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Abstract
Despite global warming, the sea surface temperature (SST) in the subpolar North Atlantic has decreased since the 1900s. This local cooling, known as the North Atlantic cold blob, signifies a unique role of the subpolar North Atlantic in uptaking heat and hence impacts downstream weather and climate. However, a lack of observational records and their constraints on climate models leave the North Atlantic cold blob formation mechanism inconclusive. Using simulations from phase 6 of Coupled Model Intercomparison Project, we assess the primary processes driving the North Atlantic cold blob within individual models and whether the mechanisms are consistent across models. We show that 11 out of 32 models, which we call “Cold Blob” models, simulate the subpolar North Atlantic cooling over 1900–2014. Further analyzing the heat budget of the subpolar North Atlantic SST shows that models have distinct mechanisms of cold blob formation. While 4 of the 11 Cold Blob models indicate decreased oceanic heat transport convergence (OHTC) as the key mechanism, another four models suggest changes in radiative processes making predominant contributions. The contribution of OHTC and radiative processes is comparable in the remaining three models. Such a model disagreement on the mechanism of cold blob formation may be associated with simulated base-state Atlantic meridional overturning circulation (AMOC) strength, which explains 39% of the intermodel spread in the contribution of OHTC to the simulated cold blob. Models with a stronger base-state AMOC suggest a greater role of OHTC, whereas those with a weaker base-state AMOC indicate that radiative processes are more responsible. This model discrepancy suggests that the cold blob formation mechanism diagnosed from single model should be interpreted with caution.
Significance Statement
The mechanisms driving sea surface temperatures over the subpolar North Atlantic to cool since the 1900s remain uncertain due to the lack of direct observations. Here, we use a temperature change decomposition framework to dissect the historical trend of surface temperature simulated in multiple global climate models. The models diverge on whether the subpolar North Atlantic cooling is induced by reduced ocean heat transport convergence or altered radiative processes. Notably, the importance of ocean heat transport convergence is influenced by the simulated base-state strength of Atlantic meridional overturning circulation and the Irminger Sea’s mixed layer depth. This finding cautions against concluding the cooling mechanism from a single model and highlights a need for ongoing observations to constrain AMOC-related climate projection in the subpolar North Atlantic.
Abstract
Despite global warming, the sea surface temperature (SST) in the subpolar North Atlantic has decreased since the 1900s. This local cooling, known as the North Atlantic cold blob, signifies a unique role of the subpolar North Atlantic in uptaking heat and hence impacts downstream weather and climate. However, a lack of observational records and their constraints on climate models leave the North Atlantic cold blob formation mechanism inconclusive. Using simulations from phase 6 of Coupled Model Intercomparison Project, we assess the primary processes driving the North Atlantic cold blob within individual models and whether the mechanisms are consistent across models. We show that 11 out of 32 models, which we call “Cold Blob” models, simulate the subpolar North Atlantic cooling over 1900–2014. Further analyzing the heat budget of the subpolar North Atlantic SST shows that models have distinct mechanisms of cold blob formation. While 4 of the 11 Cold Blob models indicate decreased oceanic heat transport convergence (OHTC) as the key mechanism, another four models suggest changes in radiative processes making predominant contributions. The contribution of OHTC and radiative processes is comparable in the remaining three models. Such a model disagreement on the mechanism of cold blob formation may be associated with simulated base-state Atlantic meridional overturning circulation (AMOC) strength, which explains 39% of the intermodel spread in the contribution of OHTC to the simulated cold blob. Models with a stronger base-state AMOC suggest a greater role of OHTC, whereas those with a weaker base-state AMOC indicate that radiative processes are more responsible. This model discrepancy suggests that the cold blob formation mechanism diagnosed from single model should be interpreted with caution.
Significance Statement
The mechanisms driving sea surface temperatures over the subpolar North Atlantic to cool since the 1900s remain uncertain due to the lack of direct observations. Here, we use a temperature change decomposition framework to dissect the historical trend of surface temperature simulated in multiple global climate models. The models diverge on whether the subpolar North Atlantic cooling is induced by reduced ocean heat transport convergence or altered radiative processes. Notably, the importance of ocean heat transport convergence is influenced by the simulated base-state strength of Atlantic meridional overturning circulation and the Irminger Sea’s mixed layer depth. This finding cautions against concluding the cooling mechanism from a single model and highlights a need for ongoing observations to constrain AMOC-related climate projection in the subpolar North Atlantic.
