Abstract
Some of the most intense thunderstorms on Earth occur in the Hindu Kush Himalaya (HKH) region of southern Asia—where many organizations lack the capacity needed to predict, observe, and/or effectively respond to the threats associated with high-impact convective weather. As a result, a disproportionately large number of casualties and damage often occur with premonsoon severe thunderstorms in this region. To address this problem, we combined ensemble numerical weather prediction (NWP), satellite-based precipitation products, and land-imagery techniques into a High-Impact Weather Assessment Toolkit (HIWAT) customized for HKH. In 2018 and 2019 demonstrations, a regional convection-allowing ensemble NWP system was configured to provide real-time probabilistic guidance of thunderstorm hazards over HKH, applying ensemble techniques developed for U.S.-focused experiments. Case studies of damaging wind, large hail, lightning, a rare Nepalese tornado, and landfalling tropical cyclone events show how HIWAT efficiently packages ensemble output into products that are readily interpreted by forecasters in HKH. Precipitation and total lightning flash verification reveal the highest skill occurred where deep convection was most frequently observed in Bangladesh and northeastern India, and verification scores exceeded global ensemble scores for heavy precipitation rates. These results demonstrate that plausible forecasts of thunderstorm hazards can be attained with relatively low computational resources, thereby facilitating advancements in extreme weather forecasting services in historically underserved regions such as HKH. In early 2022, a custom version of HIWAT was installed at the Bangladesh Meteorological Department using in-house computational resources, providing regional ensemble forecast guidance in real time.
Abstract
Some of the most intense thunderstorms on Earth occur in the Hindu Kush Himalaya (HKH) region of southern Asia—where many organizations lack the capacity needed to predict, observe, and/or effectively respond to the threats associated with high-impact convective weather. As a result, a disproportionately large number of casualties and damage often occur with premonsoon severe thunderstorms in this region. To address this problem, we combined ensemble numerical weather prediction (NWP), satellite-based precipitation products, and land-imagery techniques into a High-Impact Weather Assessment Toolkit (HIWAT) customized for HKH. In 2018 and 2019 demonstrations, a regional convection-allowing ensemble NWP system was configured to provide real-time probabilistic guidance of thunderstorm hazards over HKH, applying ensemble techniques developed for U.S.-focused experiments. Case studies of damaging wind, large hail, lightning, a rare Nepalese tornado, and landfalling tropical cyclone events show how HIWAT efficiently packages ensemble output into products that are readily interpreted by forecasters in HKH. Precipitation and total lightning flash verification reveal the highest skill occurred where deep convection was most frequently observed in Bangladesh and northeastern India, and verification scores exceeded global ensemble scores for heavy precipitation rates. These results demonstrate that plausible forecasts of thunderstorm hazards can be attained with relatively low computational resources, thereby facilitating advancements in extreme weather forecasting services in historically underserved regions such as HKH. In early 2022, a custom version of HIWAT was installed at the Bangladesh Meteorological Department using in-house computational resources, providing regional ensemble forecast guidance in real time.
Abstract
Since the Paris Agreement, climate policy has focused on 1.5 and 2°C maximum global warming targets. However, the agreement lacks a formal definition of the 19th century “pre-industrial” temperature baseline for these targets. If global warming is estimated with respect to the 1850-1900 mean, as in the latest IPCC reports, uncertainty in early instrumental temperatures affects the quantification of total warming. Here, we analyse gridded datasets of instrumental observations together with large-scale climate reconstructions from tree-rings to evaluate 19th century baseline temperatures. From 1851-1900 warm season temperatures of the Northern Hemisphere extratropical landmasses were 0.20°C cooler than the 20th century mean, with a range of 0.14-0.26°C among three instrumental datasets. At the same time, proxy-based temperature reconstructions show on average 0.39°C colder conditions with a range of 0.19-0.55°C among six records. We show that anomalously low reconstructed temperatures at high latitudes are under-represented in the instrumental fields likely due to the lack of station records in these remote regions. The 19th century offset between warmer instrumental and colder reconstructed temperatures is reduced by one third if spatial coverage is reduced to those grid cells that overlap between the different temperature fields. The instrumental dataset from Berkeley Earth shows the smallest offset to the reconstructions indicating that additional stations included in this product, due to more liberal data selection, lead to cooler baseline temperatures. The limited early instrumental records and comparison with reconstructions suggest an overestimation of 19th century temperatures, which in turn further reduces the probability of achieving the Paris targets.
