Search Results
You are looking at 1 - 10 of 13 items for
- Author or Editor: Christopher G. Fletcher x
- Refine by Access: All Content x
Abstract
Solar radiation management (SRM) with injections of aerosols into the stratosphere has emerged as a research area of focus with the potential to cool the planet. However, the amount of SRM required to achieve a given level of cooling, and how this relationship evolves in response to increasing greenhouse gas emissions, remains uncertain. Here, we explore the evolution of solar dimming efficacy over time by defining and quantifying a new SRM feedback term, which is analogous to conventional radiative feedbacks. Using Earth system model simulations that dynamically adjust the amount of insolation to offset global mean warming from increasing CO2, we find that positive SRM feedbacks decrease global planetary albedo and diminish the efficacy of solar dimming. Physically, the decrease in albedo is primarily due to reductions in optically thick tropical cloud fraction in the boundary layer and midtroposphere, which is driven by a drying and destabilization of the tropical mid- to lower troposphere. These results offer an energetic explanation for reduced cloud fraction commonly observed in idealized SRM experiments, as well as reaffirm the need to understand the troposphere response, particularly from clouds, in realizable geoengineering experiments and their potential to feed back onto SRM efficacy.
Significance Statement
The goal of this study is to understand how the effectiveness of solar geoengineering may evolve over time. Using a climate model with the ability to directly tune the amount of incoming sunlight, we explore the potential for feedback loops in the climate system to diminish or amplify the desired effect of solar tuning, which is to offset greenhouse gas–induced warming. For this climate model and this solar geoengineering proxy, in particular, we find that feedback loops reduce Earth’s albedo and therefore diminish the desired effect of turning down the sun over time. This study lays the groundwork for understanding potential feedback loops in climate model simulations that represent solar geoengineering in a more realistic way.
Abstract
Solar radiation management (SRM) with injections of aerosols into the stratosphere has emerged as a research area of focus with the potential to cool the planet. However, the amount of SRM required to achieve a given level of cooling, and how this relationship evolves in response to increasing greenhouse gas emissions, remains uncertain. Here, we explore the evolution of solar dimming efficacy over time by defining and quantifying a new SRM feedback term, which is analogous to conventional radiative feedbacks. Using Earth system model simulations that dynamically adjust the amount of insolation to offset global mean warming from increasing CO2, we find that positive SRM feedbacks decrease global planetary albedo and diminish the efficacy of solar dimming. Physically, the decrease in albedo is primarily due to reductions in optically thick tropical cloud fraction in the boundary layer and midtroposphere, which is driven by a drying and destabilization of the tropical mid- to lower troposphere. These results offer an energetic explanation for reduced cloud fraction commonly observed in idealized SRM experiments, as well as reaffirm the need to understand the troposphere response, particularly from clouds, in realizable geoengineering experiments and their potential to feed back onto SRM efficacy.
Significance Statement
The goal of this study is to understand how the effectiveness of solar geoengineering may evolve over time. Using a climate model with the ability to directly tune the amount of incoming sunlight, we explore the potential for feedback loops in the climate system to diminish or amplify the desired effect of solar tuning, which is to offset greenhouse gas–induced warming. For this climate model and this solar geoengineering proxy, in particular, we find that feedback loops reduce Earth’s albedo and therefore diminish the desired effect of turning down the sun over time. This study lays the groundwork for understanding potential feedback loops in climate model simulations that represent solar geoengineering in a more realistic way.
Abstract
Recent proposed seasonal hindcast skill estimates for the winter North Atlantic Oscillation (NAO) are derived from different lagged predictors, NAO indices, skill assessment periods, and skill validation methodologies. This creates confusion concerning what is the best-lagged predictor of the winter NAO. To rectify this situation, a standardized comparison of NAO cross-validated hindcast skill is performed against three NAO indices over three extended periods (1900–2001, 1950–2001, and 1972–2001). The lagged predictors comprise four previously published predictors involving anomalies in North Atlantic sea surface temperature (SST), Northern Hemisphere (NH) snow cover, and an additional predictor, an index of NH subpolar summer air temperature (T SP). Significant (p < 0.05) NAO hindcast skill is found with May SST 1900–2001, summer/autumn SST 1950–2001, and warm season snow cover 1972–2001. However, the highest and most significant hindcast skill for all periods and all NAO indices is achieved with T SP. Hindcast skill is nonstationary using all predictors and is highest during 1972–2001 with a T SP correlation skill of 0.59 and a mean-squared skill score of 35%. Observational evidence is presented to support a dynamical link between summer T SP and the winter NAO. Summer T SP is associated with a contemporaneous midlatitude zonal wind anomaly. This leads a pattern of North Atlantic SST that persists through autumn. Autumn SSTs may force a direct thermal NAO response or initiate a response via a third variable. These findings suggest that the NH subpolar regions may provide additional winter NAO lagged predictability alongside the midlatitudes and the Tropics.