Abstract
In the last decades, many efforts have been made to understand how different tropical oceanic basins are able to impact El Niño–Southern Oscillation (ENSO). However, the collective connectivity among the tropical oceans and their associated influence on ENSO are less understood. Using a complex network methodology, the degree of collective connectivity among the tropical oceans is analyzed focusing on the detection of periods when the tropical basins collectively interact and the Atlantic and Indian basins influence the equatorial Pacific sea surface temperatures (SSTs). The background state for the periods of strong collective connectivity is also investigated. Our results show a marked multidecadal variability in the tropical interbasin connection, with periods of stronger and weaker collective connectivity. These changes seem to be modulated by changes in the North Atlantic Ocean mean state a decade in advance. In particular, strong connectivity occurs in periods with colder than average tropical North Atlantic surface ocean. Associated with this cooling, an anomalous convergence of the vertical integral of total energy flux (VIEF) takes place over the tropical north–west Atlantic, associated with anomalous divergence of VIEF over the equatorial eastern Pacific. In turn, an anomalous zonal surface pressure gradient over the tropical Pacific weakens the trades over the western equatorial Pacific. Consequently, a shallower thermocline emerges over the western equatorial Pacific, which can enhance thermocline feedbacks, the triggering of ENSO events, and therefore, ENSO variability. By construction, our results put forward opposite conditions for periods of weak tropical basin connectivity. These results have important implications for seasonal to decadal predictions.
Abstract
In the last decades, many efforts have been made to understand how different tropical oceanic basins are able to impact El Niño–Southern Oscillation (ENSO). However, the collective connectivity among the tropical oceans and their associated influence on ENSO are less understood. Using a complex network methodology, the degree of collective connectivity among the tropical oceans is analyzed focusing on the detection of periods when the tropical basins collectively interact and the Atlantic and Indian basins influence the equatorial Pacific sea surface temperatures (SSTs). The background state for the periods of strong collective connectivity is also investigated. Our results show a marked multidecadal variability in the tropical interbasin connection, with periods of stronger and weaker collective connectivity. These changes seem to be modulated by changes in the North Atlantic Ocean mean state a decade in advance. In particular, strong connectivity occurs in periods with colder than average tropical North Atlantic surface ocean. Associated with this cooling, an anomalous convergence of the vertical integral of total energy flux (VIEF) takes place over the tropical north–west Atlantic, associated with anomalous divergence of VIEF over the equatorial eastern Pacific. In turn, an anomalous zonal surface pressure gradient over the tropical Pacific weakens the trades over the western equatorial Pacific. Consequently, a shallower thermocline emerges over the western equatorial Pacific, which can enhance thermocline feedbacks, the triggering of ENSO events, and therefore, ENSO variability. By construction, our results put forward opposite conditions for periods of weak tropical basin connectivity. These results have important implications for seasonal to decadal predictions.
Abstract
In 2021, the energy sector was put at risk by extreme weather in many different ways: North America and Spain suffered heavy winter storms that led to the collapse of the electricity network; California specifically experienced heavy droughts and heat-wave conditions, causing the operations of hydropower stations to halt; floods caused substantial damage to energy infrastructure in central Europe, Australia, and China throughout the year, and unusual wind drought conditions decreased wind power production in the United Kingdom by almost 40% during summer. The total economic impacts of these extreme weather events are estimated at billions of U.S. dollars. Here we review and assess in some detail the main extreme weather events that impacted the energy sector in 2021 worldwide, discussing some of the most relevant case studies and the meteorological conditions that led to them. We provide a perspective on their impacts on electricity generation, transmission, and consumption, and summarize estimations of economic losses.