Abstract
Since the Paris Agreement, climate policy has focused on 1.5 and 2°C maximum global warming targets. However, the agreement lacks a formal definition of the 19th century “pre-industrial” temperature baseline for these targets. If global warming is estimated with respect to the 1850-1900 mean, as in the latest IPCC reports, uncertainty in early instrumental temperatures affects the quantification of total warming. Here, we analyse gridded datasets of instrumental observations together with large-scale climate reconstructions from tree-rings to evaluate 19th century baseline temperatures. From 1851-1900 warm season temperatures of the Northern Hemisphere extratropical landmasses were 0.20°C cooler than the 20th century mean, with a range of 0.14-0.26°C among three instrumental datasets. At the same time, proxy-based temperature reconstructions show on average 0.39°C colder conditions with a range of 0.19-0.55°C among six records. We show that anomalously low reconstructed temperatures at high latitudes are under-represented in the instrumental fields likely due to the lack of station records in these remote regions. The 19th century offset between warmer instrumental and colder reconstructed temperatures is reduced by one third if spatial coverage is reduced to those grid cells that overlap between the different temperature fields. The instrumental dataset from Berkeley Earth shows the smallest offset to the reconstructions indicating that additional stations included in this product, due to more liberal data selection, lead to cooler baseline temperatures. The limited early instrumental records and comparison with reconstructions suggest an overestimation of 19th century temperatures, which in turn further reduces the probability of achieving the Paris targets.
Abstract
Heatwaves in California manifest as both dry and humid events. While both forms have become more prevalent, recent studies have identified a shift towards more humid events. Understanding the complex interactions of each heatwave type with the urban heat island are crucial for impacts, but remain understudied. Here, we address this gap by contrasting how dry versus humid heatwaves shape the intra-urban heat of greater Los Angeles (LA) area. We used a consecutive contrasting set of heatwaves from 2020 as a case study: a prolonged humid heatwave in August and an extremely dry heatwave in September. We used MERRA2 reanalysis data to compare mesoscale dynamics, followed by high-resolution Weather Research Forecast modeling over urbanized Southern California. We employ moist thermodynamic variables to quantify heat stress and perform spatial clustering analysis to characterize the spatiotemporal intra-urban variability. We find that despite temperatures being 10±3°C hotter in the September heatwave, the wet bulb temperature, closely related to the risk of human heat stroke, was higher in August. While dry and humid heat display different spatial patterns, three distinct spatial clusters emerge based on non-heatwave local climates. But both types of heatwaves diminish the intra-urban heat stress variability. Valley areas such as San Bernardino and Riverside experience the worst impacts with up to 6±0.5°C of additional heat stress during heatwave nights. Our results highlight the need to account for the disparity in small-scale heatwave patterns across urban neighborhoods in designing policies for equitable climate action.
Abstract
Heatwaves in California manifest as both dry and humid events. While both forms have become more prevalent, recent studies have identified a shift towards more humid events. Understanding the complex interactions of each heatwave type with the urban heat island are crucial for impacts, but remain understudied. Here, we address this gap by contrasting how dry versus humid heatwaves shape the intra-urban heat of greater Los Angeles (LA) area. We used a consecutive contrasting set of heatwaves from 2020 as a case study: a prolonged humid heatwave in August and an extremely dry heatwave in September. We used MERRA2 reanalysis data to compare mesoscale dynamics, followed by high-resolution Weather Research Forecast modeling over urbanized Southern California. We employ moist thermodynamic variables to quantify heat stress and perform spatial clustering analysis to characterize the spatiotemporal intra-urban variability. We find that despite temperatures being 10±3°C hotter in the September heatwave, the wet bulb temperature, closely related to the risk of human heat stroke, was higher in August. While dry and humid heat display different spatial patterns, three distinct spatial clusters emerge based on non-heatwave local climates. But both types of heatwaves diminish the intra-urban heat stress variability. Valley areas such as San Bernardino and Riverside experience the worst impacts with up to 6±0.5°C of additional heat stress during heatwave nights. Our results highlight the need to account for the disparity in small-scale heatwave patterns across urban neighborhoods in designing policies for equitable climate action.
