Search Results
You are looking at 21 - 30 of 42 items for
- Author or Editor: Adam A. Scaife x
- Refine by Access: All Content x
Abstract
No Abstract available.
Abstract
No Abstract available.
Abstract
A mechanistic model simulation initialized on 14 September 2002, forced by 100-hPa geopotential heights from Met Office analyses, reproduced the dynamical features of the 2002 Antarctic major warming. The vortex split on ∼25 September; recovery after the warming, westward and equatorward tilting vortices, and strong baroclinic zones in temperature associated with a dipole pattern of upward and downward vertical velocities were all captured in the simulation. Model results and analyses show a pattern of strong upward wave propagation throughout the warming, with zonal wind deceleration throughout the stratosphere at high latitudes before the vortex split, continuing in the middle and upper stratosphere and spreading to lower latitudes after the split. Three-dimensional Eliassen–Palm fluxes show the largest upward and poleward wave propagation in the 0°–90°E sector prior to the vortex split (coincident with the location of strongest cyclogenesis at the model’s lower boundary), with an additional region of strong upward propagation developing near 180°–270°E. These characteristics are similar to those of Arctic wave-2 major warmings, except that during this warming, the vortex did not split below ∼600 K. The effects of poleward transport and mixing dominate modeled trace gas evolution through most of the mid- to high-latitude stratosphere, with a core region in the lower-stratospheric vortex where enhanced descent dominates and the vortex remains isolated. Strongly tilted vortices led to low-latitude air overlying vortex air, resulting in highly unusual trace gas profiles. Simulations driven with several meteorological datasets reproduced the major warming, but in others, stronger latitudinal gradients at high latitudes at the model boundary resulted in simulations without a complete vortex split in the midstratosphere. Numerous tests indicate very high sensitivity to the boundary fields, especially the wave-2 amplitude. Major warmings occurred for initial fields with stronger winds and larger vortices, but not smaller vortices, consistent with the initiation of wind deceleration by upward-propagating waves near the poleward edge of the region where wave 2 can propagate above the jet core. Thus, given the observed 100-hPa boundary forcing, stratospheric preconditioning is not needed to reproduce a major warming similar to that observed. The anomalously strong forcing in the lower stratosphere can be viewed as the primary direct cause of the major warming.
Abstract
A mechanistic model simulation initialized on 14 September 2002, forced by 100-hPa geopotential heights from Met Office analyses, reproduced the dynamical features of the 2002 Antarctic major warming. The vortex split on ∼25 September; recovery after the warming, westward and equatorward tilting vortices, and strong baroclinic zones in temperature associated with a dipole pattern of upward and downward vertical velocities were all captured in the simulation. Model results and analyses show a pattern of strong upward wave propagation throughout the warming, with zonal wind deceleration throughout the stratosphere at high latitudes before the vortex split, continuing in the middle and upper stratosphere and spreading to lower latitudes after the split. Three-dimensional Eliassen–Palm fluxes show the largest upward and poleward wave propagation in the 0°–90°E sector prior to the vortex split (coincident with the location of strongest cyclogenesis at the model’s lower boundary), with an additional region of strong upward propagation developing near 180°–270°E. These characteristics are similar to those of Arctic wave-2 major warmings, except that during this warming, the vortex did not split below ∼600 K. The effects of poleward transport and mixing dominate modeled trace gas evolution through most of the mid- to high-latitude stratosphere, with a core region in the lower-stratospheric vortex where enhanced descent dominates and the vortex remains isolated. Strongly tilted vortices led to low-latitude air overlying vortex air, resulting in highly unusual trace gas profiles. Simulations driven with several meteorological datasets reproduced the major warming, but in others, stronger latitudinal gradients at high latitudes at the model boundary resulted in simulations without a complete vortex split in the midstratosphere. Numerous tests indicate very high sensitivity to the boundary fields, especially the wave-2 amplitude. Major warmings occurred for initial fields with stronger winds and larger vortices, but not smaller vortices, consistent with the initiation of wind deceleration by upward-propagating waves near the poleward edge of the region where wave 2 can propagate above the jet core. Thus, given the observed 100-hPa boundary forcing, stratospheric preconditioning is not needed to reproduce a major warming similar to that observed. The anomalously strong forcing in the lower stratosphere can be viewed as the primary direct cause of the major warming.
