Abstract
The high-frequency (10–25-day, HF) boreal summer intraseasonal oscillation (BSISO) connects tightly to day-to-day weather and is inimitable in regulating extreme weather events. The western North Pacific (WNP) exhibits the most robust HF-BSISO variability worldwide, overshadowing the low-frequency (25–90-day) BSISO variability. Revealing the diversity of the HF-BSISO over the WNP area is crucial for understanding the BSISO physics and the Week Two-to-Four subseasonal forecast. This study objectively identified four types of HF-BSISO by cluster analysis of the (two-dimensional) propagation of OLR anomalies, including two northwestward-propagating patterns, with trajectories predominantly being located to the east and west of the Philippines, a westward-propagating Rossby wave train from the Philippine Sea to the Bay of Bengal, and a quasi-standing oscillation pattern centered around the Luzon Island. We have explored the propagation mechanisms of each archetype through a column-integrated moist static energy (MSE) budget diagnosis. Potential factors influencing the diverse propagation patterns are identified and compared. Basically, advections by the background flow predominantly drive the three propagating patterns, while the quasi-standing oscillation pattern is more sensitive to the surface turbulent heat fluxes. It is suggested that the diverse propagation patterns of the HF-BSISO over WNP could be impacted by the background states. However, the origins of these diversified propagation forms are complex and deserve further exploration.
Abstract
The high-frequency (10–25-day, HF) boreal summer intraseasonal oscillation (BSISO) connects tightly to day-to-day weather and is inimitable in regulating extreme weather events. The western North Pacific (WNP) exhibits the most robust HF-BSISO variability worldwide, overshadowing the low-frequency (25–90-day) BSISO variability. Revealing the diversity of the HF-BSISO over the WNP area is crucial for understanding the BSISO physics and the Week Two-to-Four subseasonal forecast. This study objectively identified four types of HF-BSISO by cluster analysis of the (two-dimensional) propagation of OLR anomalies, including two northwestward-propagating patterns, with trajectories predominantly being located to the east and west of the Philippines, a westward-propagating Rossby wave train from the Philippine Sea to the Bay of Bengal, and a quasi-standing oscillation pattern centered around the Luzon Island. We have explored the propagation mechanisms of each archetype through a column-integrated moist static energy (MSE) budget diagnosis. Potential factors influencing the diverse propagation patterns are identified and compared. Basically, advections by the background flow predominantly drive the three propagating patterns, while the quasi-standing oscillation pattern is more sensitive to the surface turbulent heat fluxes. It is suggested that the diverse propagation patterns of the HF-BSISO over WNP could be impacted by the background states. However, the origins of these diversified propagation forms are complex and deserve further exploration.
Abstract
This paper implements the “spatial first differences” (SFD) method to a Ricardian approach to analyze spatial differences in climate and evaluate their impact on farmland values in New Zealand. We use property valuation data from 1993 to 2018 across different agricultural land-uses. Ricardian analyses harness cross-sectional variation in climate and farmland values to estimate the effect of climate on agriculture. However, Ricardian studies are vulnerable to omitted variables varying slowly or not varying over time. The idea of the SFD approach is to apply spatial analogous of the “first-differences” estimator for longitudinal analyses to control for omitted variables that vary differently than climate variables among neighboring units. The results suggest that a warmer or drier climate is associated with higher farmland values in New Zealand. These findings may be mediated by productivity differences, costly physical improvements, and climate amenity values. Besides contributing novel insights by applying the Ricardian approach to New Zealand data and overcoming challenges of unobserved heterogeneity with the SFD method, we conclude that, for a better understanding of the welfare implications of climate change, it is crucial to differentiate between the effects of costly improvements and amenity values, apart from the pure climate productivity effect.
Abstract
This paper implements the “spatial first differences” (SFD) method to a Ricardian approach to analyze spatial differences in climate and evaluate their impact on farmland values in New Zealand. We use property valuation data from 1993 to 2018 across different agricultural land-uses. Ricardian analyses harness cross-sectional variation in climate and farmland values to estimate the effect of climate on agriculture. However, Ricardian studies are vulnerable to omitted variables varying slowly or not varying over time. The idea of the SFD approach is to apply spatial analogous of the “first-differences” estimator for longitudinal analyses to control for omitted variables that vary differently than climate variables among neighboring units. The results suggest that a warmer or drier climate is associated with higher farmland values in New Zealand. These findings may be mediated by productivity differences, costly physical improvements, and climate amenity values. Besides contributing novel insights by applying the Ricardian approach to New Zealand data and overcoming challenges of unobserved heterogeneity with the SFD method, we conclude that, for a better understanding of the welfare implications of climate change, it is crucial to differentiate between the effects of costly improvements and amenity values, apart from the pure climate productivity effect.