Abstract
Recent proposed seasonal hindcast skill estimates for the winter North Atlantic Oscillation (NAO) are derived from different lagged predictors, NAO indices, skill assessment periods, and skill validation methodologies. This creates confusion concerning what is the best-lagged predictor of the winter NAO. To rectify this situation, a standardized comparison of NAO cross-validated hindcast skill is performed against three NAO indices over three extended periods (1900–2001, 1950–2001, and 1972–2001). The lagged predictors comprise four previously published predictors involving anomalies in North Atlantic sea surface temperature (SST), Northern Hemisphere (NH) snow cover, and an additional predictor, an index of NH subpolar summer air temperature (T SP). Significant (p < 0.05) NAO hindcast skill is found with May SST 1900–2001, summer/autumn SST 1950–2001, and warm season snow cover 1972–2001. However, the highest and most significant hindcast skill for all periods and all NAO indices is achieved with T SP. Hindcast skill is nonstationary using all predictors and is highest during 1972–2001 with a T SP correlation skill of 0.59 and a mean-squared skill score of 35%. Observational evidence is presented to support a dynamical link between summer T SP and the winter NAO. Summer T SP is associated with a contemporaneous midlatitude zonal wind anomaly. This leads a pattern of North Atlantic SST that persists through autumn. Autumn SSTs may force a direct thermal NAO response or initiate a response via a third variable. These findings suggest that the NH subpolar regions may provide additional winter NAO lagged predictability alongside the midlatitudes and the Tropics.
Abstract
The northern annular mode (NAM) influences wintertime climate variability in the Northern Hemisphere, and understanding the processes controlling its sign and amplitude is of critical importance. Mounting evidence supports a robust teleconnection between the El Niño–Southern Oscillation (ENSO) and the NAM, while internal variability generated in the tropical Indian Ocean (TIO) may be associated with a NAM response of the opposite sign. This study uses a coupled ocean–atmosphere model to separate the influence on the NAM from teleconnections driven by ENSO and the TIO. In composites constructed using a long preindustrial control integration, increased December–February precipitation in the central/eastern Pacific drives a negative late-winter NAM response. When isolated from ENSO variability, increased precipitation over the western-central TIO drives a strong and persistent positive NAM response throughout the winter. Opposite linear interference of the anomalous wave teleconnections explains most of the opposite-signed planetary wavedriving of the NAM responses. The case with combined ENSO and TIO variability yields cancellation of the wave interference and a weak NAM response. This mechanism is confirmed using experiments where the tropical ocean is nudged separately over the Pacific and TIO to the large-amplitude 1997/98–1998/99 ENSO cycle. The phases of the Rossby wave and NAM responses in these two cases are of opposite sign, providing strong evidence that internal variability over the TIO can induce teleconnections independent of—and with opposite sign to—those associated with ENSO.
Abstract
The northern annular mode (NAM) influences wintertime climate variability in the Northern Hemisphere, and understanding the processes controlling its sign and amplitude is of critical importance. Mounting evidence supports a robust teleconnection between the El Niño–Southern Oscillation (ENSO) and the NAM, while internal variability generated in the tropical Indian Ocean (TIO) may be associated with a NAM response of the opposite sign. This study uses a coupled ocean–atmosphere model to separate the influence on the NAM from teleconnections driven by ENSO and the TIO. In composites constructed using a long preindustrial control integration, increased December–February precipitation in the central/eastern Pacific drives a negative late-winter NAM response. When isolated from ENSO variability, increased precipitation over the western-central TIO drives a strong and persistent positive NAM response throughout the winter. Opposite linear interference of the anomalous wave teleconnections explains most of the opposite-signed planetary wavedriving of the NAM responses. The case with combined ENSO and TIO variability yields cancellation of the wave interference and a weak NAM response. This mechanism is confirmed using experiments where the tropical ocean is nudged separately over the Pacific and TIO to the large-amplitude 1997/98–1998/99 ENSO cycle. The phases of the Rossby wave and NAM responses in these two cases are of opposite sign, providing strong evidence that internal variability over the TIO can induce teleconnections independent of—and with opposite sign to—those associated with ENSO.
