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Abstract
This study examines the historical record of hurricanes and tropical storms in the Atlantic Ocean basin to determine the eventual landfall probability for the U.S. coastline based on the complete tracks of those storms. The current method for estimating empirical landfall probabilities is to report a frequency based on the number of storms affecting a region over a certain period of time. A spatial dimension is added in this study to determine which storms in all portions of the basin might ultimately strike the United States based on the historical record. For example, if a tropical cyclone is near the island of Puerto Rico, which portions (if any) of the U.S. coastline are most at risk of eventual landfall? A tessellation of hexagons is systematically evaluated, and eventual landfall probabilities are calculated for all storms passing through each hexagon. Probabilities are calculated and mapped for four individual states and for the United States as a whole. The maps show the spatial areas that contribute storms to each of the states. In addition, an average length of time until landfall is calculated for the entire Atlantic basin based on the complete period of record. This highlights regions of the Atlantic basin lying outside of the maximum forecast period, up to 15 days prior to potential landfall.
Abstract
This study examines the historical record of hurricanes and tropical storms in the Atlantic Ocean basin to determine the eventual landfall probability for the U.S. coastline based on the complete tracks of those storms. The current method for estimating empirical landfall probabilities is to report a frequency based on the number of storms affecting a region over a certain period of time. A spatial dimension is added in this study to determine which storms in all portions of the basin might ultimately strike the United States based on the historical record. For example, if a tropical cyclone is near the island of Puerto Rico, which portions (if any) of the U.S. coastline are most at risk of eventual landfall? A tessellation of hexagons is systematically evaluated, and eventual landfall probabilities are calculated for all storms passing through each hexagon. Probabilities are calculated and mapped for four individual states and for the United States as a whole. The maps show the spatial areas that contribute storms to each of the states. In addition, an average length of time until landfall is calculated for the entire Atlantic basin based on the complete period of record. This highlights regions of the Atlantic basin lying outside of the maximum forecast period, up to 15 days prior to potential landfall.
The National Climatic Data Center and numerous other sources list the 15.20-in. (386 mm) rainfall observed at the Angoon, Alaska, cooperative weather station on 12 October 1982 as the state record for a single calendar-day precipitation amount. However, a close inspection of the precipitation data recorded during 1982 in Angoon reveals a pattern of suspect values, calling into question the validity of the data collected during this time period. Our analysis shows that errors may be present in the Angoon precipitation record, and therefore consideration should be given to removing those values from the official climate database. This study evaluates Angoon precipitation observations over a 12-month period starting in February 1982 using two objective analytical techniques (statistical and climatological) and a 2012 interview of the Angoon cooperative station observer during the time period in question. If the Angoon precipitation values are deemed erroneous, then the 12 October 1982 observation will no longer hold the distinction as the single largest precipitation event. The second place event is a 15.05 in. (382 mm) observation measured in Seward, Alaska, on 10 October 1986.
The National Climatic Data Center and numerous other sources list the 15.20-in. (386 mm) rainfall observed at the Angoon, Alaska, cooperative weather station on 12 October 1982 as the state record for a single calendar-day precipitation amount. However, a close inspection of the precipitation data recorded during 1982 in Angoon reveals a pattern of suspect values, calling into question the validity of the data collected during this time period. Our analysis shows that errors may be present in the Angoon precipitation record, and therefore consideration should be given to removing those values from the official climate database. This study evaluates Angoon precipitation observations over a 12-month period starting in February 1982 using two objective analytical techniques (statistical and climatological) and a 2012 interview of the Angoon cooperative station observer during the time period in question. If the Angoon precipitation values are deemed erroneous, then the 12 October 1982 observation will no longer hold the distinction as the single largest precipitation event. The second place event is a 15.05 in. (382 mm) observation measured in Seward, Alaska, on 10 October 1986.
