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Abstract
Well-known problems trouble coupled general circulation models of the eastern Atlantic and Pacific Ocean basins. Model climates are significantly more symmetric about the equator than is observed. Model sea surface temperatures are biased warm south and southeast of the equator, and the atmosphere is too rainy within a band south of the equator. Near-coastal eastern equatorial SSTs are too warm, producing a zonal SST gradient in the Atlantic opposite in sign to that observed. The U.S. Climate Variability and Predictability Program (CLIVAR) Eastern Tropical Ocean Synthesis Working Group (WG) has pursued an updated assessment of coupled model SST biases, focusing on the surface energy balance components, on regional error sources from clouds, deep convection, winds, and ocean eddies; on the sensitivity to model resolution; and on remote impacts. Motivated by the assessment, the WG makes the following recommendations: 1) encourage identification of the specific parameterizations contributing to the biases in individual models, as these can be model dependent; 2) restrict multimodel intercomparisons to specific processes; 3) encourage development of high-resolution coupled models with a concurrent emphasis on parameterization development of finer-scale ocean and atmosphere features, including low clouds; 4) encourage further availability of all surface flux components from buoys, for longer continuous time periods, in persistently cloudy regions; and 5) focus on the eastern basin coastal oceanic upwelling regions, where further opportunities for observational–modeling synergism exist.
Abstract
Well-known problems trouble coupled general circulation models of the eastern Atlantic and Pacific Ocean basins. Model climates are significantly more symmetric about the equator than is observed. Model sea surface temperatures are biased warm south and southeast of the equator, and the atmosphere is too rainy within a band south of the equator. Near-coastal eastern equatorial SSTs are too warm, producing a zonal SST gradient in the Atlantic opposite in sign to that observed. The U.S. Climate Variability and Predictability Program (CLIVAR) Eastern Tropical Ocean Synthesis Working Group (WG) has pursued an updated assessment of coupled model SST biases, focusing on the surface energy balance components, on regional error sources from clouds, deep convection, winds, and ocean eddies; on the sensitivity to model resolution; and on remote impacts. Motivated by the assessment, the WG makes the following recommendations: 1) encourage identification of the specific parameterizations contributing to the biases in individual models, as these can be model dependent; 2) restrict multimodel intercomparisons to specific processes; 3) encourage development of high-resolution coupled models with a concurrent emphasis on parameterization development of finer-scale ocean and atmosphere features, including low clouds; 4) encourage further availability of all surface flux components from buoys, for longer continuous time periods, in persistently cloudy regions; and 5) focus on the eastern basin coastal oceanic upwelling regions, where further opportunities for observational–modeling synergism exist.
Abstract
The polar regions have been attracting more and more attention in recent years, fueled by the perceptible impacts of anthropogenic climate change. Polar climate change provides new opportunities, such as shorter shipping routes between Europe and East Asia, but also new risks such as the potential for industrial accidents or emergencies in ice-covered seas. Here, it is argued that environmental prediction systems for the polar regions are less developed than elsewhere. There are many reasons for this situation, including the polar regions being (historically) lower priority, with fewer in situ observations, and with numerous local physical processes that are less well represented by models. By contrasting the relative importance of different physical processes in polar and lower latitudes, the need for a dedicated polar prediction effort is illustrated. Research priorities are identified that will help to advance environmental polar prediction capabilities. Examples include an improvement of the polar observing system; the use of coupled atmosphere–sea ice–ocean models, even for short-term prediction; and insight into polar–lower-latitude linkages and their role for forecasting. Given the enormity of some of the challenges ahead, in a harsh and remote environment such as the polar regions, it is argued that rapid progress will only be possible with a coordinated international effort. More specifically, it is proposed to hold a Year of Polar Prediction (YOPP) from mid-2017 to mid-2019 in which the international research and operational forecasting communites will work together with stakeholders in a period of intensive observing, modeling, prediction, verification, user engagement, and educational activities.
Abstract
The polar regions have been attracting more and more attention in recent years, fueled by the perceptible impacts of anthropogenic climate change. Polar climate change provides new opportunities, such as shorter shipping routes between Europe and East Asia, but also new risks such as the potential for industrial accidents or emergencies in ice-covered seas. Here, it is argued that environmental prediction systems for the polar regions are less developed than elsewhere. There are many reasons for this situation, including the polar regions being (historically) lower priority, with fewer in situ observations, and with numerous local physical processes that are less well represented by models. By contrasting the relative importance of different physical processes in polar and lower latitudes, the need for a dedicated polar prediction effort is illustrated. Research priorities are identified that will help to advance environmental polar prediction capabilities. Examples include an improvement of the polar observing system; the use of coupled atmosphere–sea ice–ocean models, even for short-term prediction; and insight into polar–lower-latitude linkages and their role for forecasting. Given the enormity of some of the challenges ahead, in a harsh and remote environment such as the polar regions, it is argued that rapid progress will only be possible with a coordinated international effort. More specifically, it is proposed to hold a Year of Polar Prediction (YOPP) from mid-2017 to mid-2019 in which the international research and operational forecasting communites will work together with stakeholders in a period of intensive observing, modeling, prediction, verification, user engagement, and educational activities.