Abstract
Aerosols play a very important role in climate change with large uncertainties. Using the multi-model results from CMIP6, we analyzed the aerosol effective radiative forcing (ERF) and aerosol-induced surface air temperature (SAT) change in China in the present day (PD, 11-year mean of 2004–2014) relative to the pre-industrial time (PI, 11-year mean of 1850–1860). With the increase in the anthropogenic emissions, the simulated surface PM2.5 concentration and aerosol optical depth (AOD) averaged over eastern China (EC, 18–44°N, 103–122°E) increased by 21.43±7.58 μg m−3 and 0.47±0.33, respectively, from PI to PD. The simulated aerosol ERFs in EC were −4.91±2.56 and −5.35±2.40 W m−2 from equilibrium and transient simulations, respectively. The simulated change in SAT caused by the increases in aerosols was −1.37±0.38℃ in EC from PI to PD. The simulated values of equilibrium and transient climate sensitivity to aerosols (CSA, aerosol-induced SAT change per unit aerosol ERF) in EC were 0.236 and 0.222℃ (W m−2)−1, respectively. By using the observed AOD from MODIS to constrain aerosol ERF, the constrained aerosol equilibrium and transient ERFs over EC were −4.66 W m−2 and −4.93 W m−2, respectively, which were smaller in magnitude than the simulated values directly from the models. By using the observed SAT from the Climatic Research Unit temperature version 5 to constrain aerosol-induced cooling, the surface cooling caused by aerosols was magnified to −1.47℃. The adjusted CSA after the constraint was calculated by dividing adjusted aerosol-induced SAT change by adjusted aerosol ERF. Adjusted equilibrium and transient CSA values in EC were 0.32 and 0.34℃ (W m−2)−1, respectively.
Abstract
Aerosols play a very important role in climate change with large uncertainties. Using the multi-model results from CMIP6, we analyzed the aerosol effective radiative forcing (ERF) and aerosol-induced surface air temperature (SAT) change in China in the present day (PD, 11-year mean of 2004–2014) relative to the pre-industrial time (PI, 11-year mean of 1850–1860). With the increase in the anthropogenic emissions, the simulated surface PM2.5 concentration and aerosol optical depth (AOD) averaged over eastern China (EC, 18–44°N, 103–122°E) increased by 21.43±7.58 μg m−3 and 0.47±0.33, respectively, from PI to PD. The simulated aerosol ERFs in EC were −4.91±2.56 and −5.35±2.40 W m−2 from equilibrium and transient simulations, respectively. The simulated change in SAT caused by the increases in aerosols was −1.37±0.38℃ in EC from PI to PD. The simulated values of equilibrium and transient climate sensitivity to aerosols (CSA, aerosol-induced SAT change per unit aerosol ERF) in EC were 0.236 and 0.222℃ (W m−2)−1, respectively. By using the observed AOD from MODIS to constrain aerosol ERF, the constrained aerosol equilibrium and transient ERFs over EC were −4.66 W m−2 and −4.93 W m−2, respectively, which were smaller in magnitude than the simulated values directly from the models. By using the observed SAT from the Climatic Research Unit temperature version 5 to constrain aerosol-induced cooling, the surface cooling caused by aerosols was magnified to −1.47℃. The adjusted CSA after the constraint was calculated by dividing adjusted aerosol-induced SAT change by adjusted aerosol ERF. Adjusted equilibrium and transient CSA values in EC were 0.32 and 0.34℃ (W m−2)−1, respectively.