Abstract
Alaska experienced record-setting warmth during the 2015/16 cold season (October–April). Statewide average temperatures exceeded the period-of-record mean by more than 4°C over the 7-month cold season and by more than 6°C over the 4-month late-winter period, January–April. The record warmth raises two questions: 1) Why was Alaska so warm during the 2015/16 cold season? 2) At what point in the future might this warmth become typical if greenhouse warming continues? On the basis of circulation analogs computed from sea level pressure and 850-hPa geopotential height fields, the atmospheric circulation explains less than half of the anomalous warmth. The warming signal forced by greenhouse gases in climate models accounts for about 1°C of the anomalous warmth. A factor that is consistent with the seasonal and spatial patterns of the warmth is the anomalous surface state. The surface anomalies include 1) above-normal ocean surface temperatures and below-normal sea ice coverage in the surrounding seas from which air advects into Alaska and 2) the deficient snowpack over Alaska itself. The location of the maximum of anomalous warmth over Alaska and the late-winter–early-spring increase of the anomalous warmth unexplained by the atmospheric circulation implicates snow cover and its albedo effect, which is supported by observational measurements in the boreal forest and tundra biomes. Climate model simulations indicate that warmth of this magnitude will become the norm by the 2050s if greenhouse gas emissions follow their present scenario.
Abstract
Alaska experienced record-setting warmth during the 2015/16 cold season (October–April). Statewide average temperatures exceeded the period-of-record mean by more than 4°C over the 7-month cold season and by more than 6°C over the 4-month late-winter period, January–April. The record warmth raises two questions: 1) Why was Alaska so warm during the 2015/16 cold season? 2) At what point in the future might this warmth become typical if greenhouse warming continues? On the basis of circulation analogs computed from sea level pressure and 850-hPa geopotential height fields, the atmospheric circulation explains less than half of the anomalous warmth. The warming signal forced by greenhouse gases in climate models accounts for about 1°C of the anomalous warmth. A factor that is consistent with the seasonal and spatial patterns of the warmth is the anomalous surface state. The surface anomalies include 1) above-normal ocean surface temperatures and below-normal sea ice coverage in the surrounding seas from which air advects into Alaska and 2) the deficient snowpack over Alaska itself. The location of the maximum of anomalous warmth over Alaska and the late-winter–early-spring increase of the anomalous warmth unexplained by the atmospheric circulation implicates snow cover and its albedo effect, which is supported by observational measurements in the boreal forest and tundra biomes. Climate model simulations indicate that warmth of this magnitude will become the norm by the 2050s if greenhouse gas emissions follow their present scenario.
Abstract
Did the strong 2023–24 El Niño live up to the hype? While climate prediction is inherently probabilistic, many users compare El Niño events against a deterministic map of expected impacts (e.g., wetter or drier regions). Here, using this event as a guide, we show that no El Niño perfectly matches the ideal image and that observed anomalies will only partially match what was anticipated. In fact, the degree to which the climate anomalies match the expected ENSO impacts tends to scale with the strength of the event. The 2023–24 event generally matched well with ENSO expectations around the United States. However, this will not always be the case, as the analysis shows larger deviations from the historical ENSO pattern of impacts are commonplace, with some climate variables more prone to inconsistencies (e.g., temperature) than others (e.g., precipitation). Users should incorporate this inherent uncertainty in their risk and decision-making analysis.
Abstract
Did the strong 2023–24 El Niño live up to the hype? While climate prediction is inherently probabilistic, many users compare El Niño events against a deterministic map of expected impacts (e.g., wetter or drier regions). Here, using this event as a guide, we show that no El Niño perfectly matches the ideal image and that observed anomalies will only partially match what was anticipated. In fact, the degree to which the climate anomalies match the expected ENSO impacts tends to scale with the strength of the event. The 2023–24 event generally matched well with ENSO expectations around the United States. However, this will not always be the case, as the analysis shows larger deviations from the historical ENSO pattern of impacts are commonplace, with some climate variables more prone to inconsistencies (e.g., temperature) than others (e.g., precipitation). Users should incorporate this inherent uncertainty in their risk and decision-making analysis.
Abstract
Some of the largest climatic changes in the Arctic have been observed in Alaska and the surrounding marginal seas. Near-surface air temperature (T2m), precipitation (P), snowfall, and sea ice changes have been previously documented, often in disparate studies. Here, we provide an updated, long-term trend analysis (1957–2021; n = 65 years) of such parameters in ERA5, NOAA U.S. Climate Gridded Dataset (NClimGrid), NOAA National Centers for Environmental Information (NCEI) Alaska climate division, and composite sea ice products preceding the upcoming Fifth National Climate Assessment (NCA5) and other near-future climate reports. In the past half century, annual T2m has broadly increased across Alaska, and during winter, spring, and autumn on the North Slope and North Panhandle (T2m > 0.50°C decade−1). Precipitation has also increased across climate divisions and appears strongly interrelated with temperature–sea ice feedbacks on the North Slope, specifically with increased (decreased) open water (sea ice extent). Snowfall equivalent (SFE) has decreased in autumn and spring, perhaps aligned with a regime transition of snow to rain, while winter SFE has broadly increased across the state. Sea ice decline and melt-season lengthening also have a pronounced signal around Alaska, with the largest trends in these parameters found in the Beaufort Sea. Alaska’s climatic changes are also placed in context against regional and contiguous U.S. air temperature trends and show ∼50% greater warming in Alaska relative to the lower-48 states. Alaska T2m increases also exceed those of any contiguous U.S. subregion, positioning Alaska at the forefront of U.S. climate warming.
