Abstract
This paper describes the vision and objectives for evaluation and quality control of datasets available on the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S). The CDS is a cloud service providing reliable, open, and free access to climate datasets from many different sources, including observations and derived climate data records, global and regional reanalysis products, and output from global and regional climate models. CDS data and information are widely used to monitor climate change, for policy development, environmental impact studies, business plans, public awareness, and many other purposes. CDS users include policymakers, technical consultants, and data scientists who are climate literate but not necessarily specialized in climate science. A key objective for C3S is to simplify data discovery and access and to ensure that all information provided is fit for purpose. The role of evaluation and quality control is to generate useful statements about the technical and scientific quality of the datasets available on the CDS, presented in a form that can be mapped directly to user requirements associated with specific application contexts. The ambition is to enable users to select, locate, access, and process climate data that are fit for purpose, simply by expressing their requirements in their own words.
Abstract
This paper describes the vision and objectives for evaluation and quality control of datasets available on the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S). The CDS is a cloud service providing reliable, open, and free access to climate datasets from many different sources, including observations and derived climate data records, global and regional reanalysis products, and output from global and regional climate models. CDS data and information are widely used to monitor climate change, for policy development, environmental impact studies, business plans, public awareness, and many other purposes. CDS users include policymakers, technical consultants, and data scientists who are climate literate but not necessarily specialized in climate science. A key objective for C3S is to simplify data discovery and access and to ensure that all information provided is fit for purpose. The role of evaluation and quality control is to generate useful statements about the technical and scientific quality of the datasets available on the CDS, presented in a form that can be mapped directly to user requirements associated with specific application contexts. The ambition is to enable users to select, locate, access, and process climate data that are fit for purpose, simply by expressing their requirements in their own words.
Abstract
Forecasts of sea surface temperature anomalies (SSTAs) provide essential information to stakeholders of marine resources in coastal ecosystems, such as the California Current Large Marine Ecosystem (CCLME), at management-relevant monthly-to-annual time scales. Diagnosing dynamical sources of predictability and the mechanisms differentiating skill among forecasts is required for verification and improvement in operational forecasting systems. Using retrospective forecasts (1982–2020) from a four-member subset of the North American Multi-Model Ensemble (NMME), we evaluate the conditional skill of SSTA forecasts in the CCLME at monthly resolution for lead times up to 10.5 months. Forecasts from ensemble members with relatively small SSTA errors at shorter lead times retain higher skill at longer lead times, with the most substantial and long-lasting increases for forecasts initialized in the fall and early spring. The “best” low-error SSTA forecasts are characterized by increased skill in the prediction of North Pacific atmospheric circulation [sea level pressure (SLP) and 200-hPa geopotential height] the month prior to the evaluation of SSTA errors in the CCLME and exhibit more realistic progressions of anomalous SLP. The Pacific meridional mode (PMM) emerges as a diagnostic of skillful North Pacific atmosphere–ocean coupling, as forecasts that correctly simulate the PMM and its associated SLP variability increase the SSTA prediction skill in the CCLME in the fall through spring. Predictable coupled ocean–atmosphere modes provide a target for enhancing predictability with early detection of the onset of a deterministic progression emerging from stochastic atmospheric variability.
Significance Statement
Global forecast systems provide near-term climate predictions that inform the management of marine resources, such as those of the California Current Large Marine Ecosystem. In this study, we probe the processes which lead forecasts to succeed or fail at predicting sea surface temperatures in the California Current at seasonal time scales among retrospective forecasts from the North American Multimodel Ensemble. We demonstrate that forecasts which best simulate sea surface temperatures at the earliest lead times sustain advantages in forecast skill and find that correctly simulating extratropical atmospheric circulation increases the predictive skill of sea surface temperatures in the northeast Pacific in the following lead times. Our results offer North Pacific atmospheric circulation as a target for forecast model improvement that would additionally enhance ocean forecasts.