Abstract
There is strong evidence that the present-day Atlantic Meridional Overturning Circulation (AMOC) is in a bi-stable regime and hence it is important to determine probabilities and pathways for noise-induced transitions between its equilibrium states. Here, using Large Deviation Theory (LDT), the most probable transition pathways for the noise-induced collapse and recovery of the AMOC are computed in a stochastic box model of the World Ocean. This allows us to determine the physical mechanisms of noise-induced AMOC transitions. We show that the most likely path of an AMOC collapse starts paradoxically with a strengthening of the AMOC followed by an immediate drop within a couple of years due to a short but relatively strong freshwater pulse. The recovery on the other hand is a slow process, where the North Atlantic needs to be gradually salinified over a course of 20 years. The proposed method provides several benefits, including an estimate of probability ratios of collapse between various freshwater noise scenarios, showing that the AMOC is most vulnerable to freshwater forcing into the Atlantic thermocline region. Moreover, a comparison with a quasi-equilibrium approach reveals the contrasts in behavior of a bifurcation-induced and a noise-induced collapse of the AMOC.
Abstract
There is strong evidence that the present-day Atlantic Meridional Overturning Circulation (AMOC) is in a bi-stable regime and hence it is important to determine probabilities and pathways for noise-induced transitions between its equilibrium states. Here, using Large Deviation Theory (LDT), the most probable transition pathways for the noise-induced collapse and recovery of the AMOC are computed in a stochastic box model of the World Ocean. This allows us to determine the physical mechanisms of noise-induced AMOC transitions. We show that the most likely path of an AMOC collapse starts paradoxically with a strengthening of the AMOC followed by an immediate drop within a couple of years due to a short but relatively strong freshwater pulse. The recovery on the other hand is a slow process, where the North Atlantic needs to be gradually salinified over a course of 20 years. The proposed method provides several benefits, including an estimate of probability ratios of collapse between various freshwater noise scenarios, showing that the AMOC is most vulnerable to freshwater forcing into the Atlantic thermocline region. Moreover, a comparison with a quasi-equilibrium approach reveals the contrasts in behavior of a bifurcation-induced and a noise-induced collapse of the AMOC.
Abstract
Using the GECCO3 reanalysis data, this work explores the Indian Ocean Meridional Overturning Circulation (IMOC) variability and mechanism during the mature phase of the subtropical Indian Ocean dipole (IOSD). The IMOC is decomposed into the Ekman component, geostrophic component, external mode and residue. The IMOC exhibits counterclockwise circulation anomaly in 0 - 30°S during the mature phase of the IOSD. While the Ekman component dominates in 10°S - 30°S, the geostrophic component prevails in 5°S - 20°S. During the mature phase of the positive IOSD events, while an anticyclonic wind anomaly over the southern Indian Ocean causes a convergence and sinking of the sea water near 30°S, a cyclonic wind field anomaly near 10°S induces a divergence and rising, causing a counterclockwise Ekman component anomaly in 10°S - 30°S. The geostrophic component anomaly in 5°S - 20°S is caused by the sea level anomaly (SLA) gradient around 10°S related to a Rossby wave. A linear, 1D baroclinic Rossby wave model shows that the negative SLA in the west is caused by local and remote wind forcing, whereas the positive SLA in the east is generated by radiation from the eastern boundary and is slightly contributed by local wind forcing. Further Parallel Ocean Program (POP2) experiments confirmed that the Ekman component anomaly primarily responds to the wind field of the mature phase of the IOSD, and revealed that the geostrophic component anomaly is affected by the wind field of both the developing and mature phases of the IOSD.
Abstract
Using the GECCO3 reanalysis data, this work explores the Indian Ocean Meridional Overturning Circulation (IMOC) variability and mechanism during the mature phase of the subtropical Indian Ocean dipole (IOSD). The IMOC is decomposed into the Ekman component, geostrophic component, external mode and residue. The IMOC exhibits counterclockwise circulation anomaly in 0 - 30°S during the mature phase of the IOSD. While the Ekman component dominates in 10°S - 30°S, the geostrophic component prevails in 5°S - 20°S. During the mature phase of the positive IOSD events, while an anticyclonic wind anomaly over the southern Indian Ocean causes a convergence and sinking of the sea water near 30°S, a cyclonic wind field anomaly near 10°S induces a divergence and rising, causing a counterclockwise Ekman component anomaly in 10°S - 30°S. The geostrophic component anomaly in 5°S - 20°S is caused by the sea level anomaly (SLA) gradient around 10°S related to a Rossby wave. A linear, 1D baroclinic Rossby wave model shows that the negative SLA in the west is caused by local and remote wind forcing, whereas the positive SLA in the east is generated by radiation from the eastern boundary and is slightly contributed by local wind forcing. Further Parallel Ocean Program (POP2) experiments confirmed that the Ekman component anomaly primarily responds to the wind field of the mature phase of the IOSD, and revealed that the geostrophic component anomaly is affected by the wind field of both the developing and mature phases of the IOSD.