Abstract
Dynamical climate predictions are produced by assimilating observations and running ensemble simulations of Earth system models. This process is time-consuming and by the time the forecast is delivered, new observations are already available, making it obsolete from the release date. Moreover, producing such predictions is computationally demanding, and their production frequency is restricted. We tested the potential of a computationally cheap weighting average technique that can continuously adjust such probabilistic forecast—in between production intervals — using newly available data. The method estimates local positive weights computed with a Bayesian framework, favoring members closer to observations. We tested the approach with the Norwegian Climate Prediction Model (NorCPM), which assimilates monthly sea surface temperature (SST) and hydrographic profiles with the ensemble Kalman filter. By the time the NorCPM forecast is delivered operationally, a week of unused SST data is available. We demonstrate the benefit of our weighting method on retrospective hindcasts. The weighting method greatly enhanced the NorCPM hindcast skill compared to the standard equal weight approach up to a 2-month lead time (global correlation of 0.71 versus 0.55 at a 1-month lead time and 0.51 versus 0.45 at a 2-month lead time). The skill at a 1-month lead time is comparable to the accuracy of the EnKF analysis. We also show that weights determined using SST data can be used to improve the skill of other quantities, such as the sea-ice extent. Our approach can provide a continuous forecast between the intermittent forecast production cycle and be extended to other independent datasets.
Abstract
Dynamical climate predictions are produced by assimilating observations and running ensemble simulations of Earth system models. This process is time-consuming and by the time the forecast is delivered, new observations are already available, making it obsolete from the release date. Moreover, producing such predictions is computationally demanding, and their production frequency is restricted. We tested the potential of a computationally cheap weighting average technique that can continuously adjust such probabilistic forecast—in between production intervals — using newly available data. The method estimates local positive weights computed with a Bayesian framework, favoring members closer to observations. We tested the approach with the Norwegian Climate Prediction Model (NorCPM), which assimilates monthly sea surface temperature (SST) and hydrographic profiles with the ensemble Kalman filter. By the time the NorCPM forecast is delivered operationally, a week of unused SST data is available. We demonstrate the benefit of our weighting method on retrospective hindcasts. The weighting method greatly enhanced the NorCPM hindcast skill compared to the standard equal weight approach up to a 2-month lead time (global correlation of 0.71 versus 0.55 at a 1-month lead time and 0.51 versus 0.45 at a 2-month lead time). The skill at a 1-month lead time is comparable to the accuracy of the EnKF analysis. We also show that weights determined using SST data can be used to improve the skill of other quantities, such as the sea-ice extent. Our approach can provide a continuous forecast between the intermittent forecast production cycle and be extended to other independent datasets.
Abstract
This work evaluates the fidelity of various upper ocean turbulence parameterizations subject to realistic monsoon forcing and presents a finite-time ensemble vector (EV) method to better manage the design and numerical principles of these parameterizations. The EV method emphasizes the dynamics of a turbulence closure multi-model ensemble and is applied to evaluate ten different ocean surface boundary layer (OSBL) parameterizations within a single column (SC) model against two boundary layer large eddy simulations (LES). Both LES include realistic surface forcing, but one includes wind-driven shear turbulence only, while the other includes additional Stokes forcing through the wave-average equations that generates Langmuir turbulence. The finite-time EV framework focuses on what constitutes the local behavior of the mixed layer dynamical system and isolates the forcing and ocean state conditions where turbulence parameterizations most disagree. Identifying disagreement provides the potential to evaluate SC models comparatively against the LES. Observations collected during the 2018 Monsoon onset in the Bay of Bengal provide a case study to evaluate models under realistic and variable forcing conditions. The case study results highlight two regimes where models disagree a) during wind-driven deepening of the mixed layer and b) under strong diurnal forcing.