Abstract
Recent observational and modeling studies have demonstrated a link between eastern tropical Pacific Ocean (TPO) warming associated with the El Niño–Southern Oscillation (ENSO) and the negative phase of the wintertime northern annular mode (NAM). The TPO–NAM link involves a Rossby wave teleconnection from the tropics to the extratropics, and an increase in polar stratospheric wave driving that in turn induces a negative NAM anomaly in the stratosphere and troposphere. Previous work further suggests that tropical Indian Ocean (TIO) warming is associated with a positive NAM anomaly, which is of opposite sign to the TPO case. The TIO case is, however, difficult to interpret because the TPO and TIO warmings are not independent. To better understand the dynamics of tropical influences on the NAM, the current study investigates the NAM response to imposed TPO and TIO warmings in a general circulation model. The NAM responses to the two warmings have opposite sign and can be of surprisingly similar amplitude even though the TIO forcing is relatively weak. It is shown that the sign and strength of the NAM response is often simply related to the phasing, and hence the linear interference, between the Rossby wave response and the climatological stationary wave. The TPO (TIO) wave response reinforces (attenuates) the climatological wave and therefore weakens (strengthens) the stratospheric jet and leads to a negative (positive) NAM response. In additional simulations, it is shown that decreasing the strength of the climatological stationary wave reduces the importance of linear interference and increases the importance of nonlinearity. This work demonstrates that the simulated extratropical annular mode response to climate forcings can depend sensitively on the amplitude and phase of the climatological stationary wave and the wave response.
Abstract
Recent observational and modeling studies have demonstrated a link between eastern tropical Pacific Ocean (TPO) warming associated with the El Niño–Southern Oscillation (ENSO) and the negative phase of the wintertime northern annular mode (NAM). The TPO–NAM link involves a Rossby wave teleconnection from the tropics to the extratropics, and an increase in polar stratospheric wave driving that in turn induces a negative NAM anomaly in the stratosphere and troposphere. Previous work further suggests that tropical Indian Ocean (TIO) warming is associated with a positive NAM anomaly, which is of opposite sign to the TPO case. The TIO case is, however, difficult to interpret because the TPO and TIO warmings are not independent. To better understand the dynamics of tropical influences on the NAM, the current study investigates the NAM response to imposed TPO and TIO warmings in a general circulation model. The NAM responses to the two warmings have opposite sign and can be of surprisingly similar amplitude even though the TIO forcing is relatively weak. It is shown that the sign and strength of the NAM response is often simply related to the phasing, and hence the linear interference, between the Rossby wave response and the climatological stationary wave. The TPO (TIO) wave response reinforces (attenuates) the climatological wave and therefore weakens (strengthens) the stratospheric jet and leads to a negative (positive) NAM response. In additional simulations, it is shown that decreasing the strength of the climatological stationary wave reduces the importance of linear interference and increases the importance of nonlinearity. This work demonstrates that the simulated extratropical annular mode response to climate forcings can depend sensitively on the amplitude and phase of the climatological stationary wave and the wave response.
Abstract
Remote sensing snowfall retrievals are powerful tools for advancing our understanding of global snow accumulation patterns. However, current satellite-based snowfall retrievals rely on assumptions about snowfall particle shape, size, and distribution that contribute to uncertainty and biases in their estimates. Vertical radar reflectivity profiles provided by the vertically pointing X-band radar (VertiX) instrument in Egbert, Ontario, Canada, are compared with in situ surface snow accumulation measurements from January to March 2012 as a part of the Global Precipitation Measurement (GPM) Cold Season Precipitation Experiment (GCPEx). In this work, we train a random forest (RF) machine learning model on VertiX radar profiles and ERA5 atmospheric temperature estimates to derive a surface snow accumulation regression model. Using event-based training–testing sets, the RF model demonstrates high predictive skill in estimating surface snow accumulation at 5-min intervals with a low mean-square error of approximately 1.8 × 10−3 mm2 when compared with collocated in situ measurements. The machine learning model outperformed other common radar-based snowfall retrievals (Ze –S relationships) that were unable to accurately capture the magnitudes of peaks and troughs in observed snow accumulation. The RF model also displayed consistent skill when applied to unseen data at a separate experimental site in South Korea. An estimate of predictor importance from the RF model reveals that combinations of multiple reflectivity measurement bins in the boundary layer below 2 km were the most significant features in predicting snow accumulation. Nonlinear machine learning–based retrievals like those explored in this work can offer new, important insights into global snow accumulation patterns and overcome traditional challenges resulting from sparse in situ observational networks.
