuracy. While the observing system experiment (OSE) requires considerable computational resources, the adjoint-derived method can evaluate the impact of all observational components at a lower cost. In this study, the effect of observations on forecasts is evaluated by the adjoint-derived method using the Weather Research and Forecasting Model, its adjoint model, and a corresponding three-dimensional variational data assimilation system in East Asia and the western North Pacific for the 2008 typhoon season. Radiance observations had the greatest total impact on forecasts, but conventional wind observations had the greatest impact per observation. For each observation type, the total impact was greatest for radiosonde and each Advanced Microwave Sounding Unit (AMSU)-A satellite, followed by surface synoptic observation from a land station (SYNOP), Quick Scatterometer (QuikSCAT), atmospheric motion vector (AMV) wind from a geostationary satellite (GEOAMV), and aviation routine weather reports (METARs). The fraction of beneficial observations was approximately 60%–70%, which is higher than that reported in previous studies. For several analyses of Typhoons Sinlaku (200813) and Jangmi (200815), dropsonde soundings taken near the typhoon had similar or greater observation impacts than routine radiosonde soundings. The sensitivity to the error covariance parameter indicates that reducing (increasing) observation (background) error covariance helps to reduce forecast error in the current analysis framework. The observation impact from OSEs is qualitatively similar to that from the adjoint method for major observation types. This study confirms that radiosonde observations provide primary information on the atmospheric state as in situ observations and that satellite radiances are an essential component of atmospheric observation systems.
Abstract
Based on analysis of observational data it has been suggested that a negative feedback of ice–ocean stress coupling may limit freshwater accumulation in the Beaufort Gyre (BG). In this paper we explore how this feedback can significantly contribute to BG stabilization in an anticyclonic wind regime. We use an ice–ocean model and turn on and off the feedback in simulations to elucidate the role of the feedback. When a persistent anticyclonic wind anomaly is applied over the BG, liquid freshwater content (FWC) increases because of enhanced Ekman downwelling. As a consequence, ocean surface geostrophic currents speed up. However, the spinup of sea ice is weaker than the acceleration of surface geostrophic currents during wintertime, because of strong sea ice internal stress when ice concentration is high and ice is thick. This leads to cyclonic anomalies in the ice–ocean relative velocity and stress over the BG. The resultant seasonal Ekman upwelling anomaly reduces freshwater accumulation by about 1/4 as compared to a simulation with the negative feedback turned off in a control experiment, with a reduction range of 1/10–1/3 in all experiments conducted. We show that the feedback is more effective when the model’s mesoscale eddy diffusivity is smaller or when sea ice internal stress is stronger. Finally, we argue that the ice–ocean stress feedback may become less significant as the Arctic warms and sea ice declines.
Abstract
Based on analysis of observational data it has been suggested that a negative feedback of ice–ocean stress coupling may limit freshwater accumulation in the Beaufort Gyre (BG). In this paper we explore how this feedback can significantly contribute to BG stabilization in an anticyclonic wind regime. We use an ice–ocean model and turn on and off the feedback in simulations to elucidate the role of the feedback. When a persistent anticyclonic wind anomaly is applied over the BG, liquid freshwater content (FWC) increases because of enhanced Ekman downwelling. As a consequence, ocean surface geostrophic currents speed up. However, the spinup of sea ice is weaker than the acceleration of surface geostrophic currents during wintertime, because of strong sea ice internal stress when ice concentration is high and ice is thick. This leads to cyclonic anomalies in the ice–ocean relative velocity and stress over the BG. The resultant seasonal Ekman upwelling anomaly reduces freshwater accumulation by about 1/4 as compared to a simulation with the negative feedback turned off in a control experiment, with a reduction range of 1/10–1/3 in all experiments conducted. We show that the feedback is more effective when the model’s mesoscale eddy diffusivity is smaller or when sea ice internal stress is stronger. Finally, we argue that the ice–ocean stress feedback may become less significant as the Arctic warms and sea ice declines.