Abstract
In 2021, the energy sector was put at risk by extreme weather in many different ways: North America and Spain suffered heavy winter storms that led to the collapse of the electricity network; California specifically experienced heavy droughts and heat-wave conditions, causing the operations of hydropower stations to halt; floods caused substantial damage to energy infrastructure in central Europe, Australia, and China throughout the year, and unusual wind drought conditions decreased wind power production in the United Kingdom by almost 40% during summer. The total economic impacts of these extreme weather events are estimated at billions of U.S. dollars. Here we review and assess in some detail the main extreme weather events that impacted the energy sector in 2021 worldwide, discussing some of the most relevant case studies and the meteorological conditions that led to them. We provide a perspective on their impacts on electricity generation, transmission, and consumption, and summarize estimations of economic losses.
Abstract
The polar regions are facing a wide range of compounding challenges, from climate change to increased human activity. Infrastructure, rescue services, and disaster response capabilities are limited in these remote environments. Relevant and usable weather, water, ice, and climate (WWIC) information is vital for safety, activity success, adaptation, and environmental protection. This has been a key focus for the World Meteorological Organization’s (WMO) Polar Prediction Project (PPP), and in particular its “Societal and Economic Research and Applications” (PPP-SERA) Task Team, which together over a decade have sought to understand polar WWIC information use in relation to operational needs, constraints, and decision contexts to inform the development of relevant services. To understand research progress and gaps on WWIC information use during the PPP (2013–23), we undertook a systematic bibliometric review of aligned scholarly peer-reviewed journal articles (n = 43), examining collaborations, topics, methods, and regional differences. Themes to emerge included activity and context, human factors, information needs, situational awareness, experience, local and Indigenous knowledge, and sharing of information. We observed an uneven representation of disciplinary backgrounds, geographic locations, research topics, and sectoral foci. Our review signifies an overall lack of Antarctic WWIC services research and a dominant focus on Arctic sea ice operations and risks. We noted with concern a mismatch between user needs and services provided. Our findings can help to improve WWIC services’ dissemination, communication effectiveness, and actionable knowledge provision for users and guide future research as the critical need for salient weather services across the polar regions remains beyond the PPP.
Significance Statement
Every day, people in the Arctic and Antarctic use weather, water, ice, and climate information to plan and carry out outdoor activities and operations in a safe way. Despite advances in numerical weather prediction, technology, and product development, barriers to accessing and effectively communicating high-quality usable observations, forecasts, and actionable knowledge remain. Poorer services, prediction accuracy, and interpretation are exacerbated by a lack of integrated social science research on relevant topics and a mismatch between the services provided and user needs. As a result, continued user engagement, research focusing on information use, risk communication, decision-making processes, and the application of science for services remain highly relevant to reducing risks and improving safety for people living, visiting, and working in the polar regions.
Abstract
The polar regions are facing a wide range of compounding challenges, from climate change to increased human activity. Infrastructure, rescue services, and disaster response capabilities are limited in these remote environments. Relevant and usable weather, water, ice, and climate (WWIC) information is vital for safety, activity success, adaptation, and environmental protection. This has been a key focus for the World Meteorological Organization’s (WMO) Polar Prediction Project (PPP), and in particular its “Societal and Economic Research and Applications” (PPP-SERA) Task Team, which together over a decade have sought to understand polar WWIC information use in relation to operational needs, constraints, and decision contexts to inform the development of relevant services. To understand research progress and gaps on WWIC information use during the PPP (2013–23), we undertook a systematic bibliometric review of aligned scholarly peer-reviewed journal articles (n = 43), examining collaborations, topics, methods, and regional differences. Themes to emerge included activity and context, human factors, information needs, situational awareness, experience, local and Indigenous knowledge, and sharing of information. We observed an uneven representation of disciplinary backgrounds, geographic locations, research topics, and sectoral foci. Our review signifies an overall lack of Antarctic WWIC services research and a dominant focus on Arctic sea ice operations and risks. We noted with concern a mismatch between user needs and services provided. Our findings can help to improve WWIC services’ dissemination, communication effectiveness, and actionable knowledge provision for users and guide future research as the critical need for salient weather services across the polar regions remains beyond the PPP.
Significance Statement
Every day, people in the Arctic and Antarctic use weather, water, ice, and climate information to plan and carry out outdoor activities and operations in a safe way. Despite advances in numerical weather prediction, technology, and product development, barriers to accessing and effectively communicating high-quality usable observations, forecasts, and actionable knowledge remain. Poorer services, prediction accuracy, and interpretation are exacerbated by a lack of integrated social science research on relevant topics and a mismatch between the services provided and user needs. As a result, continued user engagement, research focusing on information use, risk communication, decision-making processes, and the application of science for services remain highly relevant to reducing risks and improving safety for people living, visiting, and working in the polar regions.