Abstract
Forecasts of sea surface temperature anomalies (SSTAs) provide essential information to stakeholders of marine resources in coastal ecosystems, such as the California Current Large Marine Ecosystem (CCLME), at management-relevant monthly-to-annual time scales. Diagnosing dynamical sources of predictability and the mechanisms differentiating skill among forecasts is required for verification and improvement in operational forecasting systems. Using retrospective forecasts (1982–2020) from a four-member subset of the North American Multi-Model Ensemble (NMME), we evaluate the conditional skill of SSTA forecasts in the CCLME at monthly resolution for lead times up to 10.5 months. Forecasts from ensemble members with relatively small SSTA errors at shorter lead times retain higher skill at longer lead times, with the most substantial and long-lasting increases for forecasts initialized in the fall and early spring. The “best” low-error SSTA forecasts are characterized by increased skill in the prediction of North Pacific atmospheric circulation [sea level pressure (SLP) and 200-hPa geopotential height] the month prior to the evaluation of SSTA errors in the CCLME and exhibit more realistic progressions of anomalous SLP. The Pacific meridional mode (PMM) emerges as a diagnostic of skillful North Pacific atmosphere–ocean coupling, as forecasts that correctly simulate the PMM and its associated SLP variability increase the SSTA prediction skill in the CCLME in the fall through spring. Predictable coupled ocean–atmosphere modes provide a target for enhancing predictability with early detection of the onset of a deterministic progression emerging from stochastic atmospheric variability.
Significance Statement
Global forecast systems provide near-term climate predictions that inform the management of marine resources, such as those of the California Current Large Marine Ecosystem. In this study, we probe the processes which lead forecasts to succeed or fail at predicting sea surface temperatures in the California Current at seasonal time scales among retrospective forecasts from the North American Multimodel Ensemble. We demonstrate that forecasts which best simulate sea surface temperatures at the earliest lead times sustain advantages in forecast skill and find that correctly simulating extratropical atmospheric circulation increases the predictive skill of sea surface temperatures in the northeast Pacific in the following lead times. Our results offer North Pacific atmospheric circulation as a target for forecast model improvement that would additionally enhance ocean forecasts.
Abstract
During early boreal winter, the extra-tropical atmospheric circulation is influenced by Rossby waves propagating from the Indian Ocean towards the North Atlantic-European (NAE) regions, resulting in a North Atlantic Oscillation (NAO)-like pattern. The mechanisms driving these teleconnections are not well understood and are crucial for improving model skills. This study investigates these mechanisms using the ERA5 dataset and tests the predictive capabilities of the ECMWF-SEAS5 hindcast, exploring potential reasons for a weak model response. Linear regression methods are employed to examine the extra-tropical links with the Indian Ocean dipole (IOD), both in isolation and in combination with the El Niño-Southern Oscillation (ENSO). Our findings demonstrate a connection between October IOD sea surface temperature anomalies and December Indian Ocean precipitation patterns. Furthermore, a correlation between the October IOD and December NAO time series suggests a link between the IOD and NAE circulation. The early winter European response to a positive IOD is characterized by a north-south precipitation dipole and a large positive surface air temperature anomaly. Positive feedback from transient eddy forcing reinforces the wavenumber-3-like propagation across extra-tropical regions, with ENSO playing a minor role compared to the IOD. This phenomenon is particularly evident in regions such as the North Pacific and North Atlantic, where wave energy propagation is intensified. Although SEAS5 replicates the NAO response, its magnitude is significantly weaker. The model struggles to simulate the delayed rainfall dipole response to the IOD accurately and shows structural discrepancies compared to reanalysis data.
Abstract
During early boreal winter, the extra-tropical atmospheric circulation is influenced by Rossby waves propagating from the Indian Ocean towards the North Atlantic-European (NAE) regions, resulting in a North Atlantic Oscillation (NAO)-like pattern. The mechanisms driving these teleconnections are not well understood and are crucial for improving model skills. This study investigates these mechanisms using the ERA5 dataset and tests the predictive capabilities of the ECMWF-SEAS5 hindcast, exploring potential reasons for a weak model response. Linear regression methods are employed to examine the extra-tropical links with the Indian Ocean dipole (IOD), both in isolation and in combination with the El Niño-Southern Oscillation (ENSO). Our findings demonstrate a connection between October IOD sea surface temperature anomalies and December Indian Ocean precipitation patterns. Furthermore, a correlation between the October IOD and December NAO time series suggests a link between the IOD and NAE circulation. The early winter European response to a positive IOD is characterized by a north-south precipitation dipole and a large positive surface air temperature anomaly. Positive feedback from transient eddy forcing reinforces the wavenumber-3-like propagation across extra-tropical regions, with ENSO playing a minor role compared to the IOD. This phenomenon is particularly evident in regions such as the North Pacific and North Atlantic, where wave energy propagation is intensified. Although SEAS5 replicates the NAO response, its magnitude is significantly weaker. The model struggles to simulate the delayed rainfall dipole response to the IOD accurately and shows structural discrepancies compared to reanalysis data.