Abstract
Recent observational studies have shown that the centers of action of interannual variability of the North Atlantic Oscillation (NAO) were located farther eastward during winters of the period 1978–97 compared to previous decades of the twentieth century. In this study, which focuses on the winter season (December–March), new diagnostics characterizing this shift are presented. Further, the importance of this shift for NAO-related interannual climate variability in the North Atlantic region is discussed. It is shown that an NAO-related eastward shift in variability can be found for a wide range of different parameters like the number of deep cyclones, near-surface air temperature, and turbulent surface heat flux throughout the North Atlantic region. By using a near-surface air temperature dataset that is homogenous with respect to the kind of observations used, it is shown that the eastward shift is not an artifact of changes in observational practices that took place around the late 1970s. Finally, an EOF-based Monte Carlo test is developed to quantify the probability of changes in the spatial structure of interannual NAO variability for a relatively short (20 yr) time series given multivariate “white noise.” It is estimated that the likelihood for differences in the spatial structure of the NAO between two independent 20-yr periods, which are similar (as measured by the angle and pattern correlation between two NAO patterns) to the observed differences, to occur just by chance is about 18%. From the above results it is argued that care has to be taken when conclusions about long-term properties of NAO-related climate variability are being drawn from relatively short recent observational data (e.g., 1978–97).
Abstract
Recent observational studies have shown that the centers of action of interannual variability of the North Atlantic Oscillation (NAO) were located farther eastward during winters of the period 1978–97 compared to previous decades of the twentieth century. In this study, which focuses on the winter season (December–March), new diagnostics characterizing this shift are presented. Further, the importance of this shift for NAO-related interannual climate variability in the North Atlantic region is discussed. It is shown that an NAO-related eastward shift in variability can be found for a wide range of different parameters like the number of deep cyclones, near-surface air temperature, and turbulent surface heat flux throughout the North Atlantic region. By using a near-surface air temperature dataset that is homogenous with respect to the kind of observations used, it is shown that the eastward shift is not an artifact of changes in observational practices that took place around the late 1970s. Finally, an EOF-based Monte Carlo test is developed to quantify the probability of changes in the spatial structure of interannual NAO variability for a relatively short (20 yr) time series given multivariate “white noise.” It is estimated that the likelihood for differences in the spatial structure of the NAO between two independent 20-yr periods, which are similar (as measured by the angle and pattern correlation between two NAO patterns) to the observed differences, to occur just by chance is about 18%. From the above results it is argued that care has to be taken when conclusions about long-term properties of NAO-related climate variability are being drawn from relatively short recent observational data (e.g., 1978–97).
Abstract
Arctic sea ice decline is expected to continue throughout the twenty-first century as a result of increased greenhouse gas concentrations. Here we investigate the impact of a strong Arctic sea ice decline on the atmospheric circulation and low pressure systems in the Northern Hemisphere through numerical experimentation with a coupled climate model. More specifically, a large ensemble of 1-yr-long integrations, initialized on 1 June with Arctic sea ice thickness artificially reduced by 80%, is compared to corresponding unperturbed control experiments. The sensitivity experiment shows an ice-free Arctic from July to October; during autumn the largest near-surface temperature increase of about 15 K is found in the central Arctic, which goes along with a reduced meridional temperature gradient, a decreased jet stream, and a southward shifted Northern Hemisphere storm track; and the near-surface temperature response in winter and spring reduces substantially due to relatively fast sea ice growth during the freezing season. Changes in the maximum Eady growth rate are generally below 5% and hardly significant, with reduced vertical wind shear and reduced vertical stability counteracting each other. The reduced vertical wind shear manifests itself in a decrease of synoptic activity by up to 10% and shallower cyclones while the reduced vertical stability along with stronger diabatic heating due to more available moisture may be responsible for the stronger deepening rates and thus faster cyclone development once a cyclone starts to form. Furthermore, precipitation minus evaporation decreases over the Arctic because the increase in evaporation outweighs that for precipitation, with implications for the ocean stratification and hence ocean circulation.