Abstract
Despite the sophistication of global climate models (GCMs), their coarse spatial resolution limits their ability to resolve important aspects of climate variability and change at the local scale. Both dynamical and empirical methods are used for enhancing the resolution of climate projections through downscaling, each with distinct advantages and challenges. Dynamical downscaling is physics based but comes with a large computational cost, posing a barrier for downscaling an ensemble of GCMs large enough for reliable uncertainty quantification of climate risks. In contrast, empirical downscaling, which encompasses statistical and machine learning techniques, provides a computationally efficient alternative to downscaling GCMs. Empirical downscaling algorithms can be developed to emulate the behavior of dynamical models directly, or through frameworks such as perfect prognosis in which relationships are established between large-scale atmospheric conditions and local weather variables using observational data. However, the ability of empirical downscaling algorithms to apply their learned relationships out of distribution into future climates remains uncertain, as is their ability to represent certain types of extreme events. This review covers the growing potential of machine learning methods to address these challenges, offering a thorough exploration of the current applications and training strategies that can circumvent certain issues. Additionally, we propose an evaluation framework for machine learning algorithms specific to the problem of climate downscaling as needed to improve transparency and foster trust in climate projections.
Significance Statement
This review offers a significant contribution to our understanding of how machine learning can offer a transformative change in climate downscaling. It serves as a guide to navigate recent advances in machine learning and how these advances can be better aligned toward inherent challenges in climate downscaling. In this review, we provide an overview of these recent advances with a critical discussion of their advantages and limitations. We also discuss opportunities to refine existing machine learning methods alongside new approaches for the generation of large ensembles of high-resolution climate projections.
Abstract
Despite the sophistication of global climate models (GCMs), their coarse spatial resolution limits their ability to resolve important aspects of climate variability and change at the local scale. Both dynamical and empirical methods are used for enhancing the resolution of climate projections through downscaling, each with distinct advantages and challenges. Dynamical downscaling is physics based but comes with a large computational cost, posing a barrier for downscaling an ensemble of GCMs large enough for reliable uncertainty quantification of climate risks. In contrast, empirical downscaling, which encompasses statistical and machine learning techniques, provides a computationally efficient alternative to downscaling GCMs. Empirical downscaling algorithms can be developed to emulate the behavior of dynamical models directly, or through frameworks such as perfect prognosis in which relationships are established between large-scale atmospheric conditions and local weather variables using observational data. However, the ability of empirical downscaling algorithms to apply their learned relationships out of distribution into future climates remains uncertain, as is their ability to represent certain types of extreme events. This review covers the growing potential of machine learning methods to address these challenges, offering a thorough exploration of the current applications and training strategies that can circumvent certain issues. Additionally, we propose an evaluation framework for machine learning algorithms specific to the problem of climate downscaling as needed to improve transparency and foster trust in climate projections.
Significance Statement
This review offers a significant contribution to our understanding of how machine learning can offer a transformative change in climate downscaling. It serves as a guide to navigate recent advances in machine learning and how these advances can be better aligned toward inherent challenges in climate downscaling. In this review, we provide an overview of these recent advances with a critical discussion of their advantages and limitations. We also discuss opportunities to refine existing machine learning methods alongside new approaches for the generation of large ensembles of high-resolution climate projections.