Abstract
This work evaluates the fidelity of various upper ocean turbulence parameterizations subject to realistic monsoon forcing and presents a finite-time ensemble vector (EV) method to better manage the design and numerical principles of these parameterizations. The EV method emphasizes the dynamics of a turbulence closure multi-model ensemble and is applied to evaluate ten different ocean surface boundary layer (OSBL) parameterizations within a single column (SC) model against two boundary layer large eddy simulations (LES). Both LES include realistic surface forcing, but one includes wind-driven shear turbulence only, while the other includes additional Stokes forcing through the wave-average equations that generates Langmuir turbulence. The finite-time EV framework focuses on what constitutes the local behavior of the mixed layer dynamical system and isolates the forcing and ocean state conditions where turbulence parameterizations most disagree. Identifying disagreement provides the potential to evaluate SC models comparatively against the LES. Observations collected during the 2018 Monsoon onset in the Bay of Bengal provide a case study to evaluate models under realistic and variable forcing conditions. The case study results highlight two regimes where models disagree a) during wind-driven deepening of the mixed layer and b) under strong diurnal forcing.
Abstract
The national upgrade of the operational weather radar network to include polarimetric capabilities has lead to numerous studies focusing on polarimetric radar signatures commonly observed in supercells. One such signature is the horizontal separation of regions of enhanced differential reflectivity (ZDR ) and specific differential phase (KDP ) values due to hydrometeor size sorting. Recent observational studies have shown that the orientation of this separation tends to be more perpendicular to storm motion in supercells that produce tornadoes. Although this finding has potential operational utility, the physical relationship between this observed radar signature and tornadic potential is not known. This study uses an ensemble of supercell simulations initialized with tornadic and nontornadic environments to investigate this connection. The tendency for tornadic supercells to have a more perpendicular separation orientation was reproduced, although to a lesser degree. This difference in orientation angles was caused by stronger rearward storm-relative flow in the nontornadic supercells, leading to a rearward shift of precipitation and, therefore, the enhanced KDP region within the supercell. Further, this resulted in an unfavorable rearward shift of the negative buoyancy region, which led to an order of magnitude less baroclinic generation of circulation in the nontornadic simulations compared to tornadic simulations.
Abstract
The national upgrade of the operational weather radar network to include polarimetric capabilities has lead to numerous studies focusing on polarimetric radar signatures commonly observed in supercells. One such signature is the horizontal separation of regions of enhanced differential reflectivity (ZDR ) and specific differential phase (KDP ) values due to hydrometeor size sorting. Recent observational studies have shown that the orientation of this separation tends to be more perpendicular to storm motion in supercells that produce tornadoes. Although this finding has potential operational utility, the physical relationship between this observed radar signature and tornadic potential is not known. This study uses an ensemble of supercell simulations initialized with tornadic and nontornadic environments to investigate this connection. The tendency for tornadic supercells to have a more perpendicular separation orientation was reproduced, although to a lesser degree. This difference in orientation angles was caused by stronger rearward storm-relative flow in the nontornadic supercells, leading to a rearward shift of precipitation and, therefore, the enhanced KDP region within the supercell. Further, this resulted in an unfavorable rearward shift of the negative buoyancy region, which led to an order of magnitude less baroclinic generation of circulation in the nontornadic simulations compared to tornadic simulations.
Abstract
Large-scale distribution and variations in active salt-finger (SF) in the western North Pacific were examined by detecting the active SF with a vertical density ratio Rρ = 1 − 2 at depths of 10-300m using a monthly gridded hydrographic dataset from 2001 to 2016. The active SF is distributed mostly in March along 40°N around the Subarctic Boundary (SAB), where the mixed layer deepens northward and corresponds to the Central Mode Water formation site with a density of +0.02σ θ to +0.2σ θ of surface density and mainly in 26.1-26.4σ θ . This active SF along 40°N underwent seasonal variation and decayed rapidly from March to August from the shallower and less dense parts of the active SF with increasing mean density. The features of the active SF in March are consistent with the hypothesis that surface water with a horizontal density ratio RL = 1 − 2 is subducted and vertically superposed, resulting in an active SF. The mean density of the active SF in March is well correlated with the surface density with RL = 1 − 2, and both mean densities showed a decreasing trend from 2001 to 2016, following the surface warming trend (~0.057°C/yr) in the surface water with RL = 1 − 2. Large year-to-year variations in the active SF in March are explained by both horizontal and vertical extensions, and can be reproduced by four conditions: 1) from 1°N to 3°S of SAB, 2) RL =1-2, and 3) northward deepening of the mixed layer depth, and 4) the part with a density of +0.02σ θ to +0.2σ θ of surface density.