Abstract
A new system for attribution of weather and climate extreme events has been developed based on the atmospheric component of the latest Hadley Centre model. The model is run with either observational data of sea surface temperature and sea ice or estimates of what their values would be without the effect of anthropogenic climatic forcings. In that way, ensembles of simulations are produced that represent the climate with and without the effect of human influences. A comparison between the ensembles provides estimates of the change in the frequency of extremes due to anthropogenic forcings. To evaluate the new system, reliability diagrams are constructed, which compare the model-derived probability of extreme events with their observed frequency. The ability of the model to reproduce realistic distributions of relevant climatic variables is another key aspect of the system evaluation. Results are then presented from analyses of three recent high-impact events: the 2009/10 cold winter in the United Kingdom, the heat wave in Moscow in July 2010, and floods in Pakistan in July 2010. An evaluation assessment indicates the model can provide reliable results for the U.K. and Moscow events but not for Pakistan. It is found that without anthropogenic forcings winters in the United Kingdom colder than 2009/10 would be 7–10 times (best estimate) more common. Although anthropogenic forcings increase the likelihood of heat waves in Moscow, the 2010 event is found to be very uncommon and associated with a return time of several hundred years. No reliable attribution assessment can be made for high-precipitation events in Pakistan.
Abstract
A new system for attribution of weather and climate extreme events has been developed based on the atmospheric component of the latest Hadley Centre model. The model is run with either observational data of sea surface temperature and sea ice or estimates of what their values would be without the effect of anthropogenic climatic forcings. In that way, ensembles of simulations are produced that represent the climate with and without the effect of human influences. A comparison between the ensembles provides estimates of the change in the frequency of extremes due to anthropogenic forcings. To evaluate the new system, reliability diagrams are constructed, which compare the model-derived probability of extreme events with their observed frequency. The ability of the model to reproduce realistic distributions of relevant climatic variables is another key aspect of the system evaluation. Results are then presented from analyses of three recent high-impact events: the 2009/10 cold winter in the United Kingdom, the heat wave in Moscow in July 2010, and floods in Pakistan in July 2010. An evaluation assessment indicates the model can provide reliable results for the U.K. and Moscow events but not for Pakistan. It is found that without anthropogenic forcings winters in the United Kingdom colder than 2009/10 would be 7–10 times (best estimate) more common. Although anthropogenic forcings increase the likelihood of heat waves in Moscow, the 2010 event is found to be very uncommon and associated with a return time of several hundred years. No reliable attribution assessment can be made for high-precipitation events in Pakistan.
Abstract
Using a set of seasonal hindcast simulations produced by the Met Office Global Seasonal Forecast System, version 5 (GloSea5), significant predictability of the southern annular mode (SAM) is demonstrated during the austral spring. The correlation of the September–November mean SAM with observed values is 0.64, which is statistically significant at the 95% confidence level [confidence interval: (0.18, 0.92)], and is similar to that found recently for the North Atlantic Oscillation in the same system. Significant skill is also found in the prediction of the strength of the Antarctic stratospheric polar vortex at 1 month average lead times. Because of the observed strong correlation between interannual variability in the strength of the Antarctic stratospheric circulation and ozone concentrations, it is possible to make skillful predictions of Antarctic column ozone amounts. By studying the variation of forecast skill with time and height, it is shown that skillful predictions of the SAM are significantly influenced by stratospheric anomalies that descend with time and are coupled with the troposphere. This effect allows skillful statistical forecasts of the October mean SAM to be produced based only on midstratosphere anomalies on 1 August. Together, these results both demonstrate a significant advance in the skill of seasonal forecasts of the Southern Hemisphere and highlight the importance of accurate modeling and observation of the stratosphere in producing long-range forecasts.
