Abstract
Sea surface height observations provided by satellite altimetry since 1993 show a rising rate (3.4 mm/year) for global mean sea level. While on average, sea level has risen 10 cm over the last 30 years, there is considerable regional variation in the sea level change. Through this work, we predict sea level trends 30 years into the future at a 2-degree spatial resolution and investigate the future patterns of the sea level change. We show the potential of machine learning (ML) in this challenging application of long-term sea level forecasting over the global ocean. Our approach incorporates sea level data from both altimeter observations and climate model simulations. We develop a supervised learning framework using fully connected neural networks (FCNNs) that can predict the sea level trend based on climate model projections. Alongside this, our method provides uncertainty estimates associated with the ML prediction. We also show the effectiveness of partitioning our spatial dataset and learning a dedicated ML model for each segmented region. We compare two partitioning strategies: one achieved using domain knowledge, and the other employing spectral clustering. Our results demonstrate that segmenting the spatial dataset with spectral clustering improves the ML predictions.
Abstract
Sea surface height observations provided by satellite altimetry since 1993 show a rising rate (3.4 mm/year) for global mean sea level. While on average, sea level has risen 10 cm over the last 30 years, there is considerable regional variation in the sea level change. Through this work, we predict sea level trends 30 years into the future at a 2-degree spatial resolution and investigate the future patterns of the sea level change. We show the potential of machine learning (ML) in this challenging application of long-term sea level forecasting over the global ocean. Our approach incorporates sea level data from both altimeter observations and climate model simulations. We develop a supervised learning framework using fully connected neural networks (FCNNs) that can predict the sea level trend based on climate model projections. Alongside this, our method provides uncertainty estimates associated with the ML prediction. We also show the effectiveness of partitioning our spatial dataset and learning a dedicated ML model for each segmented region. We compare two partitioning strategies: one achieved using domain knowledge, and the other employing spectral clustering. Our results demonstrate that segmenting the spatial dataset with spectral clustering improves the ML predictions.
Abstract
The Heat Index (HI), based on Steadman's model of thermoregulation, estimates heat stress on the human body from ambient temperature and humidity. It has been used widely both in applications, such as the issuance of heat advisories by the National Weather Service (NWS), and as well for research on possible changes in the future due to climate change. However, temperature/humidity combinations that exceed the applicable range of the model are becoming more common due to climate warming. Recent work by Lu and Romps has produced an Extended Heat Index (EHI) which is valid for values outside the range of the original HI. For these values the HI can underestimate the EHI by a considerable amount. This work utilizes observed data from 15 US weather stations along with bias-adjusted output from a climate model to explore the spatial and temporal aspects of the disparity between the HI and the EHI from the recent past out to the end of the 21st century. The underestimate of human heat stress by the HI is found to be largest for the most extreme cases (∼ 5-10° C), which are also the most impactful. Conditions warranting NWS excessive heat warnings are found to increase dramatically from less than 5% of days historically at most stations to more than 90% in the future at some stations. Although, by design, the scope of this work is limited, it demonstrates the need for the adoption of the EHI for both applications and research.
Abstract
The Heat Index (HI), based on Steadman's model of thermoregulation, estimates heat stress on the human body from ambient temperature and humidity. It has been used widely both in applications, such as the issuance of heat advisories by the National Weather Service (NWS), and as well for research on possible changes in the future due to climate change. However, temperature/humidity combinations that exceed the applicable range of the model are becoming more common due to climate warming. Recent work by Lu and Romps has produced an Extended Heat Index (EHI) which is valid for values outside the range of the original HI. For these values the HI can underestimate the EHI by a considerable amount. This work utilizes observed data from 15 US weather stations along with bias-adjusted output from a climate model to explore the spatial and temporal aspects of the disparity between the HI and the EHI from the recent past out to the end of the 21st century. The underestimate of human heat stress by the HI is found to be largest for the most extreme cases (∼ 5-10° C), which are also the most impactful. Conditions warranting NWS excessive heat warnings are found to increase dramatically from less than 5% of days historically at most stations to more than 90% in the future at some stations. Although, by design, the scope of this work is limited, it demonstrates the need for the adoption of the EHI for both applications and research.