Abstract
Large-scale distribution and variations in active salt-finger (SF) in the western North Pacific were examined by detecting the active SF with a vertical density ratio Rρ = 1 − 2 at depths of 10-300m using a monthly gridded hydrographic dataset from 2001 to 2016. The active SF is distributed mostly in March along 40°N around the Subarctic Boundary (SAB), where the mixed layer deepens northward and corresponds to the Central Mode Water formation site with a density of +0.02σ θ to +0.2σ θ of surface density and mainly in 26.1-26.4σ θ . This active SF along 40°N underwent seasonal variation and decayed rapidly from March to August from the shallower and less dense parts of the active SF with increasing mean density. The features of the active SF in March are consistent with the hypothesis that surface water with a horizontal density ratio RL = 1 − 2 is subducted and vertically superposed, resulting in an active SF. The mean density of the active SF in March is well correlated with the surface density with RL = 1 − 2, and both mean densities showed a decreasing trend from 2001 to 2016, following the surface warming trend (~0.057°C/yr) in the surface water with RL = 1 − 2. Large year-to-year variations in the active SF in March are explained by both horizontal and vertical extensions, and can be reproduced by four conditions: 1) from 1°N to 3°S of SAB, 2) RL =1-2, and 3) northward deepening of the mixed layer depth, and 4) the part with a density of +0.02σ θ to +0.2σ θ of surface density.
Abstract
Over three decades, the U.S. Global Change Research Program (USGCRP) has developed an assessment process to integrate, evaluate, and interpret scientific findings on climate change and discuss uncertainties. In six USGCRP assessments, authors have identified research gaps, or topics that assessment authors indicated required more information or study. Examining research gaps on a continual and systematic basis can aid decisions about research projects, programmatic priorities, and strategic scientific visions. The methodology presented here addresses two aims: (1) identify and categorize research gaps within a searchable database, and (2) demonstrate use of the database to inform future science planning and assessment. Results include the top 10 database themes, 18 recurring topics across assessments, and a search example for vulnerability gaps. The benefits and limitations of this approach are discussed along with recommendations to improve future U.S. climate assessment products.
Abstract
Over three decades, the U.S. Global Change Research Program (USGCRP) has developed an assessment process to integrate, evaluate, and interpret scientific findings on climate change and discuss uncertainties. In six USGCRP assessments, authors have identified research gaps, or topics that assessment authors indicated required more information or study. Examining research gaps on a continual and systematic basis can aid decisions about research projects, programmatic priorities, and strategic scientific visions. The methodology presented here addresses two aims: (1) identify and categorize research gaps within a searchable database, and (2) demonstrate use of the database to inform future science planning and assessment. Results include the top 10 database themes, 18 recurring topics across assessments, and a search example for vulnerability gaps. The benefits and limitations of this approach are discussed along with recommendations to improve future U.S. climate assessment products.
Abstract
Coastal oceans host 40% of the world population and amount to $1.5 trillion of the global economy. Studying, managing, and developing the coastal regions require decades-long information about their environment. Long-term ocean measurements are, however, lacking for most coastal regions and often global reanalyses are used instead. These are however coarse in nature and tuned for the global circulations.
The Red Sea (RS) is a narrow basin connected to the Indian Ocean through the Bab-al-Mandab strait. Despite being the busiest commercial crossroad and hosting the world’s 3rd largest coral reef system, the RS lacks long-term observations. A recent increase in population and an unprecedented acceleration in governmental and industrial developments further emphasized the need for long-term datasets to support its development and the sustainability of its habitats, and to understand its response to a changing climate. Towards this end, we have generated a 20-year high-resolution reanalysis for the RS (RSRA) using a state-of-the-art ensemble data assimilation system incorporating available observations.
Compared to global reanalyses, RSRA provides a markedly better description of the RS general and mesoscale circulation features, their variability, and trends. In particular, RSRA accurately captures the three-layer summer transport through the Bab-al-Mandab, simulated as two-layer transport by some global reanalyses. It further reproduces the seasonal anomalies, whereas global reanalyses misidentify some seasons as anomalous. Global reanalyses further overestimate the interannual variations in salinity, misrepresent the trend in temperature, and underestimate the trend in sea level. Our study clearly emphasizes the importance of generating dedicated high-resolution regional ocean reanalyses.