Abstract
Extreme weather events (EWEs) linked to climate change are expected to increase in frequency in the coming years, putting the entire world in danger. Parents exert a significant influence on the lives of their children and the overall function of the family unit. However, natural disasters have a significant impact on daily life and pose an immediate danger, resulting in loss of life, injuries, and property damage. In addition, disasters can also have an impact on the responsibilities that parents play in their house. This study examines the evolving dynamics of parental roles in the context of EWEs, examining the shifting expectations and actual realities of fatherhood and motherhood. The study examines the various effects of EWEs on family structures, gender roles, and parental obligations by conducting a comprehensive review of 30 relevant articles. Our findings indicate that in severe weather conditions, men tend to adopt the position of “father” and are perceived as heroic figures, rescuers, and guardians/protectors who prioritize the well-being of their children and families, as well as take on financial obligations. On the other hand, women are often viewed as caregivers/rescuers/victims during such conditions. Moreover, in many countries, women are expected to care for other family members, including younger children and the elderly, which may limit their mobility during severe weather. Extreme weather conditions affect men and women differently, and there may also be significant differences in gender-related expectations and dimensions within a country. It is therefore essential to thoroughly study how these roles change in response to extreme weather events. We recommend conducting additional rigorous studies, both quantitative and qualitative, to comprehensively examine this relationship. This study will aid in designing initiatives aimed at fostering parenting attributes, particularly in regions susceptible to disasters.
Abstract
Extreme weather events (EWEs) linked to climate change are expected to increase in frequency in the coming years, putting the entire world in danger. Parents exert a significant influence on the lives of their children and the overall function of the family unit. However, natural disasters have a significant impact on daily life and pose an immediate danger, resulting in loss of life, injuries, and property damage. In addition, disasters can also have an impact on the responsibilities that parents play in their house. This study examines the evolving dynamics of parental roles in the context of EWEs, examining the shifting expectations and actual realities of fatherhood and motherhood. The study examines the various effects of EWEs on family structures, gender roles, and parental obligations by conducting a comprehensive review of 30 relevant articles. Our findings indicate that in severe weather conditions, men tend to adopt the position of “father” and are perceived as heroic figures, rescuers, and guardians/protectors who prioritize the well-being of their children and families, as well as take on financial obligations. On the other hand, women are often viewed as caregivers/rescuers/victims during such conditions. Moreover, in many countries, women are expected to care for other family members, including younger children and the elderly, which may limit their mobility during severe weather. Extreme weather conditions affect men and women differently, and there may also be significant differences in gender-related expectations and dimensions within a country. It is therefore essential to thoroughly study how these roles change in response to extreme weather events. We recommend conducting additional rigorous studies, both quantitative and qualitative, to comprehensively examine this relationship. This study will aid in designing initiatives aimed at fostering parenting attributes, particularly in regions susceptible to disasters.
Abstract
Tropical cyclone (TC) structure and intensity are strongly modulated by interactions with deep-layer vertical wind shear (VWS)—the vector difference between horizontal winds at 200 and 850 hPa. This paper presents a comprehensive review of more than a century of research on TC–VWS interactions. The literature broadly agrees that a TC vortex becomes vertically tilted, precipitation organizes into a wavenumber-1 asymmetric pattern, and thermal and kinematic asymmetries emerge when a TC encounters an environmental sheared flow. However, these responses depend on other factors, including the magnitude and direction of horizontal winds at other vertical levels between 200 and 850 hPa, the amount and location of dry environmental air, and the underlying sea surface temperature. While early studies investigated how VWS weakens TCs, an emerging line of research has focused on understanding how TCs intensify under moderate and strong VWS (i.e., shear magnitudes greater than 5 m s−1). Modeling and observational studies have identified four pathways to intensification: vortex tilt reduction, vortex reformation, axisymmetrization of precipitation, and outflow blocking. These pathways may not be uniquely different because convection and vortex asymmetries are strongly coupled to each other. In addition to discussing these topics, this review presents open questions and recommendations for future research on TC–VWS interactions.
Abstract
Tropical cyclone (TC) structure and intensity are strongly modulated by interactions with deep-layer vertical wind shear (VWS)—the vector difference between horizontal winds at 200 and 850 hPa. This paper presents a comprehensive review of more than a century of research on TC–VWS interactions. The literature broadly agrees that a TC vortex becomes vertically tilted, precipitation organizes into a wavenumber-1 asymmetric pattern, and thermal and kinematic asymmetries emerge when a TC encounters an environmental sheared flow. However, these responses depend on other factors, including the magnitude and direction of horizontal winds at other vertical levels between 200 and 850 hPa, the amount and location of dry environmental air, and the underlying sea surface temperature. While early studies investigated how VWS weakens TCs, an emerging line of research has focused on understanding how TCs intensify under moderate and strong VWS (i.e., shear magnitudes greater than 5 m s−1). Modeling and observational studies have identified four pathways to intensification: vortex tilt reduction, vortex reformation, axisymmetrization of precipitation, and outflow blocking. These pathways may not be uniquely different because convection and vortex asymmetries are strongly coupled to each other. In addition to discussing these topics, this review presents open questions and recommendations for future research on TC–VWS interactions.