Significance Statement
This study produces an updated, long-term trend analysis (1957–2021) of key Alaska climate parameters, including air temperature, precipitation (including snowfall equivalent), and sea ice, to inform upcoming climate assessment reports, including the Fifth National Climate Assessment (NCA5) scheduled for publication in 2023. Key findings include widespread annual and seasonal warming with increased precipitation across much of the state. Winter snowfall has broadly increased, but spring and autumn snowfalls have decreased as rainfall increased. Autumn warming and precipitation increases over the North Slope, in particular, appear related to decreased sea ice coverage in the Beaufort Sea and Chukchi Seas. These trends may result from interrelated processes that accelerate Alaska climate changes relative to those of the contiguous United States.
Abstract
Some of the largest climatic changes in the Arctic have been observed in Alaska and the surrounding marginal seas. Near-surface air temperature (T2m), precipitation (P), snowfall, and sea ice changes have been previously documented, often in disparate studies. Here, we provide an updated, long-term trend analysis (1957–2021; n = 65 years) of such parameters in ERA5, NOAA U.S. Climate Gridded Dataset (NClimGrid), NOAA National Centers for Environmental Information (NCEI) Alaska climate division, and composite sea ice products preceding the upcoming Fifth National Climate Assessment (NCA5) and other near-future climate reports. In the past half century, annual T2m has broadly increased across Alaska, and during winter, spring, and autumn on the North Slope and North Panhandle (T2m > 0.50°C decade−1). Precipitation has also increased across climate divisions and appears strongly interrelated with temperature–sea ice feedbacks on the North Slope, specifically with increased (decreased) open water (sea ice extent). Snowfall equivalent (SFE) has decreased in autumn and spring, perhaps aligned with a regime transition of snow to rain, while winter SFE has broadly increased across the state. Sea ice decline and melt-season lengthening also have a pronounced signal around Alaska, with the largest trends in these parameters found in the Beaufort Sea. Alaska’s climatic changes are also placed in context against regional and contiguous U.S. air temperature trends and show ∼50% greater warming in Alaska relative to the lower-48 states. Alaska T2m increases also exceed those of any contiguous U.S. subregion, positioning Alaska at the forefront of U.S. climate warming.
Significance Statement
This study produces an updated, long-term trend analysis (1957–2021) of key Alaska climate parameters, including air temperature, precipitation (including snowfall equivalent), and sea ice, to inform upcoming climate assessment reports, including the Fifth National Climate Assessment (NCA5) scheduled for publication in 2023. Key findings include widespread annual and seasonal warming with increased precipitation across much of the state. Winter snowfall has broadly increased, but spring and autumn snowfalls have decreased as rainfall increased. Autumn warming and precipitation increases over the North Slope, in particular, appear related to decreased sea ice coverage in the Beaufort Sea and Chukchi Seas. These trends may result from interrelated processes that accelerate Alaska climate changes relative to those of the contiguous United States.
Abstract
In this study, seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), are compared with station observations to assess their usefulness in producing accurate buildup index (BUI) forecasts for the fire season in Interior Alaska. These comparisons indicate that the CFSv2 June–July–August (JJA) climatology (1994–2017) produces negatively biased BUI forecasts because of negative temperature and positive precipitation biases. With quantile mapping (QM) correction, the temperature and precipitation forecasts better match the observations. The long-term JJA mean BUI improves from 12 to 42 when computed using the QM-corrected forecasts. Further postprocessing of the QM-corrected BUI forecasts using the quartile classification method shows anomalously high values for the 2004 fire season, which was the worst on record in terms of the area burned by wildfires. These results suggest that the QM-corrected CFSv2 forecasts can be used to predict extreme fire events. An assessment of the classified BUI ensemble members at the subseasonal scale shows that persistently occurring BUI forecasts exceeding 150 in the cumulative drought season can be used as an indicator that extreme fire events will occur during the upcoming season. This study demonstrates the ability of QM-corrected CFSv2 forecasts to predict the potential fire season in advance. This information could, therefore, assist fire managers in resource allocation and disaster response preparedness.