Abstract
Based on the observations and the phase 6 of Coupled Model Intercomparison Project (CMIP6) multimodel simulations, we conducted a detection and attribution analysis for the observed changes in intensity and frequency indices of extreme precipitation during 1961–2014 over the whole of China and within distinct climate regions across the country. A space–time analysis is simultaneously applied in detection so that spatial structure on the signals is considered. Results show that the CMIP6 models can simulate the observed general increases of extreme precipitation indices during the historical period except for the drying trends from southwestern to northeastern China. The anthropogenic (ANT) signal is detectable and attributable to the observed increase of extreme precipitation over China, with human-induced greenhouse gas (GHG) increases being the dominant contributor. Additionally, we also detected the ANT and GHG signals in China’s temperate continental, subtropical–tropical monsoon, and plateau mountain climate zones, demonstrating the role of human activity in historical extreme precipitation changes on much smaller spatial scales.
Significance Statement
The observed intensification of extreme precipitation globally has been attributed to human influences. Here, we demonstrate that anthropogenic forcing has discernably intensified extreme precipitation over the period 1961–2014, over China and in three of its four climate zones, with human-induced greenhouse gas increases being the dominant contributor. Our results strengthen the body of evidence that greenhouse gas increases are intensifying extreme precipitation by quantifying their role in observed changes at smaller regional scales than previously reported.
Abstract
Based on the observations and the phase 6 of Coupled Model Intercomparison Project (CMIP6) multimodel simulations, we conducted a detection and attribution analysis for the observed changes in intensity and frequency indices of extreme precipitation during 1961–2014 over the whole of China and within distinct climate regions across the country. A space–time analysis is simultaneously applied in detection so that spatial structure on the signals is considered. Results show that the CMIP6 models can simulate the observed general increases of extreme precipitation indices during the historical period except for the drying trends from southwestern to northeastern China. The anthropogenic (ANT) signal is detectable and attributable to the observed increase of extreme precipitation over China, with human-induced greenhouse gas (GHG) increases being the dominant contributor. Additionally, we also detected the ANT and GHG signals in China’s temperate continental, subtropical–tropical monsoon, and plateau mountain climate zones, demonstrating the role of human activity in historical extreme precipitation changes on much smaller spatial scales.
Significance Statement
The observed intensification of extreme precipitation globally has been attributed to human influences. Here, we demonstrate that anthropogenic forcing has discernably intensified extreme precipitation over the period 1961–2014, over China and in three of its four climate zones, with human-induced greenhouse gas increases being the dominant contributor. Our results strengthen the body of evidence that greenhouse gas increases are intensifying extreme precipitation by quantifying their role in observed changes at smaller regional scales than previously reported.
Abstract
Nowcasting hail size poses a major challenge in operational practice due to physical limitations of weather radar technology once hailstones are sufficiently large to enter the resonance scattering regime. Numerous radar-based hail size proxies have been derived in recent decades, but their performance is generally poor in identifying giant hail (≥ 10 cm). Using a novel thunderstorm updraft detection method, we examine the updraft characteristics of hailstorms in the U.S. Great Plains based on a NEXRAD dataset of 114 hail events between 2013 and 2023. We find that some radar-derived variables within the detected updraft are well suited for discriminating between small (1.0 - 3.0 cm) and severe (≥ 3.5 cm) hail, e.g. minimum co-polar cross-correlation coefficient in the mid-level updraft, whereas other radar metrics such as the area of reflectivity > 50 dBZ in the upper portion of the updraft suggest the presence of giant hail. However, the statistical distributions of each variable overlap for different hail sizes and there is no single metric which performs well across the entire hail size spectrum. Therefore, we trained a Random Forest model to nowcast hail size categories using a multitude of these radar metrics. The model shows promising performance for discriminating hail sizes > 5 cm but requires further refinement for smaller hail. We showcase the model’s capabilities for a set of hailstorms in the Great Plains.