Abstract
Understanding convective aggregation is very important for understanding tropical climate and climate sensitivity. However, we still lack a full understanding of how aggregation evolves in the real world or what phenomena and scales are analogous to the self-aggregation observed in idealized models. In this study, we apply the moist static energy (MSE) variance budget framework to ERA5 reanalysis data to study the evolution of large-scale aggregation over tropical oceans at basin wide scales. Our novel phase space diagnostics focus on the variability of observed aggregation compared to most previous self-aggregation studies, which focus more on the aggregated mean state. We visualize observed aggregation to evolve anomalously around a mean state in a cyclical fashion forming aggregation - disaggregation cycles. We find horizontal advection of MSE to play the primary role in determining when the domain aggregates or disaggregates. In contrast, all advective, radiative and surface flux feedbacks are found important for determining the magnitude of the aggregation anomalies. Surface fluxes and horizontal advection tend to dampen aggregation anomalies, while radiative fluxes and vertical advection tend to amplify aggregation anomalies. Looking deeper into the advection terms, we find that changes in vertical advection are dominated by an enhanced low level subsidence over the dry regions during the more aggregated states. This creates an anomalous drying tendency over the dry regions, which maintains aggregation anomalies. In contrast, horizontal advection changes are found to be dominated by increased moisture advection out of the moist columns with stronger aggregation.
Abstract
Understanding convective aggregation is very important for understanding tropical climate and climate sensitivity. However, we still lack a full understanding of how aggregation evolves in the real world or what phenomena and scales are analogous to the self-aggregation observed in idealized models. In this study, we apply the moist static energy (MSE) variance budget framework to ERA5 reanalysis data to study the evolution of large-scale aggregation over tropical oceans at basin wide scales. Our novel phase space diagnostics focus on the variability of observed aggregation compared to most previous self-aggregation studies, which focus more on the aggregated mean state. We visualize observed aggregation to evolve anomalously around a mean state in a cyclical fashion forming aggregation - disaggregation cycles. We find horizontal advection of MSE to play the primary role in determining when the domain aggregates or disaggregates. In contrast, all advective, radiative and surface flux feedbacks are found important for determining the magnitude of the aggregation anomalies. Surface fluxes and horizontal advection tend to dampen aggregation anomalies, while radiative fluxes and vertical advection tend to amplify aggregation anomalies. Looking deeper into the advection terms, we find that changes in vertical advection are dominated by an enhanced low level subsidence over the dry regions during the more aggregated states. This creates an anomalous drying tendency over the dry regions, which maintains aggregation anomalies. In contrast, horizontal advection changes are found to be dominated by increased moisture advection out of the moist columns with stronger aggregation.
Abstract
High-latitudes, including the Bering Sea, are experiencing unprecedented rates of change. Long-term Bering Sea warming trends have been identified, and marine heatwaves (MHWs), event-scale elevated sea surface temperature (SST) extremes, have also increased in frequency and longevity in recent years. Recent work has shown that variability in air-sea coupling plays a dominant role in driving Bering Sea upper ocean thermal variability, and that surface forcing has driven an increase in the occurrence of positive ocean temperature anomalies since 2010. In this work, we characterize the drivers of the anomalous surface air-sea heat fluxes in the Bering Sea over the period 2010-2022 using ERA5 fields. We show that the surface turbulent heat flux dominates the net surface heat flux variability from September-April, and is primarily a result of near-surface air temperature and specific humidity anomalies. The air-mass anomalies that account for the majority of the turbulent heat flux variability are a function of wind direction, with southerly (northerly) wind advecting anomalously warm (cool), moist (dry) air over the Bering Sea, resulting in positive (negative) surface turbulent flux anomalies. During the remaining months of the year, anomalies in the surface radiative fluxes account for the majority of the net surface heat flux variability, and are a result of anomalous cloud coverage, anomalous lower tropospheric virtual temperature, and sea ice coverage variability. Our results indicate that atmospheric variability drives much of the Bering Sea upper ocean temperature variability through mediation of the surface heat fluxes during the analysis period.