Abstract
The Indochinese Peninsula experiences a dry season with extensive biomass burning peaking from March to April. As the monsoon arrives, rainfall significantly removes aerosols through deposition, ending the emission season. However, the biomass burning aerosols exert an influence on the atmospheric circulation prior to the monsoon onset. This study employed statistical methods and a regional atmosphere-chemistry model (WRF-Chem) to investigate the delayed impact of biomass burning from the Indochinese Peninsula on the monsoon onset. The results indicate that the cold sea surface temperature anomaly caused by the aerosols during the emission season can be stored in the ocean and inhibit convective activities over the adjacent sea regions in the post-emission season, suppressing the southwestward cross-equatorial flow over the southern Bay of Bengal. This suppression delays the westward extension and northward shift of the upper-level South Asian high-pressure system, along with its divergence and subsidence effects, thereby postponing the breakdown and retreat of the subtropical high-pressure belt. Simultaneously, the cold sea temperature also suppresses the development of a warm pool in the southeastern Bay of Bengal, which is associated with the generation of a monsoon onset vortex. Consequently, the onset of the Bay of Bengal monsoon is delayed. Due to the decreasing delayed effects of aerosols over time and the counteractive warming from the accelerated abnormal anticyclonic circulation in the upper Bay of Bengal, which results in accelerated sea surface warming, the delayed influence of aerosols diminishes gradually after the onset of the Bay of Bengal monsoon until it disappears.
Abstract
The Indochinese Peninsula experiences a dry season with extensive biomass burning peaking from March to April. As the monsoon arrives, rainfall significantly removes aerosols through deposition, ending the emission season. However, the biomass burning aerosols exert an influence on the atmospheric circulation prior to the monsoon onset. This study employed statistical methods and a regional atmosphere-chemistry model (WRF-Chem) to investigate the delayed impact of biomass burning from the Indochinese Peninsula on the monsoon onset. The results indicate that the cold sea surface temperature anomaly caused by the aerosols during the emission season can be stored in the ocean and inhibit convective activities over the adjacent sea regions in the post-emission season, suppressing the southwestward cross-equatorial flow over the southern Bay of Bengal. This suppression delays the westward extension and northward shift of the upper-level South Asian high-pressure system, along with its divergence and subsidence effects, thereby postponing the breakdown and retreat of the subtropical high-pressure belt. Simultaneously, the cold sea temperature also suppresses the development of a warm pool in the southeastern Bay of Bengal, which is associated with the generation of a monsoon onset vortex. Consequently, the onset of the Bay of Bengal monsoon is delayed. Due to the decreasing delayed effects of aerosols over time and the counteractive warming from the accelerated abnormal anticyclonic circulation in the upper Bay of Bengal, which results in accelerated sea surface warming, the delayed influence of aerosols diminishes gradually after the onset of the Bay of Bengal monsoon until it disappears.
Abstract
An increasing number of studies have recognized the essential influence of surface potential vorticity (PV) forcing on atmospheric circulation. In this study, we investigated the temporal characteristics of global surface PV forcing in January and its associated climate anomalies. The global surface PV forcing exhibited a pronounced decreasing trend, implying reduced forcing on atmospheric PV. Its interannual component was accompanied by cold winters on the Eurasian and North American continents and long-persisting droughts in Southwest China (SWC), consistent with the coexistence of these extreme events. Based on the global surface PV forcing index, the mechanism underlying long-persisting droughts, which lasted from October to January, was investigated. The formation mechanisms of persistent drought varied monthly. Specifically, the occurrence of drought in October was closely related to Rossby wave activity over Eurasia, which enhanced the anomalous anticyclone over the Tibetan Plateau and subsequently induced air descent over SWC. In contrast, drought in November and December could be ascribed to La Niña events in the central Pacific, which facilitated subsidence over SWC through local meridional circulation anomalies. Distinct from other months, the combined effects of La Niña events and circulation anomalies over northern Eurasia caused the drought in January. The former reduced precipitation over southern SWC, whereas the latter influenced precipitation over central SWC. The present study provides novel insights into simultaneous extreme events in the Northern Hemisphere.