Abstract
Nowcasting hail size poses a major challenge in operational practice due to physical limitations of weather radar technology once hailstones are sufficiently large to enter the resonance scattering regime. Numerous radar-based hail size proxies have been derived in recent decades, but their performance is generally poor in identifying giant hail (≥ 10 cm). Using a novel thunderstorm updraft detection method, we examine the updraft characteristics of hailstorms in the U.S. Great Plains based on a NEXRAD dataset of 114 hail events between 2013 and 2023. We find that some radar-derived variables within the detected updraft are well suited for discriminating between small (1.0 - 3.0 cm) and severe (≥ 3.5 cm) hail, e.g. minimum co-polar cross-correlation coefficient in the mid-level updraft, whereas other radar metrics such as the area of reflectivity > 50 dBZ in the upper portion of the updraft suggest the presence of giant hail. However, the statistical distributions of each variable overlap for different hail sizes and there is no single metric which performs well across the entire hail size spectrum. Therefore, we trained a Random Forest model to nowcast hail size categories using a multitude of these radar metrics. The model shows promising performance for discriminating hail sizes > 5 cm but requires further refinement for smaller hail. We showcase the model’s capabilities for a set of hailstorms in the Great Plains.
Research and Application of Destriping Algorithm Based on Weighted Block Sparse Representation to Fengyun Satellite Imager DataAbstract
In remote sensing imaging systems, stripe noise is a pervasive issue primarily caused by the inconsistent response of multiple detectors. Stripe noise not only affects image quality but also severely hinders subsequent quantitative derived products and applications. Therefore, it is crucial to eliminate stripe noise while preserving detailed structure information in order to enhance image quality. Although existing destriping methods have achieved certain effects to some extent, they still face problems such as loss of image details, image blur, and ringing artifacts. To address these issues, this study proposes an image stripe correction algorithm based on weighted block sparse representation. This research applies techniques such as differential low-rank constraint and edge weight factor to remove stripe noise while retaining image detail information. The algorithm also uses the alternating direction method of multipliers (ADMM) to solve the minimax concave penalty (MCP)-regularized least squares optimization problem model, improving the processing efficiency of the model. The results of this study have been applied and validated in imager data from the Medium Resolution Spectral Imager (MERSI-II) onboard Fengyun-3D satellite, the multichannel scanning radiometer [Advanced Geosynchronous Radiation Imager (AGRI)] onboard Fengyun-4A satellite, and precipitation microwave radiometer [Microwave Radiation Imager-Rainfall Mission (MWRI-RM)] onboard Fengyun-3G. Compared to typical stripe correction methods, the proposed method achieves better stripe removal while preserving image detail information. The destriped image data can be used to generate high-quality quantitative products for various applications. Overall, by combining insights from prior research and innovative techniques, this study provides a more effective and robust solution to the stripe noise problem in remote sensing and weather forecast.
Significance Statement
Stripe noise is a persistent problem in remote sensing imaging systems, hindering image quality and subsequent analysis. This study introduces a novel algorithm based on weighted block sparse representation to remove stripe noise while preserving image details. By incorporating techniques like differential low-rank constraint and edge weight factor, our method achieves superior stripe removal. The proposed approach was validated using data from MERSI-II and AGRI satellites, showing its effectiveness in enhancing image quality. This research provides a more robust solution to the stripe noise issue, benefiting various applications in remote sensing and weather forecast.