Abstract
Arctic sea ice decline is expected to continue throughout the twenty-first century as a result of increased greenhouse gas concentrations. Here we investigate the impact of a strong Arctic sea ice decline on the atmospheric circulation and low pressure systems in the Northern Hemisphere through numerical experimentation with a coupled climate model. More specifically, a large ensemble of 1-yr-long integrations, initialized on 1 June with Arctic sea ice thickness artificially reduced by 80%, is compared to corresponding unperturbed control experiments. The sensitivity experiment shows an ice-free Arctic from July to October; during autumn the largest near-surface temperature increase of about 15 K is found in the central Arctic, which goes along with a reduced meridional temperature gradient, a decreased jet stream, and a southward shifted Northern Hemisphere storm track; and the near-surface temperature response in winter and spring reduces substantially due to relatively fast sea ice growth during the freezing season. Changes in the maximum Eady growth rate are generally below 5% and hardly significant, with reduced vertical wind shear and reduced vertical stability counteracting each other. The reduced vertical wind shear manifests itself in a decrease of synoptic activity by up to 10% and shallower cyclones while the reduced vertical stability along with stronger diabatic heating due to more available moisture may be responsible for the stronger deepening rates and thus faster cyclone development once a cyclone starts to form. Furthermore, precipitation minus evaporation decreases over the Arctic because the increase in evaporation outweighs that for precipitation, with implications for the ocean stratification and hence ocean circulation.
Taking into Account Atmospheric Uncertainty Improves Sequential Assimilation of SMOS Sea Ice Thickness Data in an Ice–Ocean ModelAbstract
The sensitivity of assimilating sea ice thickness data to uncertainty in atmospheric forcing fields is examined using ensemble-based data assimilation experiments with the Massachusetts Institute of Technology General Circulation Model (MITgcm) in the Arctic Ocean during November 2011–January 2012 and the Met Office (UKMO) ensemble atmospheric forecasts. The assimilation system is based on a local singular evolutive interpolated Kalman (LSEIK) filter. It combines sea ice thickness data derived from the European Space Agency’s (ESA) Soil Moisture Ocean Salinity (SMOS) satellite and Special Sensor Microwave Imager/Sounder (SSMIS) sea ice concentration data with the numerical model. The effect of representing atmospheric uncertainty implicit in the ensemble forcing is assessed by three different assimilation experiments. The first two experiments use a single deterministic forcing dataset and a different forgetting factor to inflate the ensemble spread. The third experiment uses 23 members of the UKMO atmospheric ensemble prediction system. It avoids additional ensemble inflation and is hence easier to implement. As expected, the model-data misfits are substantially reduced in all three experiments, but with the ensemble forcing the errors in the forecasts of sea ice concentration and thickness are smaller compared to the experiments with deterministic forcing. This is most likely because the ensemble forcing results in a more plausible spread of the model state ensemble, which represents model uncertainty and produces a better forecast.
Abstract
The sensitivity of assimilating sea ice thickness data to uncertainty in atmospheric forcing fields is examined using ensemble-based data assimilation experiments with the Massachusetts Institute of Technology General Circulation Model (MITgcm) in the Arctic Ocean during November 2011–January 2012 and the Met Office (UKMO) ensemble atmospheric forecasts. The assimilation system is based on a local singular evolutive interpolated Kalman (LSEIK) filter. It combines sea ice thickness data derived from the European Space Agency’s (ESA) Soil Moisture Ocean Salinity (SMOS) satellite and Special Sensor Microwave Imager/Sounder (SSMIS) sea ice concentration data with the numerical model. The effect of representing atmospheric uncertainty implicit in the ensemble forcing is assessed by three different assimilation experiments. The first two experiments use a single deterministic forcing dataset and a different forgetting factor to inflate the ensemble spread. The third experiment uses 23 members of the UKMO atmospheric ensemble prediction system. It avoids additional ensemble inflation and is hence easier to implement. As expected, the model-data misfits are substantially reduced in all three experiments, but with the ensemble forcing the errors in the forecasts of sea ice concentration and thickness are smaller compared to the experiments with deterministic forcing. This is most likely because the ensemble forcing results in a more plausible spread of the model state ensemble, which represents model uncertainty and produces a better forecast.
