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- Author or Editor: Andrew King x
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Abstract
El Niño-Southern Oscillation (ENSO) is the dominant source of climate variability globally. Many of the most devastating impacts of ENSO are felt through extremes. Here we present and describe a spatially complete global synthesis of extreme temperature and precipitation relationships with ENSO. We also investigate how these relationships evolve under a future warming scenario under high greenhouse gas emissions using fourteen models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble. Firstly, we demonstrate that models broadly capture observed ENSO teleconnections to means and extremes using the Twentieth Century Reanalysis version 3 (20CRv3). The models project that more regions will experience an amplification of the historical ENSO teleconnection with mean temperature and precipitation than a dampening under a high-emissions climate projection. The response of the ENSO teleconnection with extremes is very similar to the mean response, with even larger changes in some regions. Hence, regions that are predicted to experience an amplification of the ENSO teleconnection under future warming can also expect a comparable amplification in the intensity of extremes. Furthermore, models that suggest greater amplification of ENSO amplitude also tend to exhibit greater intensification of teleconnections. Future changes in regional climate variability may be better constrained if changes in ENSO itself are better understood.
Abstract
El Niño-Southern Oscillation (ENSO) is the dominant source of climate variability globally. Many of the most devastating impacts of ENSO are felt through extremes. Here we present and describe a spatially complete global synthesis of extreme temperature and precipitation relationships with ENSO. We also investigate how these relationships evolve under a future warming scenario under high greenhouse gas emissions using fourteen models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble. Firstly, we demonstrate that models broadly capture observed ENSO teleconnections to means and extremes using the Twentieth Century Reanalysis version 3 (20CRv3). The models project that more regions will experience an amplification of the historical ENSO teleconnection with mean temperature and precipitation than a dampening under a high-emissions climate projection. The response of the ENSO teleconnection with extremes is very similar to the mean response, with even larger changes in some regions. Hence, regions that are predicted to experience an amplification of the ENSO teleconnection under future warming can also expect a comparable amplification in the intensity of extremes. Furthermore, models that suggest greater amplification of ENSO amplitude also tend to exhibit greater intensification of teleconnections. Future changes in regional climate variability may be better constrained if changes in ENSO itself are better understood.
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
The term “new normal” has been used in scientific literature and public commentary to contextualize contemporary climate events as an indicator of a changing climate due to enhanced greenhouse warming. A new normal has been used broadly but tends to be descriptive and ambiguously defined. Here we review previous studies conceptualizing this idea of a new climatological normal and argue that this term should be used cautiously and with explicit definition in order to avoid confusion. We provide a formal definition of a new climate normal relative to present based around record-breaking contemporary events and explore the timing of when such extremes become statistically normal in the future model simulations. Applying this method to the record-breaking global-average 2015 temperatures as a reference event and a suite of model climate models, we determine that 2015 global annual-average temperatures will be the new normal by 2040 in all emissions scenarios. At the regional level, a new normal can be delayed through aggressive greenhouse gas emissions reductions. Using this specific case study to investigate a climatological new normal, our approach demonstrates the greater value of the concept of a climatological new normal for understanding and communicating climate change when the term is explicitly defined. This approach moves us one step closer to understanding how current extremes will change in the future in a warming world.
Abstract
The term “new normal” has been used in scientific literature and public commentary to contextualize contemporary climate events as an indicator of a changing climate due to enhanced greenhouse warming. A new normal has been used broadly but tends to be descriptive and ambiguously defined. Here we review previous studies conceptualizing this idea of a new climatological normal and argue that this term should be used cautiously and with explicit definition in order to avoid confusion. We provide a formal definition of a new climate normal relative to present based around record-breaking contemporary events and explore the timing of when such extremes become statistically normal in the future model simulations. Applying this method to the record-breaking global-average 2015 temperatures as a reference event and a suite of model climate models, we determine that 2015 global annual-average temperatures will be the new normal by 2040 in all emissions scenarios. At the regional level, a new normal can be delayed through aggressive greenhouse gas emissions reductions. Using this specific case study to investigate a climatological new normal, our approach demonstrates the greater value of the concept of a climatological new normal for understanding and communicating climate change when the term is explicitly defined. This approach moves us one step closer to understanding how current extremes will change in the future in a warming world.