Abstract
Led by NOAA’s Storm Prediction Center and National Severe Storms Laboratory, annual spring forecasting experiments (SFEs) in the Hazardous Weather Testbed test and evaluate cutting-edge technologies and concepts for improving severe weather prediction through intensive real-time forecasting and evaluation activities. Experimental forecast guidance is provided through collaborations with several U.S. government and academic institutions, as well as the Met Office. The purpose of this article is to summarize activities, insights, and preliminary findings from recent SFEs, emphasizing SFE 2015. Several innovative aspects of recent experiments are discussed, including the 1) use of convection-allowing model (CAM) ensembles with advanced ensemble data assimilation, 2) generation of severe weather outlooks valid at time periods shorter than those issued operationally (e.g., 1–4 h), 3) use of CAMs to issue outlooks beyond the day 1 period, 4) increased interaction through software allowing participants to create individual severe weather outlooks, and 5) tests of newly developed storm-attribute-based diagnostics for predicting tornadoes and hail size. Additionally, plans for future experiments will be discussed, including the creation of a Community Leveraged Unified Ensemble (CLUE) system, which will test various strategies for CAM ensemble design using carefully designed sets of ensemble members contributed by different agencies to drive evidence-based decision-making for near-future operational systems.
Abstract
Led by NOAA’s Storm Prediction Center and National Severe Storms Laboratory, annual spring forecasting experiments (SFEs) in the Hazardous Weather Testbed test and evaluate cutting-edge technologies and concepts for improving severe weather prediction through intensive real-time forecasting and evaluation activities. Experimental forecast guidance is provided through collaborations with several U.S. government and academic institutions, as well as the Met Office. The purpose of this article is to summarize activities, insights, and preliminary findings from recent SFEs, emphasizing SFE 2015. Several innovative aspects of recent experiments are discussed, including the 1) use of convection-allowing model (CAM) ensembles with advanced ensemble data assimilation, 2) generation of severe weather outlooks valid at time periods shorter than those issued operationally (e.g., 1–4 h), 3) use of CAMs to issue outlooks beyond the day 1 period, 4) increased interaction through software allowing participants to create individual severe weather outlooks, and 5) tests of newly developed storm-attribute-based diagnostics for predicting tornadoes and hail size. Additionally, plans for future experiments will be discussed, including the creation of a Community Leveraged Unified Ensemble (CLUE) system, which will test various strategies for CAM ensemble design using carefully designed sets of ensemble members contributed by different agencies to drive evidence-based decision-making for near-future operational systems.
Abstract
This article summarizes research and risk reduction that will inform acquisition decisions regarding NOAA’s future national operational weather radar network. A key alternative being evaluated is polarimetric phased-array radar (PAR). Research indicates PAR can plausibly achieve fast, adaptive volumetric scanning, with associated benefits for severe-weather warning performance. We assess these benefits using storm observations and analyses, observing system simulation experiments, and real radar-data assimilation studies. Changes in the number and/or locations of radars in the future network could improve coverage at low altitude. Analysis of benefits that might be so realized indicates the possibility for additional improvement in severe-weather and flash-flood warning performance, with associated reduction in casualties. Simulations are used to evaluate techniques for rapid volumetric scanning and assess data quality characteristics of PAR. Finally, we describe progress in developing methods to compensate for polarimetric variable estimate biases introduced by electronic beam-steering. A research-to-operations (R2O) strategy for the PAR alternative for the WSR-88D replacement network is presented.
Abstract
This article summarizes research and risk reduction that will inform acquisition decisions regarding NOAA’s future national operational weather radar network. A key alternative being evaluated is polarimetric phased-array radar (PAR). Research indicates PAR can plausibly achieve fast, adaptive volumetric scanning, with associated benefits for severe-weather warning performance. We assess these benefits using storm observations and analyses, observing system simulation experiments, and real radar-data assimilation studies. Changes in the number and/or locations of radars in the future network could improve coverage at low altitude. Analysis of benefits that might be so realized indicates the possibility for additional improvement in severe-weather and flash-flood warning performance, with associated reduction in casualties. Simulations are used to evaluate techniques for rapid volumetric scanning and assess data quality characteristics of PAR. Finally, we describe progress in developing methods to compensate for polarimetric variable estimate biases introduced by electronic beam-steering. A research-to-operations (R2O) strategy for the PAR alternative for the WSR-88D replacement network is presented.
Although continental-scale plumes of Asian dust and pollution reduce the amount of solar radiation reaching the earth's surface and perturb the chemistry of the atmosphere, our ability to quantify these effects has been limited by a lack of critical observations, particularly of layers above the surface. Comprehensive surface, airborne, shipboard, and satellite measurements of Asian aerosol chemical composition, size, optical properties, and radiative impacts were performed during the Asian Pacific Regional Aerosol Characterization Experiment (ACE-Asia) study. Measurements within a massive Chinese dust storm at numerous widely spaced sampling locations revealed the highly complex structure of the atmosphere, in which layers of dust, urban pollution, and biomass- burning smoke may be transported long distances as distinct entities or mixed together. The data allow a first-time assessment of the regional climatic and atmospheric chemical effects of a continental-scale mixture of dust and pollution. Our results show that radiative flux reductions during such episodes are sufficient to cause regional climate change.