Abstract
An increasing number of studies have recognized the essential influence of surface potential vorticity (PV) forcing on atmospheric circulation. In this study, we investigated the temporal characteristics of global surface PV forcing in January and its associated climate anomalies. The global surface PV forcing exhibited a pronounced decreasing trend, implying reduced forcing on atmospheric PV. Its interannual component was accompanied by cold winters on the Eurasian and North American continents and long-persisting droughts in Southwest China (SWC), consistent with the coexistence of these extreme events. Based on the global surface PV forcing index, the mechanism underlying long-persisting droughts, which lasted from October to January, was investigated. The formation mechanisms of persistent drought varied monthly. Specifically, the occurrence of drought in October was closely related to Rossby wave activity over Eurasia, which enhanced the anomalous anticyclone over the Tibetan Plateau and subsequently induced air descent over SWC. In contrast, drought in November and December could be ascribed to La Niña events in the central Pacific, which facilitated subsidence over SWC through local meridional circulation anomalies. Distinct from other months, the combined effects of La Niña events and circulation anomalies over northern Eurasia caused the drought in January. The former reduced precipitation over southern SWC, whereas the latter influenced precipitation over central SWC. The present study provides novel insights into simultaneous extreme events in the Northern Hemisphere.
Abstract
Artificial intelligence and machine learning (AI/ML) have attracted a great deal of attention from the atmospheric science community. The explosion of attention on AI/ML development carries implications for the operational community, prompting questions about how novel AI/ML advancements will translate from research into operations. However, the field lacks empirical evidence on how National Weather Service (NWS) forecasters, as key intended users, perceive AI/ML and its use in operational forecasting. This study addresses this crucial gap through structured interviews conducted with 29 NWS forecasters, from October 2021 through July 2023 in which we explored their perceptions of AI/ML in forecasting. We found that forecasters generally prefer the term “machine learning” over “artificial intelligence” and that labeling a product as being AI/ML did not hurt perceptions of the products and made some forecasters more excited about the product. Forecasters also had a wide range of familiarity with AI/ML, and overall they were (tentatively) open to the use of AI/ML in forecasting. We also provide examples of specific areas related to AI/ML that forecasters are excited or hopeful about and that they are concerned or worried about. One concern that was raised in several ways was that AI/ML could replace forecasters or remove them from the forecasting process. However, forecasters expressed a widespread and deep commitment to the best possible forecasts and services to uphold the agency mission using whatever tools or products that are available to assist them. Lastly, we note how forecasters’ perceptions evolved over the course of the study.
Abstract
Artificial intelligence and machine learning (AI/ML) have attracted a great deal of attention from the atmospheric science community. The explosion of attention on AI/ML development carries implications for the operational community, prompting questions about how novel AI/ML advancements will translate from research into operations. However, the field lacks empirical evidence on how National Weather Service (NWS) forecasters, as key intended users, perceive AI/ML and its use in operational forecasting. This study addresses this crucial gap through structured interviews conducted with 29 NWS forecasters, from October 2021 through July 2023 in which we explored their perceptions of AI/ML in forecasting. We found that forecasters generally prefer the term “machine learning” over “artificial intelligence” and that labeling a product as being AI/ML did not hurt perceptions of the products and made some forecasters more excited about the product. Forecasters also had a wide range of familiarity with AI/ML, and overall they were (tentatively) open to the use of AI/ML in forecasting. We also provide examples of specific areas related to AI/ML that forecasters are excited or hopeful about and that they are concerned or worried about. One concern that was raised in several ways was that AI/ML could replace forecasters or remove them from the forecasting process. However, forecasters expressed a widespread and deep commitment to the best possible forecasts and services to uphold the agency mission using whatever tools or products that are available to assist them. Lastly, we note how forecasters’ perceptions evolved over the course of the study.
Abstract
We introduce a quasi-analytical model of thermally-induced flows in valleys with sloping floors, a feature absent from most theoretical valley wind studies. One of the main theories for valley winds – the valley volume effect – emerged from field studies in the European Alps in the 1930s and 1940s. According to that theory, along-valley variations in the heating rate arising from variations in valley geometry generated the pressure gradient that drove the valley wind. However, while those early studies were conducted in valleys with relatively flat (horizontal) floors, valleys with sloping floors are ubiquitous and presumably affected directly by slope buoyancy (Prandtl mechanism). Our model is developed for the Prandtl setting of steady flow of a stably stratified fluid over a heated planar slope, but with the slope replaced by a periodic system of sloping valleys. As the valley characteristics do not change along the valley, there is no valley volume effect. The 2D linearized Boussinesq governing equations are solved using Fourier methods. Examples are explored for symmetric (with respect to valley axis) valleys subject to symmetric and antisymmetric heating. The flows are 2D, but the trajectories are intrinsically 3D. For symmetric heating, trajectories are of two types: (i) helical trajectories of parcels trapped within one of two counter-rotating vortices straddling the valley axis, and (ii) trajectories of environmental parcels that approach the valley horizontally, move under and then over the helical trajectories, and then return to the environment. For antisymmetric heating, three types of trajectories are identified.