Abstract
Convectively-coupled equatorial waves (CCEWs) with an off-equatorial convective center, such as Equatorial Rossby Waves (ER), Mixed-Rossby Gravity waves (MRG), and Tropical Depression-type Waves (TD), can be potential sources of predictability for sub-seasonal to seasonal prediction over northern Australia. To establish the statistical relationship of the wave-rainfall interaction, we investigate the influences of CCEWs on rainfall means and extremes during the Austral Summer (December-February) and Autumn (March-May) from 1981 to 2018. The results show that ER waves increase the average daily rainfall by up to 7 mm day−1 (4 mm day−1) during the austral summer (autumn) and increase the probability of extreme rainfall (above the 90th percentile) by around 1.5 - 2.4 times (summer) and 1.1 - 1.8 times (autumn) relative to climatology. MRG and TD-type waves are shown to have a smaller impact, increasing rainfall by around 1 - 4 mm day−1 (1 - 1.5 mm day−1 and 1 - 3 mm day−1) during the summer (autumn) and extreme probability by 1.4 - 1.6 times and 1.25 - 1.9 times (1.3 - 1.8 times and 1.27 - 1.7 times), respectively. The increase in rainfall can be attributed to the enhancement of moisture convergence during the time of the rainfall. Furthermore, moisture gain and enhancement of moisture advection were found ahead of the convective center. Additionally, we find that interactions between multiple waves can act to amplify or suppress the mean daily and probability of extreme rainfall. This research highlights the important role of CCEWs on northern Australian precipitation variability.
Abstract
Convectively-coupled equatorial waves (CCEWs) with an off-equatorial convective center, such as Equatorial Rossby Waves (ER), Mixed-Rossby Gravity waves (MRG), and Tropical Depression-type Waves (TD), can be potential sources of predictability for sub-seasonal to seasonal prediction over northern Australia. To establish the statistical relationship of the wave-rainfall interaction, we investigate the influences of CCEWs on rainfall means and extremes during the Austral Summer (December-February) and Autumn (March-May) from 1981 to 2018. The results show that ER waves increase the average daily rainfall by up to 7 mm day−1 (4 mm day−1) during the austral summer (autumn) and increase the probability of extreme rainfall (above the 90th percentile) by around 1.5 - 2.4 times (summer) and 1.1 - 1.8 times (autumn) relative to climatology. MRG and TD-type waves are shown to have a smaller impact, increasing rainfall by around 1 - 4 mm day−1 (1 - 1.5 mm day−1 and 1 - 3 mm day−1) during the summer (autumn) and extreme probability by 1.4 - 1.6 times and 1.25 - 1.9 times (1.3 - 1.8 times and 1.27 - 1.7 times), respectively. The increase in rainfall can be attributed to the enhancement of moisture convergence during the time of the rainfall. Furthermore, moisture gain and enhancement of moisture advection were found ahead of the convective center. Additionally, we find that interactions between multiple waves can act to amplify or suppress the mean daily and probability of extreme rainfall. This research highlights the important role of CCEWs on northern Australian precipitation variability.
Abstract
The large regional summer warming on the east coast of the northern Antarctic Peninsula (AP), which has taken place since the mid-1960s, has previously been proposed to be caused by a trend in the Southern Hemisphere Annular Mode (SAM). The authors utilize a high-resolution regional atmospheric model climatology (14-km grid spacing) to study the mechanisms that determine the response of the near-surface temperature to an increase in the SAM (ΔT/ΔSAM). Month-to-month variations in near-surface temperature and surface pressure are well represented by the model. It is found that north of ∼68°S, ΔT/ΔSAM is much larger on the eastern (lee) side than on the western (windward) side of the barrier. This is because of the enhanced westerly flow of relatively warm air over the barrier, which warms (and dries) further as it descends down the lee slope. The downward motion on the eastern side of the barrier causes a decrease in surface-mass balance and cloud cover. South of ∼68°S, vertical deflection across the barrier is greatly reduced and the contrast in ΔT/ΔSAM between the east and west sides of the barrier vanishes. In the northeastern part of the AP, the modeled ΔT/ΔSAM distribution is similar to the distribution derived from satellite infrared radiometer data. The region of strongest modeled temperature sensitivity to the SAM is where ice shelf collapse has recently taken place and does not extend farther south over the Larsen-C Ice Shelf.
Abstract
The large regional summer warming on the east coast of the northern Antarctic Peninsula (AP), which has taken place since the mid-1960s, has previously been proposed to be caused by a trend in the Southern Hemisphere Annular Mode (SAM). The authors utilize a high-resolution regional atmospheric model climatology (14-km grid spacing) to study the mechanisms that determine the response of the near-surface temperature to an increase in the SAM (ΔT/ΔSAM). Month-to-month variations in near-surface temperature and surface pressure are well represented by the model. It is found that north of ∼68°S, ΔT/ΔSAM is much larger on the eastern (lee) side than on the western (windward) side of the barrier. This is because of the enhanced westerly flow of relatively warm air over the barrier, which warms (and dries) further as it descends down the lee slope. The downward motion on the eastern side of the barrier causes a decrease in surface-mass balance and cloud cover. South of ∼68°S, vertical deflection across the barrier is greatly reduced and the contrast in ΔT/ΔSAM between the east and west sides of the barrier vanishes. In the northeastern part of the AP, the modeled ΔT/ΔSAM distribution is similar to the distribution derived from satellite infrared radiometer data. The region of strongest modeled temperature sensitivity to the SAM is where ice shelf collapse has recently taken place and does not extend farther south over the Larsen-C Ice Shelf.