Abstract
In remote sensing imaging systems, stripe noise is a pervasive issue primarily caused by the inconsistent response of multiple detectors. Stripe noise not only affects image quality but also severely hinders subsequent quantitative derived products and applications. Therefore, it is crucial to eliminate stripe noise while preserving detailed structure information in order to enhance image quality. Although existing destriping methods have achieved certain effects to some extent, they still face problems such as loss of image details, image blur, and ringing artifacts. To address these issues, this study proposes an image stripe correction algorithm based on weighted block sparse representation. This research applies techniques such as differential low-rank constraint and edge weight factor to remove stripe noise while retaining image detail information. The algorithm also uses the alternating direction method of multipliers (ADMM) to solve the minimax concave penalty (MCP)-regularized least squares optimization problem model, improving the processing efficiency of the model. The results of this study have been applied and validated in imager data from the Medium Resolution Spectral Imager (MERSI-II) onboard Fengyun-3D satellite, the multichannel scanning radiometer [Advanced Geosynchronous Radiation Imager (AGRI)] onboard Fengyun-4A satellite, and precipitation microwave radiometer [Microwave Radiation Imager-Rainfall Mission (MWRI-RM)] onboard Fengyun-3G. Compared to typical stripe correction methods, the proposed method achieves better stripe removal while preserving image detail information. The destriped image data can be used to generate high-quality quantitative products for various applications. Overall, by combining insights from prior research and innovative techniques, this study provides a more effective and robust solution to the stripe noise problem in remote sensing and weather forecast.
Significance Statement
Stripe noise is a persistent problem in remote sensing imaging systems, hindering image quality and subsequent analysis. This study introduces a novel algorithm based on weighted block sparse representation to remove stripe noise while preserving image details. By incorporating techniques like differential low-rank constraint and edge weight factor, our method achieves superior stripe removal. The proposed approach was validated using data from MERSI-II and AGRI satellites, showing its effectiveness in enhancing image quality. This research provides a more robust solution to the stripe noise issue, benefiting various applications in remote sensing and weather forecast.
Abstract
In the Southern Hemisphere, Earth system models project an intensification of winter storm tracks by the end of the twenty-first century. Previous studies using idealized models showed that storm track intensity saturates with increasing temperatures, suggesting that the intensification of the winter storm tracks might not continue further with increasing greenhouse gases. Here, we examine the response of midlatitude winter storm tracks in the Southern Hemisphere to increasing CO2 from two to eight times preindustrial concentrations in more realistic Earth system models. We find that at high CO2 levels (beyond 4×CO2), winter storm tracks no longer exhibit an intensification across the extratropics. Instead, they shift poleward, weakening the storm tracks at lower midlatitudes and strengthening at higher midlatitudes. By analyzing the eddy kinetic energy (EKE) budget, the nonlinear storm-track response to an increase in CO2 levels in the lower midlatitudes is found to stem from a scale-dependent conversion of eddy available potential energy to EKE. Specifically, in the lower midlatitudes, this energy conversion acts to oppositely change the EKE of long and short scales at low CO2 levels, but at high CO2 levels, it mostly reduces the EKE of shorter scales, resulting in a poleward shift of the storms. Furthermore, we identify a “tug of war” between the upper and lower temperature changes as the primary driver of the nonlinear-scale-dependent EKE response in the lower midlatitudes. Our results suggest that in the highest emission scenarios beyond the twenty-first century, the storm tracks’ response may differ in magnitude and latitudinal distribution from projected changes by 2100.
Significance Statement
The Southern Hemisphere winter storm track is projected to intensify by the end of the century, with the most significant intensification occurring in the higher midlatitudes. However, we show that the intensification is not a linear function of the radiative forcing associated with increasing CO2 levels. In fact, our study shows a poleward shift at very high CO2 levels, with the storm track moving southward. This suggests that the Southern Hemisphere winter storm track may require time-sensitive adaptation strategies, as the impacts of global warming on the storm track may not be a linear function of CO2 concentration in the atmosphere.