Abstract
Atmospheric predictability from subseasonal to seasonal time scales and climate variability are both influenced critically by gravity waves (GW). The quality of regional and global numerical models relies on thorough understanding of GW dynamics and its interplay with chemistry, precipitation, clouds, and climate across many scales. For the foreseeable future, GWs and many other relevant processes will remain partly unresolved, and models will continue to rely on parameterizations. Recent model intercomparisons and studies show that present-day GW parameterizations do not accurately represent GW processes. These shortcomings introduce uncertainties, among others, in predicting the effects of climate change on important modes of variability. However, the last decade has produced new data and advances in theoretical and numerical developments that promise to improve the situation. This review gives a survey of these developments, discusses the present status of GW parameterizations, and formulates recommendations on how to proceed from there.
Abstract
Atmospheric predictability from subseasonal to seasonal time scales and climate variability are both influenced critically by gravity waves (GW). The quality of regional and global numerical models relies on thorough understanding of GW dynamics and its interplay with chemistry, precipitation, clouds, and climate across many scales. For the foreseeable future, GWs and many other relevant processes will remain partly unresolved, and models will continue to rely on parameterizations. Recent model intercomparisons and studies show that present-day GW parameterizations do not accurately represent GW processes. These shortcomings introduce uncertainties, among others, in predicting the effects of climate change on important modes of variability. However, the last decade has produced new data and advances in theoretical and numerical developments that promise to improve the situation. This review gives a survey of these developments, discusses the present status of GW parameterizations, and formulates recommendations on how to proceed from there.
Abstract
The increased social need for more precise and reliable weather forecasts, especially when focusing on extreme weather events, pushes forward research and development in meteorology toward novel numerical weather prediction (NWP) systems that can provide simulations that resolve atmospheric processes on hectometric scales on demand. Such high-resolution NWP systems require a more detailed representation of the nonresolved processes, i.e., usage of scale-aware schemes for convection and three-dimensional turbulence (and radiation), which would additionally increase the computation needs. Therefore, developing and applying comprehensive, reliable, and computationally acceptable parameterizations in NWP systems is of urgent importance. All operationally used NWP systems are based on averaged Navier–Stokes equations, and thus require an approximation for the small-scale turbulent fluxes of momentum, energy, and matter in the system. The availability of high-fidelity data from turbulence experiments and direct numerical simulations has helped scientists in the past to construct and calibrate a range of turbulence closure approximations (from the relatively simple to more complex), some of which have been adopted and are in use in the current operational NWP systems. The significant development of learned-by-data (LBD) algorithms over the past decade (e.g., artificial intelligence) motivates engineers and researchers in fluid dynamics to explore alternatives for modeling turbulence by directly using turbulence data to quantify and reduce model uncertainties systematically. This review elaborates on the LBD approaches and their use in NWP currently, and also searches for novel data-informed turbulence models that can potentially be used and applied in NWP. Based on this literature analysis, the challenges and perspectives to do so are discussed.
Abstract
The increased social need for more precise and reliable weather forecasts, especially when focusing on extreme weather events, pushes forward research and development in meteorology toward novel numerical weather prediction (NWP) systems that can provide simulations that resolve atmospheric processes on hectometric scales on demand. Such high-resolution NWP systems require a more detailed representation of the nonresolved processes, i.e., usage of scale-aware schemes for convection and three-dimensional turbulence (and radiation), which would additionally increase the computation needs. Therefore, developing and applying comprehensive, reliable, and computationally acceptable parameterizations in NWP systems is of urgent importance. All operationally used NWP systems are based on averaged Navier–Stokes equations, and thus require an approximation for the small-scale turbulent fluxes of momentum, energy, and matter in the system. The availability of high-fidelity data from turbulence experiments and direct numerical simulations has helped scientists in the past to construct and calibrate a range of turbulence closure approximations (from the relatively simple to more complex), some of which have been adopted and are in use in the current operational NWP systems. The significant development of learned-by-data (LBD) algorithms over the past decade (e.g., artificial intelligence) motivates engineers and researchers in fluid dynamics to explore alternatives for modeling turbulence by directly using turbulence data to quantify and reduce model uncertainties systematically. This review elaborates on the LBD approaches and their use in NWP currently, and also searches for novel data-informed turbulence models that can potentially be used and applied in NWP. Based on this literature analysis, the challenges and perspectives to do so are discussed.