Abstract
Since the mid-1960s, rapid regional summer warming has occurred on the east coast of the northern Antarctic Peninsula, with near-surface temperatures increasing by more than 2°C. This warming has contributed significantly to the collapse of the northern sections of the Larsen Ice Shelf. Coincident with this warming, the summer Southern Hemisphere Annular Mode (SAM) has exhibited a marked trend, suggested by modeling studies to be predominantly a response to anthropogenic forcing, resulting in increased westerlies across the northern peninsula.
Observations and reanalysis data are utilized to demonstrate that the changing SAM has played a key role in driving this local summer warming. It is proposed that the stronger summer westerly winds reduce the blocking effect of the Antarctic Peninsula and lead to a higher frequency of air masses being advected eastward over the orographic barrier of the northern Antarctic Peninsula. When this occurs, a combination of a climatological temperature gradient across the barrier and the formation of a föhn wind on the lee side typically results in a summer near-surface temperature sensitivity to the SAM that is 3 times greater on the eastern side of the peninsula than on the west. SAM variability is also shown to play a less important role in determining summer temperatures at stations west of the barrier in the northern peninsula (∼62°S), both at the surface and throughout the troposphere. This is in contrast to a station farther south (∼65°S) where the SAM exerts little influence.
Abstract
Since the mid-1960s, rapid regional summer warming has occurred on the east coast of the northern Antarctic Peninsula, with near-surface temperatures increasing by more than 2°C. This warming has contributed significantly to the collapse of the northern sections of the Larsen Ice Shelf. Coincident with this warming, the summer Southern Hemisphere Annular Mode (SAM) has exhibited a marked trend, suggested by modeling studies to be predominantly a response to anthropogenic forcing, resulting in increased westerlies across the northern peninsula.
Observations and reanalysis data are utilized to demonstrate that the changing SAM has played a key role in driving this local summer warming. It is proposed that the stronger summer westerly winds reduce the blocking effect of the Antarctic Peninsula and lead to a higher frequency of air masses being advected eastward over the orographic barrier of the northern Antarctic Peninsula. When this occurs, a combination of a climatological temperature gradient across the barrier and the formation of a föhn wind on the lee side typically results in a summer near-surface temperature sensitivity to the SAM that is 3 times greater on the eastern side of the peninsula than on the west. SAM variability is also shown to play a less important role in determining summer temperatures at stations west of the barrier in the northern peninsula (∼62°S), both at the surface and throughout the troposphere. This is in contrast to a station farther south (∼65°S) where the SAM exerts little influence.
Abstract
Studies of atmospheric rivers (ARs) over Australia have, so far, only focused on northwest cloudband–type weather systems. Here we perform a comprehensive analysis of AR climatology and impacts over Australia that includes not only northwesterly systems, but easterly and extratropical ARs also. We quantify the impact of ARs on mean and extreme rainfall including assessing how the origin location of ARs can alter their precipitation outcomes. We found a strong relationship between ARs and extreme rainfall in the agriculturally significant Murray–Daring basin region. We test the hypothesis that the tropical and subtropical originating ARs we observe in Australasia differ from canonical extratropical ARs by examining the vertical structure of ARs grouped by origin location. We found that in the moisture abundant tropics and subtropics, wind speed drives the intensity of ARs, while in the extratropics, the strength of an AR is largely determined by moisture availability. Finally, we examine the modulation of AR frequency by different climate modes. We find weak (but occasionally significant) correlations between ARs frequency and El Niño–Southern Oscillation, the Indian Ocean dipole, and the southern annular mode. However, there is a stronger relationship between the phases of the Madden–Julian oscillation and tropical AR frequency, which is an avenue for potential skill in forecasting ARs on subseasonal time scales.
Abstract
Studies of atmospheric rivers (ARs) over Australia have, so far, only focused on northwest cloudband–type weather systems. Here we perform a comprehensive analysis of AR climatology and impacts over Australia that includes not only northwesterly systems, but easterly and extratropical ARs also. We quantify the impact of ARs on mean and extreme rainfall including assessing how the origin location of ARs can alter their precipitation outcomes. We found a strong relationship between ARs and extreme rainfall in the agriculturally significant Murray–Daring basin region. We test the hypothesis that the tropical and subtropical originating ARs we observe in Australasia differ from canonical extratropical ARs by examining the vertical structure of ARs grouped by origin location. We found that in the moisture abundant tropics and subtropics, wind speed drives the intensity of ARs, while in the extratropics, the strength of an AR is largely determined by moisture availability. Finally, we examine the modulation of AR frequency by different climate modes. We find weak (but occasionally significant) correlations between ARs frequency and El Niño–Southern Oscillation, the Indian Ocean dipole, and the southern annular mode. However, there is a stronger relationship between the phases of the Madden–Julian oscillation and tropical AR frequency, which is an avenue for potential skill in forecasting ARs on subseasonal time scales.