Abstract
Using a set of seasonal hindcast simulations produced by the Met Office Global Seasonal Forecast System, version 5 (GloSea5), significant predictability of the southern annular mode (SAM) is demonstrated during the austral spring. The correlation of the September–November mean SAM with observed values is 0.64, which is statistically significant at the 95% confidence level [confidence interval: (0.18, 0.92)], and is similar to that found recently for the North Atlantic Oscillation in the same system. Significant skill is also found in the prediction of the strength of the Antarctic stratospheric polar vortex at 1 month average lead times. Because of the observed strong correlation between interannual variability in the strength of the Antarctic stratospheric circulation and ozone concentrations, it is possible to make skillful predictions of Antarctic column ozone amounts. By studying the variation of forecast skill with time and height, it is shown that skillful predictions of the SAM are significantly influenced by stratospheric anomalies that descend with time and are coupled with the troposphere. This effect allows skillful statistical forecasts of the October mean SAM to be produced based only on midstratosphere anomalies on 1 August. Together, these results both demonstrate a significant advance in the skill of seasonal forecasts of the Southern Hemisphere and highlight the importance of accurate modeling and observation of the stratosphere in producing long-range forecasts.
Abstract
Primarily as a response to boundary forcings, certain components of the atmospheric intraseasonal variability are potentially predictable. Particularly referring to the extratropics, the current generation of seasonal forecasting systems is making advancements in predicting these components by realistically initializing many components of the climate system, using higher resolution and utilizing large ensemble sizes.
The operational seasonal prediction system of the Met Office (UKMO) and the corresponding system of the Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) are analyzed in terms of their representation of different aspects of extratropical low-frequency variability. The UKMO system achieves unprecedented high scores in predicting the winter mean phase of the North Atlantic Oscillation (NAO; correlation 0.62) and the Pacific–North American pattern (PNA; correlation 0.82). The CMCC system, despite its smaller ensemble size and coarser resolution, also exhibits significant skill (0.42 for NAO, 0.51 for PNA). Low-frequency variability is underrepresented in both models, particularly in the eastern North Atlantic. Consequently, their intrinsic variability patterns (sectoral EOFs) are somewhat different from the observed patterns.
Regarding the representation of wintertime Northern Hemisphere blocking, after bias correction both systems exhibit a realistic climatology of blocking frequency. In this assessment, instantaneous blocking and large-scale persistent blocking events are identified using daily geopotential height fields at 500 hPa. The blocking signature on the circulation and the dependence of blocking frequency on the NAO are also quite realistic for both systems. Finally, the Met Office system exhibits significant skill in predicting the winter mean frequency of blocking that relates to the NAO.
Abstract
Primarily as a response to boundary forcings, certain components of the atmospheric intraseasonal variability are potentially predictable. Particularly referring to the extratropics, the current generation of seasonal forecasting systems is making advancements in predicting these components by realistically initializing many components of the climate system, using higher resolution and utilizing large ensemble sizes.
The operational seasonal prediction system of the Met Office (UKMO) and the corresponding system of the Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) are analyzed in terms of their representation of different aspects of extratropical low-frequency variability. The UKMO system achieves unprecedented high scores in predicting the winter mean phase of the North Atlantic Oscillation (NAO; correlation 0.62) and the Pacific–North American pattern (PNA; correlation 0.82). The CMCC system, despite its smaller ensemble size and coarser resolution, also exhibits significant skill (0.42 for NAO, 0.51 for PNA). Low-frequency variability is underrepresented in both models, particularly in the eastern North Atlantic. Consequently, their intrinsic variability patterns (sectoral EOFs) are somewhat different from the observed patterns.