Abstract
In this study, seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), are compared with station observations to assess their usefulness in producing accurate buildup index (BUI) forecasts for the fire season in Interior Alaska. These comparisons indicate that the CFSv2 June–July–August (JJA) climatology (1994–2017) produces negatively biased BUI forecasts because of negative temperature and positive precipitation biases. With quantile mapping (QM) correction, the temperature and precipitation forecasts better match the observations. The long-term JJA mean BUI improves from 12 to 42 when computed using the QM-corrected forecasts. Further postprocessing of the QM-corrected BUI forecasts using the quartile classification method shows anomalously high values for the 2004 fire season, which was the worst on record in terms of the area burned by wildfires. These results suggest that the QM-corrected CFSv2 forecasts can be used to predict extreme fire events. An assessment of the classified BUI ensemble members at the subseasonal scale shows that persistently occurring BUI forecasts exceeding 150 in the cumulative drought season can be used as an indicator that extreme fire events will occur during the upcoming season. This study demonstrates the ability of QM-corrected CFSv2 forecasts to predict the potential fire season in advance. This information could, therefore, assist fire managers in resource allocation and disaster response preparedness.
Abstract
The National Oceanic and Atmospheric Administration’s (NOAA) National Weather Service (NWS) has been providing national, regional, and local climate services for more than 20 years. The NWS climate services building blocks consist of service provision infrastructure, partnership and outreach, discovery of user needs and requirements, and service delivery at national, regional, local, and tribal levels. To improve services, the NWS climate services program accelerated user engagement through customer surveys, workshops, and collaborations. Since 2002, the annual Climate Prediction Applications Science Workshop has developed a community of climate information producers and users through sharing of climate science applications, decision support tools, and effective communication practices. Although NWS had been producing operational climate monitoring and prediction products for several decades, the Weather Research and Forecasting Innovation Act of 2017 (U.S. Public Law 115-25) specifically mandated that NWS deliver services at subseasonal to seasonal time scales, including periods from two weeks to two years. Looking ahead, both the Department of Commerce (DOC) and NOAA have included climate services in their new 2022–26 strategic plans, including DOC’s goal to address the climate crisis through mitigation, adaptation, and resilience efforts and NOAA’s initiatives to build a Climate Ready Nation (CRN). The NWS Climate Services Program supports these strategic goals and CRN initiatives through integrating climate information into Impact-based Decision Support Services, the most critical element for implementation of the NWS strategy for a Weather-Ready Nation. This includes application of state-of-the-art climate monitoring and prediction products to the most societally relevant impacts while empowering regional and local climate delivery of enhanced services.
Abstract
The National Oceanic and Atmospheric Administration’s (NOAA) National Weather Service (NWS) has been providing national, regional, and local climate services for more than 20 years. The NWS climate services building blocks consist of service provision infrastructure, partnership and outreach, discovery of user needs and requirements, and service delivery at national, regional, local, and tribal levels. To improve services, the NWS climate services program accelerated user engagement through customer surveys, workshops, and collaborations. Since 2002, the annual Climate Prediction Applications Science Workshop has developed a community of climate information producers and users through sharing of climate science applications, decision support tools, and effective communication practices. Although NWS had been producing operational climate monitoring and prediction products for several decades, the Weather Research and Forecasting Innovation Act of 2017 (U.S. Public Law 115-25) specifically mandated that NWS deliver services at subseasonal to seasonal time scales, including periods from two weeks to two years. Looking ahead, both the Department of Commerce (DOC) and NOAA have included climate services in their new 2022–26 strategic plans, including DOC’s goal to address the climate crisis through mitigation, adaptation, and resilience efforts and NOAA’s initiatives to build a Climate Ready Nation (CRN). The NWS Climate Services Program supports these strategic goals and CRN initiatives through integrating climate information into Impact-based Decision Support Services, the most critical element for implementation of the NWS strategy for a Weather-Ready Nation. This includes application of state-of-the-art climate monitoring and prediction products to the most societally relevant impacts while empowering regional and local climate delivery of enhanced services.