Abstract
High-latitudes, including the Bering Sea, are experiencing unprecedented rates of change. Long-term Bering Sea warming trends have been identified, and marine heatwaves (MHWs), event-scale elevated sea surface temperature (SST) extremes, have also increased in frequency and longevity in recent years. Recent work has shown that variability in air-sea coupling plays a dominant role in driving Bering Sea upper ocean thermal variability, and that surface forcing has driven an increase in the occurrence of positive ocean temperature anomalies since 2010. In this work, we characterize the drivers of the anomalous surface air-sea heat fluxes in the Bering Sea over the period 2010-2022 using ERA5 fields. We show that the surface turbulent heat flux dominates the net surface heat flux variability from September-April, and is primarily a result of near-surface air temperature and specific humidity anomalies. The air-mass anomalies that account for the majority of the turbulent heat flux variability are a function of wind direction, with southerly (northerly) wind advecting anomalously warm (cool), moist (dry) air over the Bering Sea, resulting in positive (negative) surface turbulent flux anomalies. During the remaining months of the year, anomalies in the surface radiative fluxes account for the majority of the net surface heat flux variability, and are a result of anomalous cloud coverage, anomalous lower tropospheric virtual temperature, and sea ice coverage variability. Our results indicate that atmospheric variability drives much of the Bering Sea upper ocean temperature variability through mediation of the surface heat fluxes during the analysis period.
Abstract
Synoptic-scale vortices known as monsoon low pressure systems (LPSs) frequently produce intense precipitation and hydrological disasters in South Asia, so accurately forecasting LPS genesis is crucial for improving disaster preparedness and response. However, the accuracy of LPS genesis forecasts by numerical weather prediction models has remained unknown. Here, we evaluate the performance of two global ensemble models—the U.S. Global Ensemble Forecast System (GEFS) and the Ensemble Prediction System of the European Centre for Medium-Range Weather Forecasts (ECMWF)—in predicting LPS genesis during the years 2021–22. The GEFS successfully predicted about half the observed LPS genesis events 1–2 days in advance; the ECMWF model captured an additional 10% of observed genesis events. Both models had a false alarm ratio (FAR) of around 50% for 1–2-day lead times. In both ensembles, the control run typically exhibited a higher probability of detection (POD) of observed events and a lower FAR compared to the perturbed ensemble members. However, a consensus forecast, in which genesis is predicted when at least 20% of ensemble members forecast LPS formation, had POD values surpassing those of the control run for all lead times. Moreover, probabilistic predictions of genesis over the Bay of Bengal, where most LPSs form, were skillful, with the fraction of ensemble members predicting LPS formation over a 5-day lead time approximating the observed frequency of genesis, without any adjustment or bias correction.
Abstract
Synoptic-scale vortices known as monsoon low pressure systems (LPSs) frequently produce intense precipitation and hydrological disasters in South Asia, so accurately forecasting LPS genesis is crucial for improving disaster preparedness and response. However, the accuracy of LPS genesis forecasts by numerical weather prediction models has remained unknown. Here, we evaluate the performance of two global ensemble models—the U.S. Global Ensemble Forecast System (GEFS) and the Ensemble Prediction System of the European Centre for Medium-Range Weather Forecasts (ECMWF)—in predicting LPS genesis during the years 2021–22. The GEFS successfully predicted about half the observed LPS genesis events 1–2 days in advance; the ECMWF model captured an additional 10% of observed genesis events. Both models had a false alarm ratio (FAR) of around 50% for 1–2-day lead times. In both ensembles, the control run typically exhibited a higher probability of detection (POD) of observed events and a lower FAR compared to the perturbed ensemble members. However, a consensus forecast, in which genesis is predicted when at least 20% of ensemble members forecast LPS formation, had POD values surpassing those of the control run for all lead times. Moreover, probabilistic predictions of genesis over the Bay of Bengal, where most LPSs form, were skillful, with the fraction of ensemble members predicting LPS formation over a 5-day lead time approximating the observed frequency of genesis, without any adjustment or bias correction.