Abstract
Extreme weather events are triggered by atmospheric circulation patterns and shaped by slower components, including soil moisture and sea surface temperature, and by the background climate. This separation of factors is exploited by the storyline approach in which an atmospheric model is nudged toward the observed dynamics using different climate boundary conditions to explore their influence. The storyline approach disregards uncertain climatic changes in the frequency and intensity of dynamical conditions, focusing instead on the thermodynamic influence of climate on extreme events. Here we demonstrate an advanced storyline approach that employs a coupled climate model (AWI-CM-1-1-MR) in which the large-scale free-troposphere dynamics are nudged toward ERA5 data. Five-member ensembles are run for present-day (2017–19), preindustrial, +2-K, and +4-K climates branching off from CMIP6 historical and scenario simulations of the same model. In contrast to previous studies, which employed atmosphere-only models, feedbacks between extreme events and the ocean and sea ice state, and the dependence of such feedbacks on the climate, are consistently simulated. Our setup is capable of reproducing observed anomalies of relevant unconstrained parameters, including near-surface temperature, cloud cover, soil moisture, sea surface temperature, and sea ice concentration. Focusing on the July 2019 European heat wave, we find that the strongest warming amplification expands from southern to central Europe over the course of the twenty-first century. The warming reaches up to 10 K in the 4-K-warmer climate, suggesting that an analogous event would entail peak temperatures around 50°C in central Europe.
Significance Statement
This work explores a new storyline method to determine the impact of climate change on specific recent extreme events. The observed evolution of the large-scale atmospheric circulation is imposed in a coupled climate model. Variations in climate parameters, including ocean temperatures and sea ice, are well reproduced. By varying the background climate, including CO2 concentrations, it is demonstrated how the July 2019 European heat wave could have evolved in preindustrial times and in warmer climates. For example, up to 10°C warmer peak temperatures could occur in central Europe in a 4°C warmer climate. The method should be explored for other types of extreme events and has the potential to make climate change more tangible and to inform adaptation measures.
Abstract
Extreme weather events are triggered by atmospheric circulation patterns and shaped by slower components, including soil moisture and sea surface temperature, and by the background climate. This separation of factors is exploited by the storyline approach in which an atmospheric model is nudged toward the observed dynamics using different climate boundary conditions to explore their influence. The storyline approach disregards uncertain climatic changes in the frequency and intensity of dynamical conditions, focusing instead on the thermodynamic influence of climate on extreme events. Here we demonstrate an advanced storyline approach that employs a coupled climate model (AWI-CM-1-1-MR) in which the large-scale free-troposphere dynamics are nudged toward ERA5 data. Five-member ensembles are run for present-day (2017–19), preindustrial, +2-K, and +4-K climates branching off from CMIP6 historical and scenario simulations of the same model. In contrast to previous studies, which employed atmosphere-only models, feedbacks between extreme events and the ocean and sea ice state, and the dependence of such feedbacks on the climate, are consistently simulated. Our setup is capable of reproducing observed anomalies of relevant unconstrained parameters, including near-surface temperature, cloud cover, soil moisture, sea surface temperature, and sea ice concentration. Focusing on the July 2019 European heat wave, we find that the strongest warming amplification expands from southern to central Europe over the course of the twenty-first century. The warming reaches up to 10 K in the 4-K-warmer climate, suggesting that an analogous event would entail peak temperatures around 50°C in central Europe.
Significance Statement
This work explores a new storyline method to determine the impact of climate change on specific recent extreme events. The observed evolution of the large-scale atmospheric circulation is imposed in a coupled climate model. Variations in climate parameters, including ocean temperatures and sea ice, are well reproduced. By varying the background climate, including CO2 concentrations, it is demonstrated how the July 2019 European heat wave could have evolved in preindustrial times and in warmer climates. For example, up to 10°C warmer peak temperatures could occur in central Europe in a 4°C warmer climate. The method should be explored for other types of extreme events and has the potential to make climate change more tangible and to inform adaptation measures.