Although continental-scale plumes of Asian dust and pollution reduce the amount of solar radiation reaching the earth's surface and perturb the chemistry of the atmosphere, our ability to quantify these effects has been limited by a lack of critical observations, particularly of layers above the surface. Comprehensive surface, airborne, shipboard, and satellite measurements of Asian aerosol chemical composition, size, optical properties, and radiative impacts were performed during the Asian Pacific Regional Aerosol Characterization Experiment (ACE-Asia) study. Measurements within a massive Chinese dust storm at numerous widely spaced sampling locations revealed the highly complex structure of the atmosphere, in which layers of dust, urban pollution, and biomass- burning smoke may be transported long distances as distinct entities or mixed together. The data allow a first-time assessment of the regional climatic and atmospheric chemical effects of a continental-scale mixture of dust and pollution. Our results show that radiative flux reductions during such episodes are sufficient to cause regional climate change.
Abstract
The Arctic environment is changing, increasing the vulnerability of local communities and ecosystems, and impacting its socio-economic landscape. In this context, weather and climate prediction systems can be powerful tools to support strategic planning and decision-making at different time horizons. This article presents several success stories from the H2020 project APPLICATE on how to advance Arctic weather and seasonal climate prediction, synthesizing the key lessons learned throughout the project and providing recommendations for future model and forecast system development.
Abstract
The Arctic environment is changing, increasing the vulnerability of local communities and ecosystems, and impacting its socio-economic landscape. In this context, weather and climate prediction systems can be powerful tools to support strategic planning and decision-making at different time horizons. This article presents several success stories from the H2020 project APPLICATE on how to advance Arctic weather and seasonal climate prediction, synthesizing the key lessons learned throughout the project and providing recommendations for future model and forecast system development.
Abstract
One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s National Severe Storms Laboratory and Storm Prediction Center and conducted in the National Oceanic and Atmospheric Administration’s (NOAA) Hazardous Weather Testbed, is documenting performance characteristics of experimental, convection-allowing modeling systems (CAMs). Since 2007, the number of CAMs (including CAM ensembles) examined in the SFEs has increased dramatically, peaking at six different CAM ensembles in 2015. Meanwhile, major advances have been made in creating, importing, processing, verifying, and developing tools for analyzing and visualizing these large and complex datasets. However, progress toward identifying optimal CAM ensemble configurations has been inhibited because the different CAM systems have been independently designed, making it difficult to attribute differences in performance characteristics. Thus, for the 2016 SFE, a much more coordinated effort among many collaborators was made by agreeing on a set of model specifications (e.g., model version, grid spacing, domain size, and physics) so that the simulations contributed by each collaborator could be combined to form one large, carefully designed ensemble known as the Community Leveraged Unified Ensemble (CLUE). The 2016 CLUE was composed of 65 members contributed by five research institutions and represents an unprecedented effort to enable an evidence-driven decision process to help guide NOAA’s operational modeling efforts. Eight unique experiments were designed within the CLUE framework to examine issues directly relevant to the design of NOAA’s future operational CAM-based ensembles. This article will highlight the CLUE design and present results from one of the experiments examining the impact of single versus multicore CAM ensemble configurations.
Abstract
One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s National Severe Storms Laboratory and Storm Prediction Center and conducted in the National Oceanic and Atmospheric Administration’s (NOAA) Hazardous Weather Testbed, is documenting performance characteristics of experimental, convection-allowing modeling systems (CAMs). Since 2007, the number of CAMs (including CAM ensembles) examined in the SFEs has increased dramatically, peaking at six different CAM ensembles in 2015. Meanwhile, major advances have been made in creating, importing, processing, verifying, and developing tools for analyzing and visualizing these large and complex datasets. However, progress toward identifying optimal CAM ensemble configurations has been inhibited because the different CAM systems have been independently designed, making it difficult to attribute differences in performance characteristics. Thus, for the 2016 SFE, a much more coordinated effort among many collaborators was made by agreeing on a set of model specifications (e.g., model version, grid spacing, domain size, and physics) so that the simulations contributed by each collaborator could be combined to form one large, carefully designed ensemble known as the Community Leveraged Unified Ensemble (CLUE). The 2016 CLUE was composed of 65 members contributed by five research institutions and represents an unprecedented effort to enable an evidence-driven decision process to help guide NOAA’s operational modeling efforts. Eight unique experiments were designed within the CLUE framework to examine issues directly relevant to the design of NOAA’s future operational CAM-based ensembles. This article will highlight the CLUE design and present results from one of the experiments examining the impact of single versus multicore CAM ensemble configurations.