Regarding the representation of wintertime Northern Hemisphere blocking, after bias correction both systems exhibit a realistic climatology of blocking frequency. In this assessment, instantaneous blocking and large-scale persistent blocking events are identified using daily geopotential height fields at 500 hPa. The blocking signature on the circulation and the dependence of blocking frequency on the NAO are also quite realistic for both systems. Finally, the Met Office system exhibits significant skill in predicting the winter mean frequency of blocking that relates to the NAO.
Abstract
This study explores the role of the stratosphere as a source of seasonal predictability of surface climate over Northern Hemisphere extratropics both in the observations and climate model predictions. A suite of numerical experiments, including climate simulations and retrospective forecasts, are set up to isolate the role of the stratosphere in seasonal predictive skill of extratropical near-surface land temperature. It is shown that most of the lead-0-month spring predictive skill of land temperature over extratropics, particularly over northern Eurasia, stems from stratospheric initialization. It is further revealed that this predictive skill of extratropical land temperature arises from skillful prediction of the Arctic Oscillation (AO). The dynamical connection between the stratosphere and troposphere is also demonstrated by the significant correlation between the stratospheric polar vortex and sea level pressure anomalies, as well as the migration of the stratospheric zonal wind anomalies to the lower troposphere.
Abstract
This study explores the role of the stratosphere as a source of seasonal predictability of surface climate over Northern Hemisphere extratropics both in the observations and climate model predictions. A suite of numerical experiments, including climate simulations and retrospective forecasts, are set up to isolate the role of the stratosphere in seasonal predictive skill of extratropical near-surface land temperature. It is shown that most of the lead-0-month spring predictive skill of land temperature over extratropics, particularly over northern Eurasia, stems from stratospheric initialization. It is further revealed that this predictive skill of extratropical land temperature arises from skillful prediction of the Arctic Oscillation (AO). The dynamical connection between the stratosphere and troposphere is also demonstrated by the significant correlation between the stratospheric polar vortex and sea level pressure anomalies, as well as the migration of the stratospheric zonal wind anomalies to the lower troposphere.
Abstract
North Atlantic Ocean hurricane activity exhibits significant variation on multiannual time scales. Advance knowledge of periods of high activity would be beneficial to the insurance industry as well as society in general. Previous studies have shown that climate models initialized with current oceanic and atmospheric conditions, known as decadal prediction systems, are skillful at predicting North Atlantic hurricane activity averaged over periods of 2–10 years. We show that this skill also translates into skillful predictions of real-world U.S. hurricane damage. Using such systems, we have developed a prototype climate service for the insurance industry giving probabilistic forecasts of 5-yr-mean North Atlantic hurricane activity, measured by the total accumulated cyclone energy (ACE index), and 5-yr-total U.S. hurricane damage (given in U.S. dollars). Rather than tracking hurricanes in the decadal systems directly, the forecasts use a relative temperature index known to be strongly linked to hurricane activity. Statistical relationships based on past forecasts of the index and observed hurricane activity and U.S. damage are then used to produce probabilistic forecasts. The predictions of hurricane activity and U.S. damage for the period 2020–24 are high, with ∼95% probabilities of being above average. We note that skill in predicting the temperature index on which the forecasts are based has declined in recent years. More research is therefore needed to understand under which conditions the forecasts are most skillful.
Significance Statement
The purpose of this article is to explain the science and methods behind a recently developed prototype climate service that uses initialized climate models to give probabilistic forecasts of 5-yr-mean North Atlantic Ocean hurricane activity, as well as 5-yr-total associated U.S. hurricane damage. Although skill in predicting North Atlantic hurricane activity on this time scale has been known for some time, a key result in this article is showing that this also leads to predictability in real-world damage. These forecasts could be of benefit to the insurance industry and to society in general.