Abstract
The St. Lawrence River Valley experiences a variety of precipitation types (p-types) during the cold season, such as rain, freezing rain, ice pellets, and snow. These varied precipitation types exert considerable impacts on aviation, road transportation, power generation and distribution, and winter recreation and are shaped by diverse multiscale processes that interact with the region’s complex topography. This study utilizes ERA5 reanalysis data, surface cyclone climatology, and hourly station observations from Montréal, Québec, and Burlington, Vermont, during October–April 2000–18 to investigate the spectrum of synoptic-scale weather regimes that induce cold-season precipitation across the St. Lawrence River Valley. In particular, k-means clustering and self-organizing maps (SOMs) are used to classify cyclone tracks passing near the St. Lawrence River Valley, and their accompanying thermodynamic profiles, into a set of event types that include a U.S. East Coast track, a central U.S. track, and two Canadian clipper tracks. Composite analyses are subsequently performed to reveal the synoptic-scale environments and the characteristic p-types that most frequently accompany each event type. Global Ensemble Forecast System version 12 (GEFSv12) reforecasts are then used to examine the relative predictability of cyclone characteristics and the local thermodynamic profile associated with each event type at 0–5-day forecast lead times. The analysis suggests that forecasted cyclones near the St. Lawrence River Valley develop too quickly and are located left-of-track relative to the reanalysis on average, which has implications for forecasts of the local thermodynamic profile and p-type across the region when the temperature is near 0°C.
Significance Statement
Diverse precipitation types are observed when near-surface temperatures approach 0°C during the cold season, especially across the St. Lawrence River Valley in southern Québec. This study classifies cold-season precipitation events impacting the St. Lawrence River Valley based on the track of storm systems across the region and quantifies the average meteorological characteristics and predictability of each track. Our analysis reveals that forecasted low pressure systems develop too quickly and are left of their observed track 0–5 days prior to an event on average, which has implications for forecasted temperatures and the type of precipitation observed across the region. Our results can inform future operational forecasts of cold-season precipitation events by providing a storm-focused perspective on forecast errors during these impactful events.
Abstract
The St. Lawrence River Valley experiences a variety of precipitation types (p-types) during the cold season, such as rain, freezing rain, ice pellets, and snow. These varied precipitation types exert considerable impacts on aviation, road transportation, power generation and distribution, and winter recreation and are shaped by diverse multiscale processes that interact with the region’s complex topography. This study utilizes ERA5 reanalysis data, surface cyclone climatology, and hourly station observations from Montréal, Québec, and Burlington, Vermont, during October–April 2000–18 to investigate the spectrum of synoptic-scale weather regimes that induce cold-season precipitation across the St. Lawrence River Valley. In particular, k-means clustering and self-organizing maps (SOMs) are used to classify cyclone tracks passing near the St. Lawrence River Valley, and their accompanying thermodynamic profiles, into a set of event types that include a U.S. East Coast track, a central U.S. track, and two Canadian clipper tracks. Composite analyses are subsequently performed to reveal the synoptic-scale environments and the characteristic p-types that most frequently accompany each event type. Global Ensemble Forecast System version 12 (GEFSv12) reforecasts are then used to examine the relative predictability of cyclone characteristics and the local thermodynamic profile associated with each event type at 0–5-day forecast lead times. The analysis suggests that forecasted cyclones near the St. Lawrence River Valley develop too quickly and are located left-of-track relative to the reanalysis on average, which has implications for forecasts of the local thermodynamic profile and p-type across the region when the temperature is near 0°C.
Significance Statement
Diverse precipitation types are observed when near-surface temperatures approach 0°C during the cold season, especially across the St. Lawrence River Valley in southern Québec. This study classifies cold-season precipitation events impacting the St. Lawrence River Valley based on the track of storm systems across the region and quantifies the average meteorological characteristics and predictability of each track. Our analysis reveals that forecasted low pressure systems develop too quickly and are left of their observed track 0–5 days prior to an event on average, which has implications for forecasted temperatures and the type of precipitation observed across the region. Our results can inform future operational forecasts of cold-season precipitation events by providing a storm-focused perspective on forecast errors during these impactful events.