Abstract
Remote sensing snowfall retrievals are powerful tools for advancing our understanding of global snow accumulation patterns. However, current satellite-based snowfall retrievals rely on assumptions about snowfall particle shape, size, and distribution that contribute to uncertainty and biases in their estimates. Vertical radar reflectivity profiles provided by the vertically pointing X-band radar (VertiX) instrument in Egbert, Ontario, Canada, are compared with in situ surface snow accumulation measurements from January to March 2012 as a part of the Global Precipitation Measurement (GPM) Cold Season Precipitation Experiment (GCPEx). In this work, we train a random forest (RF) machine learning model on VertiX radar profiles and ERA5 atmospheric temperature estimates to derive a surface snow accumulation regression model. Using event-based training–testing sets, the RF model demonstrates high predictive skill in estimating surface snow accumulation at 5-min intervals with a low mean-square error of approximately 1.8 × 10−3 mm2 when compared with collocated in situ measurements. The machine learning model outperformed other common radar-based snowfall retrievals (Ze –S relationships) that were unable to accurately capture the magnitudes of peaks and troughs in observed snow accumulation. The RF model also displayed consistent skill when applied to unseen data at a separate experimental site in South Korea. An estimate of predictor importance from the RF model reveals that combinations of multiple reflectivity measurement bins in the boundary layer below 2 km were the most significant features in predicting snow accumulation. Nonlinear machine learning–based retrievals like those explored in this work can offer new, important insights into global snow accumulation patterns and overcome traditional challenges resulting from sparse in situ observational networks.
Abstract
CloudSat’s Cloud Profiling Radar is a valuable tool for remotely monitoring high-latitude snowfall, but its ability to observe hydrometeor activity near the Earth’s surface is limited by a radar blind zone caused by ground clutter contamination. This study presents the development of a deeply supervised U-Net-style convolutional neural network to predict cold season reflectivity profiles within the blind zone at two Arctic locations. The network learns to predict the presence and intensity of near-surface hydrometeors by coupling latent features encoded in blind zone-aloft clouds with additional context from collocated atmospheric state variables (i.e., temperature, specific humidity, and wind speed). Results show that the U-Net predictions outperform traditional linear extrapolation methods, with low mean absolute error, a 38% higher Sørensen–Dice coefficient, and vertical reflectivity distributions 60% closer to observed values. The U-Net is also able to detect the presence of near-surface cloud with a critical success index (CSI) of 72% and cases of shallow cumuliform snowfall and virga with 18% higher CSI values compared to linear methods. An explainability analysis shows that reflectivity information throughout the scene, especially at cloud edges and at the 1.2-km blind zone threshold, along with atmospheric state variables near the tropopause, are the most significant contributors to model skill. This surface-trained generative inpainting technique has the potential to enhance current and future remote sensing precipitation missions by providing a better understanding of the nonlinear relationship between blind zone reflectivity values and the surrounding atmospheric state.
Significance Statement
Snowfall is a critical contributor to the global water–energy budget, with important connections to water resource management, flood mitigation, and ecosystem sustainability. However, traditional spaceborne remote monitoring of snowfall faces challenges due to a near-surface radar blind zone, which masks a portion of the atmosphere. In this study, a deep learning model was developed to fill in missing data across these regions using surface radar and atmospheric state variables. The model accurately predicts reflectivity, with significant improvements over conventional methods. This innovative approach enhances our understanding of reflectivity patterns and atmospheric interactions, bolstering advances in remote snowfall prediction.