Abstract
We introduce a quasi-analytical model of thermally-induced flows in valleys with sloping floors, a feature absent from most theoretical valley wind studies. One of the main theories for valley winds – the valley volume effect – emerged from field studies in the European Alps in the 1930s and 1940s. According to that theory, along-valley variations in the heating rate arising from variations in valley geometry generated the pressure gradient that drove the valley wind. However, while those early studies were conducted in valleys with relatively flat (horizontal) floors, valleys with sloping floors are ubiquitous and presumably affected directly by slope buoyancy (Prandtl mechanism). Our model is developed for the Prandtl setting of steady flow of a stably stratified fluid over a heated planar slope, but with the slope replaced by a periodic system of sloping valleys. As the valley characteristics do not change along the valley, there is no valley volume effect. The 2D linearized Boussinesq governing equations are solved using Fourier methods. Examples are explored for symmetric (with respect to valley axis) valleys subject to symmetric and antisymmetric heating. The flows are 2D, but the trajectories are intrinsically 3D. For symmetric heating, trajectories are of two types: (i) helical trajectories of parcels trapped within one of two counter-rotating vortices straddling the valley axis, and (ii) trajectories of environmental parcels that approach the valley horizontally, move under and then over the helical trajectories, and then return to the environment. For antisymmetric heating, three types of trajectories are identified.
Abstract
The THINICE field campaign, based from Svalbard in August 2022, provided unique observations of summertime Arctic cyclones, their coupling with cloud cover, and interactions with tropopause polar vortices and sea ice conditions. THINICE was motivated by the need to advance our understanding of these processes and to improve coupled models used to forecast weather and sea ice, as well as long-term projections of climate change in the Arctic. Two research aircraft were deployed with complementary instrumentation. The Safire ATR42 aircraft, equipped with the RALI (RAdar-LIdar) remote sensing instrumentation and in-situ cloud microphysics probes, flew in the mid-troposphere to observe the wind and multi-phase cloud structure of Arctic cyclones. The British Antarctic Survey MASIN aircraft flew at low levels measuring sea-ice properties, including surface brightness temperature, albedo and roughness, and the turbulent fluxes that mediate exchange of heat and momentum between the atmosphere and the surface. Long duration instrumented balloons, operated by WindBorne Systems, sampled meteorological conditions within both cyclones and tropospheric polar vortices across the Arctic. Several novel findings are highlighted. Intense, shallow low-level jets along warm fronts were observed within three Arctic cyclones using the Doppler radar and turbulence probes. A detailed depiction of the interweaving layers of ice crystals and supercooled liquid water in mixed-phase clouds is revealed through the synergistic combination of the Doppler radar, the lidar and in-situ microphysical probes. Measurements of near-surface turbulent fluxes combined with remote sensing measurements of sea ice properties are being used to characterize atmosphere-sea ice interactions in the marginal ice zone.
Abstract
The THINICE field campaign, based from Svalbard in August 2022, provided unique observations of summertime Arctic cyclones, their coupling with cloud cover, and interactions with tropopause polar vortices and sea ice conditions. THINICE was motivated by the need to advance our understanding of these processes and to improve coupled models used to forecast weather and sea ice, as well as long-term projections of climate change in the Arctic. Two research aircraft were deployed with complementary instrumentation. The Safire ATR42 aircraft, equipped with the RALI (RAdar-LIdar) remote sensing instrumentation and in-situ cloud microphysics probes, flew in the mid-troposphere to observe the wind and multi-phase cloud structure of Arctic cyclones. The British Antarctic Survey MASIN aircraft flew at low levels measuring sea-ice properties, including surface brightness temperature, albedo and roughness, and the turbulent fluxes that mediate exchange of heat and momentum between the atmosphere and the surface. Long duration instrumented balloons, operated by WindBorne Systems, sampled meteorological conditions within both cyclones and tropospheric polar vortices across the Arctic. Several novel findings are highlighted. Intense, shallow low-level jets along warm fronts were observed within three Arctic cyclones using the Doppler radar and turbulence probes. A detailed depiction of the interweaving layers of ice crystals and supercooled liquid water in mixed-phase clouds is revealed through the synergistic combination of the Doppler radar, the lidar and in-situ microphysical probes. Measurements of near-surface turbulent fluxes combined with remote sensing measurements of sea ice properties are being used to characterize atmosphere-sea ice interactions in the marginal ice zone.