Abstract
North Atlantic Ocean hurricane activity exhibits significant variation on multiannual time scales. Advance knowledge of periods of high activity would be beneficial to the insurance industry as well as society in general. Previous studies have shown that climate models initialized with current oceanic and atmospheric conditions, known as decadal prediction systems, are skillful at predicting North Atlantic hurricane activity averaged over periods of 2–10 years. We show that this skill also translates into skillful predictions of real-world U.S. hurricane damage. Using such systems, we have developed a prototype climate service for the insurance industry giving probabilistic forecasts of 5-yr-mean North Atlantic hurricane activity, measured by the total accumulated cyclone energy (ACE index), and 5-yr-total U.S. hurricane damage (given in U.S. dollars). Rather than tracking hurricanes in the decadal systems directly, the forecasts use a relative temperature index known to be strongly linked to hurricane activity. Statistical relationships based on past forecasts of the index and observed hurricane activity and U.S. damage are then used to produce probabilistic forecasts. The predictions of hurricane activity and U.S. damage for the period 2020–24 are high, with ∼95% probabilities of being above average. We note that skill in predicting the temperature index on which the forecasts are based has declined in recent years. More research is therefore needed to understand under which conditions the forecasts are most skillful.
Significance Statement
The purpose of this article is to explain the science and methods behind a recently developed prototype climate service that uses initialized climate models to give probabilistic forecasts of 5-yr-mean North Atlantic Ocean hurricane activity, as well as 5-yr-total associated U.S. hurricane damage. Although skill in predicting North Atlantic hurricane activity on this time scale has been known for some time, a key result in this article is showing that this also leads to predictability in real-world damage. These forecasts could be of benefit to the insurance industry and to society in general.
Abstract
The Northeast Farming Region (NFR) of China is a critically important area of maize cultivation accounting for ~30% of national production. It is predominantly rain fed, meaning that adverse climate conditions such as drought can significantly affect productivity. Forewarning of such events, to improve contingency planning, could therefore be highly beneficial to the agricultural sector. For this, an improved estimate of drought exposure, and the associated large-scale circulation patterns, is of critical importance. We address these important questions by employing a large ensemble of initialized climate model simulations. These simulations provide 80 times as many summers as the equivalent observational dataset and highlight several limitations of the recent observational record. For example, the chance of a drought greater in area than any current observed event is approximately 5% per year, suggesting the risk of a major drought is significantly underestimated if based solely on recent events. The combination of a weakened East Asian jet stream and intensified subpolar jet are found to be associated with severe NFR drought through enhanced upper-level convergence and anomalous descent, reducing moisture and suppressing precipitation. We identify a strong 500-hPa geopotential height anomaly dipole pattern as a useful metric to identify this mechanism for relevance to seasonal predictability. This work can inform policy planning and decision-making through an improved understanding of the near-term climate exposure and form the basis of new climate services.
Abstract
The Northeast Farming Region (NFR) of China is a critically important area of maize cultivation accounting for ~30% of national production. It is predominantly rain fed, meaning that adverse climate conditions such as drought can significantly affect productivity. Forewarning of such events, to improve contingency planning, could therefore be highly beneficial to the agricultural sector. For this, an improved estimate of drought exposure, and the associated large-scale circulation patterns, is of critical importance. We address these important questions by employing a large ensemble of initialized climate model simulations. These simulations provide 80 times as many summers as the equivalent observational dataset and highlight several limitations of the recent observational record. For example, the chance of a drought greater in area than any current observed event is approximately 5% per year, suggesting the risk of a major drought is significantly underestimated if based solely on recent events. The combination of a weakened East Asian jet stream and intensified subpolar jet are found to be associated with severe NFR drought through enhanced upper-level convergence and anomalous descent, reducing moisture and suppressing precipitation. We identify a strong 500-hPa geopotential height anomaly dipole pattern as a useful metric to identify this mechanism for relevance to seasonal predictability. This work can inform policy planning and decision-making through an improved understanding of the near-term climate exposure and form the basis of new climate services.