Abstract
CloudSat’s Cloud Profiling Radar is a valuable tool for remotely monitoring high-latitude snowfall, but its ability to observe hydrometeor activity near the Earth’s surface is limited by a radar blind zone caused by ground clutter contamination. This study presents the development of a deeply supervised U-Net-style convolutional neural network to predict cold season reflectivity profiles within the blind zone at two Arctic locations. The network learns to predict the presence and intensity of near-surface hydrometeors by coupling latent features encoded in blind zone-aloft clouds with additional context from collocated atmospheric state variables (i.e., temperature, specific humidity, and wind speed). Results show that the U-Net predictions outperform traditional linear extrapolation methods, with low mean absolute error, a 38% higher Sørensen–Dice coefficient, and vertical reflectivity distributions 60% closer to observed values. The U-Net is also able to detect the presence of near-surface cloud with a critical success index (CSI) of 72% and cases of shallow cumuliform snowfall and virga with 18% higher CSI values compared to linear methods. An explainability analysis shows that reflectivity information throughout the scene, especially at cloud edges and at the 1.2-km blind zone threshold, along with atmospheric state variables near the tropopause, are the most significant contributors to model skill. This surface-trained generative inpainting technique has the potential to enhance current and future remote sensing precipitation missions by providing a better understanding of the nonlinear relationship between blind zone reflectivity values and the surrounding atmospheric state.
Significance Statement
Snowfall is a critical contributor to the global water–energy budget, with important connections to water resource management, flood mitigation, and ecosystem sustainability. However, traditional spaceborne remote monitoring of snowfall faces challenges due to a near-surface radar blind zone, which masks a portion of the atmosphere. In this study, a deep learning model was developed to fill in missing data across these regions using surface radar and atmospheric state variables. The model accurately predicts reflectivity, with significant improvements over conventional methods. This innovative approach enhances our understanding of reflectivity patterns and atmospheric interactions, bolstering advances in remote snowfall prediction.
Abstract
Many Earth system models contain substantial biases in the magnitude and seasonal cycle of the albedo of snow-covered surfaces. Various structural and parametric deficiencies have been identified as potential causes of these albedo biases, related to vegetation distribution and abundance, snow albedo, and the representation of snow interception by forest canopies. There is, however, little understanding of how the albedo biases directly influence simulated climate because of difficulties in isolating them from other complex processes and feedbacks. In this study, we conduct a number of novel simulations using the National Center for Atmospheric Research Community Earth System Model (CESM), replacing the model’s internal surface albedo calculation with values prescribed from observations or from other model simulations. Results show that while biases in surface albedo are largest in winter, those during spring have the greatest impact on surface climate because incoming solar radiation is much stronger. Correcting biases in the seasonal cycle of albedo in CESM reduces climatological temperature biases across the boreal region in spring and partially corrects Arctic sea level pressure biases, but due to compensating errors, overall climate biases are not always reduced. Additionally, we impose albedo patterns extracted from other climate models with large positive and negative albedo biases to illustrate the climate responses that can result. Prescribed surface albedo produces significant impacts on surface radiation, near-surface land temperatures, and, more rarely, atmospheric circulation. This is important because small changes to mean climate during spring can have major implications for the snow and surface radiation regimes.
Abstract
Many Earth system models contain substantial biases in the magnitude and seasonal cycle of the albedo of snow-covered surfaces. Various structural and parametric deficiencies have been identified as potential causes of these albedo biases, related to vegetation distribution and abundance, snow albedo, and the representation of snow interception by forest canopies. There is, however, little understanding of how the albedo biases directly influence simulated climate because of difficulties in isolating them from other complex processes and feedbacks. In this study, we conduct a number of novel simulations using the National Center for Atmospheric Research Community Earth System Model (CESM), replacing the model’s internal surface albedo calculation with values prescribed from observations or from other model simulations. Results show that while biases in surface albedo are largest in winter, those during spring have the greatest impact on surface climate because incoming solar radiation is much stronger. Correcting biases in the seasonal cycle of albedo in CESM reduces climatological temperature biases across the boreal region in spring and partially corrects Arctic sea level pressure biases, but due to compensating errors, overall climate biases are not always reduced. Additionally, we impose albedo patterns extracted from other climate models with large positive and negative albedo biases to illustrate the climate responses that can result. Prescribed surface albedo produces significant impacts on surface radiation, near-surface land temperatures, and, more rarely, atmospheric circulation. This is important because small changes to mean climate during spring can have major implications for the snow and surface radiation regimes.