Abstract
A series of papers published since 1998 asserts that US Tropical-Cyclone (TC) damage, when “normalized” for individual wealth, population, and inflation, exhibits no increase attributable to AGW (Anthropogenic Global Warming). This result is here questioned for three reasons: 1) The then-year (no demographic or economic adjustments) US TC damage increases 2.5% per year faster than US then-year Gross Domestic Product. This result, which is substantially due to faster growth of assets in hurricane-prone states, shows that TC impacts on the total US economy double every generation. 2) Fitting of an exponential curve to normalized damage binned by 5-year “pentads” yields a growth rate of 1.06% yr−1 since 1900, although causes besides AGW may contribute. 3) During the 21st century, when the Atlantic Multidecadal Oscillation (AMO) was in its warm phase, the most-damaging US TCs struck at twice the rate of the warm AMO of the 20th century and four times the rate of the entire 20th century, both warm and cool AMO phases.
A key unanswered question is: What will happen when (and if) the AMO returns to its cool phase later in this century?
Abstract
A series of papers published since 1998 asserts that US Tropical-Cyclone (TC) damage, when “normalized” for individual wealth, population, and inflation, exhibits no increase attributable to AGW (Anthropogenic Global Warming). This result is here questioned for three reasons: 1) The then-year (no demographic or economic adjustments) US TC damage increases 2.5% per year faster than US then-year Gross Domestic Product. This result, which is substantially due to faster growth of assets in hurricane-prone states, shows that TC impacts on the total US economy double every generation. 2) Fitting of an exponential curve to normalized damage binned by 5-year “pentads” yields a growth rate of 1.06% yr−1 since 1900, although causes besides AGW may contribute. 3) During the 21st century, when the Atlantic Multidecadal Oscillation (AMO) was in its warm phase, the most-damaging US TCs struck at twice the rate of the warm AMO of the 20th century and four times the rate of the entire 20th century, both warm and cool AMO phases.
A key unanswered question is: What will happen when (and if) the AMO returns to its cool phase later in this century?
Abstract
Climate models project a significant intensification of the sea surface temperature (SST) seasonal cycle over the subpolar North Pacific due to global warming, with the shallower mixed layer widely recognized as the dominant factor. However, employing slab ocean experiments with only ocean–atmosphere thermal coupling, we find a substantial contribution from changes in surface heat flux to this seasonal cycle intensification. In particular, the stronger Newtonian cooling effect in winter acts as a more potent damping than in summer. This differential damping inhibits the warming in colder seasons, significantly contributing to the intensified SST seasonal cycle in the subpolar North Pacific. In addition, consistent phase shifts in the North Pacific are identified across CMIP6 models. In the northwest North Pacific, a phase advance is associated with anomalous heating in early spring, driven by enhanced warm atmospheric advection from lower latitudes and sea ice melting in marginal seas. In contrast, the southeast North Pacific exhibits a phase delay attributed to the anomalous cooling in spring relative to autumn. This cooling is due to weakened trade winds and increased presence of high clouds. The former leads to stronger evaporative cooling in spring, while the latter impedes shortwave radiation from reaching the ocean.
Abstract
Climate models project a significant intensification of the sea surface temperature (SST) seasonal cycle over the subpolar North Pacific due to global warming, with the shallower mixed layer widely recognized as the dominant factor. However, employing slab ocean experiments with only ocean–atmosphere thermal coupling, we find a substantial contribution from changes in surface heat flux to this seasonal cycle intensification. In particular, the stronger Newtonian cooling effect in winter acts as a more potent damping than in summer. This differential damping inhibits the warming in colder seasons, significantly contributing to the intensified SST seasonal cycle in the subpolar North Pacific. In addition, consistent phase shifts in the North Pacific are identified across CMIP6 models. In the northwest North Pacific, a phase advance is associated with anomalous heating in early spring, driven by enhanced warm atmospheric advection from lower latitudes and sea ice melting in marginal seas. In contrast, the southeast North Pacific exhibits a phase delay attributed to the anomalous cooling in spring relative to autumn. This cooling is due to weakened trade winds and increased presence of high clouds. The former leads to stronger evaporative cooling in spring, while the latter impedes shortwave radiation from reaching the ocean.