Abstract
Coastal oceans host 40% of the world population and amount to $1.5 trillion of the global economy. Studying, managing, and developing the coastal regions require decades-long information about their environment. Long-term ocean measurements are, however, lacking for most coastal regions and often global reanalyses are used instead. These are however coarse in nature and tuned for the global circulations.
The Red Sea (RS) is a narrow basin connected to the Indian Ocean through the Bab-al-Mandab strait. Despite being the busiest commercial crossroad and hosting the world’s 3rd largest coral reef system, the RS lacks long-term observations. A recent increase in population and an unprecedented acceleration in governmental and industrial developments further emphasized the need for long-term datasets to support its development and the sustainability of its habitats, and to understand its response to a changing climate. Towards this end, we have generated a 20-year high-resolution reanalysis for the RS (RSRA) using a state-of-the-art ensemble data assimilation system incorporating available observations.
Compared to global reanalyses, RSRA provides a markedly better description of the RS general and mesoscale circulation features, their variability, and trends. In particular, RSRA accurately captures the three-layer summer transport through the Bab-al-Mandab, simulated as two-layer transport by some global reanalyses. It further reproduces the seasonal anomalies, whereas global reanalyses misidentify some seasons as anomalous. Global reanalyses further overestimate the interannual variations in salinity, misrepresent the trend in temperature, and underestimate the trend in sea level. Our study clearly emphasizes the importance of generating dedicated high-resolution regional ocean reanalyses.
Abstract
In addition to measuring forecast accuracy in terms of errors in a tropical system’s forecast track and other meteorological characteristics, it is important to measure the impact of those errors on society. With this in mind, the authors designed a coupled natural-human modeling framework with high-level representations of the natural hazard (hurricane), the human system (information flow, evacuation decisions), the built environment (road infrastructure), and connections between elements (forecasts and warning information, traffic). Using the model, this article begins exploring how tropical cyclone forecast errors impact evacuations, and in doing so, builds towards the development of new verification approaches. Specifically, the authors implement track errors representative of 2007 and 2022, and create situations with unexpected rapid intensification and/or rapid onset, and evaluate their impact on evacuations across real and hypothetical forecast scenarios (e.g., Hurricane Irma, Hurricane Dorian making landfall across east Florida). The results provide first-order evidence that 1) reduced forecast track errors across the 2007–2022 period translate to improvements in evacuation outcomes across these cases and 2) that unexpected rapid intensification and/or rapid onset scenarios can reduce evacuation rates, and increase traffic, across the most impacted areas. In exploring these relationships, the results demonstrate how experiments with coupled natural-human models can offer a societally relevant complement to traditional metrics of forecast accuracy. In doing so, this work points toward further development of natural-human models and associated methodologies to address these types of questions and improve forecast verification across the weather enterprise.
Abstract
In addition to measuring forecast accuracy in terms of errors in a tropical system’s forecast track and other meteorological characteristics, it is important to measure the impact of those errors on society. With this in mind, the authors designed a coupled natural-human modeling framework with high-level representations of the natural hazard (hurricane), the human system (information flow, evacuation decisions), the built environment (road infrastructure), and connections between elements (forecasts and warning information, traffic). Using the model, this article begins exploring how tropical cyclone forecast errors impact evacuations, and in doing so, builds towards the development of new verification approaches. Specifically, the authors implement track errors representative of 2007 and 2022, and create situations with unexpected rapid intensification and/or rapid onset, and evaluate their impact on evacuations across real and hypothetical forecast scenarios (e.g., Hurricane Irma, Hurricane Dorian making landfall across east Florida). The results provide first-order evidence that 1) reduced forecast track errors across the 2007–2022 period translate to improvements in evacuation outcomes across these cases and 2) that unexpected rapid intensification and/or rapid onset scenarios can reduce evacuation rates, and increase traffic, across the most impacted areas. In exploring these relationships, the results demonstrate how experiments with coupled natural-human models can offer a societally relevant complement to traditional metrics of forecast accuracy. In doing so, this work points toward further development of natural-human models and associated methodologies to address these types of questions and improve forecast verification across the weather enterprise.