Abstract
The classical problem of predicting the atmospheric circulation response to extratropical surface forcing is revisited in the context of the observed connection between autumnal snow cover anomalies over Siberia and wintertime anomalies of the northern annular mode (NAM). Previous work has shown that in general circulation model (GCM) simulations in which autumnal Siberian snow forcing is prescribed, a vertically propagating Rossby wave train is generated that propagates into the stratosphere, drives dynamical stratospheric warming, and induces a negative NAM response that couples to the troposphere. Important questions remain regarding the dynamics of the response to this surface cooling. It is shown that previously unexplained aspects of the evolution of the response in a comprehensive GCM can be explained by examining the time evolution of the phasing, and hence the linear interference, between the Rossby wave response and the background climatological stationary wave. When the wave response and background wave are in phase, wave activity into the stratosphere is amplified and the zonal-mean stratosphere–troposphere NAM response displays a negative tendency; when they are out of phase, wave activity into the stratosphere is reduced and the NAM response displays a positive tendency. The effects of linear interference are probed further in a simplified GCM, where an imposed lower tropospheric cooling is varied in position, strength, and sign. As in the comprehensive GCM, linear interference strongly influences the response over a realistic range of forcing strengths. The transition from linear to nonlinear behavior is shown to depend simply on forcing strength.
Abstract
The classical problem of predicting the atmospheric circulation response to extratropical surface forcing is revisited in the context of the observed connection between autumnal snow cover anomalies over Siberia and wintertime anomalies of the northern annular mode (NAM). Previous work has shown that in general circulation model (GCM) simulations in which autumnal Siberian snow forcing is prescribed, a vertically propagating Rossby wave train is generated that propagates into the stratosphere, drives dynamical stratospheric warming, and induces a negative NAM response that couples to the troposphere. Important questions remain regarding the dynamics of the response to this surface cooling. It is shown that previously unexplained aspects of the evolution of the response in a comprehensive GCM can be explained by examining the time evolution of the phasing, and hence the linear interference, between the Rossby wave response and the background climatological stationary wave. When the wave response and background wave are in phase, wave activity into the stratosphere is amplified and the zonal-mean stratosphere–troposphere NAM response displays a negative tendency; when they are out of phase, wave activity into the stratosphere is reduced and the NAM response displays a positive tendency. The effects of linear interference are probed further in a simplified GCM, where an imposed lower tropospheric cooling is varied in position, strength, and sign. As in the comprehensive GCM, linear interference strongly influences the response over a realistic range of forcing strengths. The transition from linear to nonlinear behavior is shown to depend simply on forcing strength.
Abstract
Future climate warming is virtually certain to bring about an increase in the frequency of heat extremes. Highway design and pavement selection are based on a temperature regime that reflects the local climate zone. Increasing heat extremes could, therefore, shift some areas into a different performance grade (PG) for pavement, and more-heat-resistant materials are associated with increased infrastructure costs. This study combines observations, output from global climate models, and a statistical model to investigate changes in 20-yr return values of extreme maximum pavement temperature TPmax. From a multimodel range of simulated TPmax, future changes in PG are computed for 17 major Canadian cities. Relative to a 1981–2000 baseline, summertime Canada-wide warming of 1°–3°C is projected for 2041–70. As a result, climate change is likely to bring about profound changes to the spatial distribution of PG, with the severity of the changes directly linked to the severity of the projected warming. Even under weak simulated warming, an increase in PG is projected for greater Toronto, which is Canada’s largest urban area; under moderate (strong) warming 7 of 17 (9 of 17) major cities exhibit an increase. The influence of model spatial resolution is evaluated by comparing the results from global climate models with output from a set of regional climate models focused on North America. With the exception of mountainous terrain in western Canada, spatial resolution is not a major determining factor for projections of future PG changes.
Abstract
Future climate warming is virtually certain to bring about an increase in the frequency of heat extremes. Highway design and pavement selection are based on a temperature regime that reflects the local climate zone. Increasing heat extremes could, therefore, shift some areas into a different performance grade (PG) for pavement, and more-heat-resistant materials are associated with increased infrastructure costs. This study combines observations, output from global climate models, and a statistical model to investigate changes in 20-yr return values of extreme maximum pavement temperature TPmax. From a multimodel range of simulated TPmax, future changes in PG are computed for 17 major Canadian cities. Relative to a 1981–2000 baseline, summertime Canada-wide warming of 1°–3°C is projected for 2041–70. As a result, climate change is likely to bring about profound changes to the spatial distribution of PG, with the severity of the changes directly linked to the severity of the projected warming. Even under weak simulated warming, an increase in PG is projected for greater Toronto, which is Canada’s largest urban area; under moderate (strong) warming 7 of 17 (9 of 17) major cities exhibit an increase. The influence of model spatial resolution is evaluated by comparing the results from global climate models with output from a set of regional climate models focused on North America. With the exception of mountainous terrain in western Canada, spatial resolution is not a major determining factor for projections of future PG changes.
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 (R 2 = 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.
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 (R 2 = 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.