Abstract
Harmful algae and cyanobacteria blooms are increasing in frequency and intensity in freshwater systems due to anthropogenic impacts such as nutrient loading in watersheds and engineered alterations of natural waterways. There are multiple physical factors that affect the conditions in a freshwater system that contribute to optimal habitats for harmful algae and toxin-producing cyanobacteria. A growing body of research shows that climate change stressors also are impacting water-body conditions that favor harmful algae and cyanobacteria species over other phytoplankton. The overgrowth of these organisms, or a “bloom,” increases the opportunity for exposure to toxins by humans, companion animals, livestock, and wildlife. As waters warm and precipitation patterns change over time, exposure to these blooms is projected to increase. Hence, it is important that states and tribes develop monitoring and reporting strategies as well as align governmental policies to protect their citizens and ecosystems within their jurisdiction. Currently, the policies and approaches taken to monitor and report on harmful algae and cyanobacteria blooms vary widely among states, and it is undetermined if any tribes have specific policies on harmful algae blooms. This paper synthesizes research on algal blooms in inland freshwater systems of the United States. This review examines how climate change contributes to trends in bloom frequency or severity and outlines approaches that states and tribes may use to monitor, report, and respond to harmful algae and cyanobacteria blooms.
Significance Statement
Inland bodies of freshwater supply drinking water for humans and animals, water for irrigating crops, habitats for aquatic species, places of cultural significance for Indigenous peoples, and other important functions. Many of these bodies of water have been polluted with runoff from industry, including agriculture, and already support harmful algal blooms during warm conditions. Hot extremes associated with climate change are expected to increase the occurrence and duration of harmful algal blooms, and in some places, initiate blooms where none have been recorded previously. These toxic blooms are harmful to people, companion animals, livestock, and wildlife. It is important to review the interconnections among biological, climate, and water systems to monitor blooms and alert the public about their toxin production.
Abstract
Harmful algae and cyanobacteria blooms are increasing in frequency and intensity in freshwater systems due to anthropogenic impacts such as nutrient loading in watersheds and engineered alterations of natural waterways. There are multiple physical factors that affect the conditions in a freshwater system that contribute to optimal habitats for harmful algae and toxin-producing cyanobacteria. A growing body of research shows that climate change stressors also are impacting water-body conditions that favor harmful algae and cyanobacteria species over other phytoplankton. The overgrowth of these organisms, or a “bloom,” increases the opportunity for exposure to toxins by humans, companion animals, livestock, and wildlife. As waters warm and precipitation patterns change over time, exposure to these blooms is projected to increase. Hence, it is important that states and tribes develop monitoring and reporting strategies as well as align governmental policies to protect their citizens and ecosystems within their jurisdiction. Currently, the policies and approaches taken to monitor and report on harmful algae and cyanobacteria blooms vary widely among states, and it is undetermined if any tribes have specific policies on harmful algae blooms. This paper synthesizes research on algal blooms in inland freshwater systems of the United States. This review examines how climate change contributes to trends in bloom frequency or severity and outlines approaches that states and tribes may use to monitor, report, and respond to harmful algae and cyanobacteria blooms.
Significance Statement
Inland bodies of freshwater supply drinking water for humans and animals, water for irrigating crops, habitats for aquatic species, places of cultural significance for Indigenous peoples, and other important functions. Many of these bodies of water have been polluted with runoff from industry, including agriculture, and already support harmful algal blooms during warm conditions. Hot extremes associated with climate change are expected to increase the occurrence and duration of harmful algal blooms, and in some places, initiate blooms where none have been recorded previously. These toxic blooms are harmful to people, companion animals, livestock, and wildlife. It is important to review the interconnections among biological, climate, and water systems to monitor blooms and alert the public about their toxin production.