Abstract
In the Southern Hemisphere, Earth system models project an intensification of winter storm tracks by the end of the twenty-first century. Previous studies using idealized models showed that storm track intensity saturates with increasing temperatures, suggesting that the intensification of the winter storm tracks might not continue further with increasing greenhouse gases. Here, we examine the response of midlatitude winter storm tracks in the Southern Hemisphere to increasing CO2 from two to eight times preindustrial concentrations in more realistic Earth system models. We find that at high CO2 levels (beyond 4×CO2), winter storm tracks no longer exhibit an intensification across the extratropics. Instead, they shift poleward, weakening the storm tracks at lower midlatitudes and strengthening at higher midlatitudes. By analyzing the eddy kinetic energy (EKE) budget, the nonlinear storm-track response to an increase in CO2 levels in the lower midlatitudes is found to stem from a scale-dependent conversion of eddy available potential energy to EKE. Specifically, in the lower midlatitudes, this energy conversion acts to oppositely change the EKE of long and short scales at low CO2 levels, but at high CO2 levels, it mostly reduces the EKE of shorter scales, resulting in a poleward shift of the storms. Furthermore, we identify a “tug of war” between the upper and lower temperature changes as the primary driver of the nonlinear-scale-dependent EKE response in the lower midlatitudes. Our results suggest that in the highest emission scenarios beyond the twenty-first century, the storm tracks’ response may differ in magnitude and latitudinal distribution from projected changes by 2100.
Significance Statement
The Southern Hemisphere winter storm track is projected to intensify by the end of the century, with the most significant intensification occurring in the higher midlatitudes. However, we show that the intensification is not a linear function of the radiative forcing associated with increasing CO2 levels. In fact, our study shows a poleward shift at very high CO2 levels, with the storm track moving southward. This suggests that the Southern Hemisphere winter storm track may require time-sensitive adaptation strategies, as the impacts of global warming on the storm track may not be a linear function of CO2 concentration in the atmosphere.
Abstract
Flight-level airborne observations have often detected gravity waves with horizontal wavelengths
Abstract
Flight-level airborne observations have often detected gravity waves with horizontal wavelengths
Abstract
An Ensemble Tangent Linear Model (ETLM) is applied to a cloud physics scheme used in the Navy Global Environmental Model (NAVGEM). The ensemble is created using 3-hour forecasts from the Ensemble Transform method used in the NAVGEM data assimilation system. The model states are saved before and after applying the cloud physics parameterization (which includes condensation/evaporation of cloud ice and cloud liquid water and stratiform precipitation), and these states are used to construct linearized model tendencies for temperature, specific humidity, cloud liquid water, and cloud ice water. We examine separately the application of the ETLM to cloud physics components that are explicitly local versus non-local. For the local components, an ETLM is built using a single grid point. ETLMs from 50 to 1000 members are tested, and skillful forecasts can be obtained for both local and non-local physics even with a moderate sized ensemble (e.g., 100 members). At 1000 members, the globally-averaged forecast error reductions (relative to persistence errors) are ∼40% for temperature, water vapor, and cloud liquid water and ∼30% for cloud ice. When initial perturbations are reduced by a factor of 0.1, the error reductions increase to ∼65% for all variables. For physics with non-local components (stratiform precipitation) the covariances that comprise the ETLM are localized with a Schur product matrix using a Gaussian localization shape with tunable length. The optimal lengths increased with ensemble size from ∼2-3 km for 50 members to ∼10 km for 1000 members. ETLMs for “all cloud physics” are also constructed and evaluated.
Abstract
An Ensemble Tangent Linear Model (ETLM) is applied to a cloud physics scheme used in the Navy Global Environmental Model (NAVGEM). The ensemble is created using 3-hour forecasts from the Ensemble Transform method used in the NAVGEM data assimilation system. The model states are saved before and after applying the cloud physics parameterization (which includes condensation/evaporation of cloud ice and cloud liquid water and stratiform precipitation), and these states are used to construct linearized model tendencies for temperature, specific humidity, cloud liquid water, and cloud ice water. We examine separately the application of the ETLM to cloud physics components that are explicitly local versus non-local. For the local components, an ETLM is built using a single grid point. ETLMs from 50 to 1000 members are tested, and skillful forecasts can be obtained for both local and non-local physics even with a moderate sized ensemble (e.g., 100 members). At 1000 members, the globally-averaged forecast error reductions (relative to persistence errors) are ∼40% for temperature, water vapor, and cloud liquid water and ∼30% for cloud ice. When initial perturbations are reduced by a factor of 0.1, the error reductions increase to ∼65% for all variables. For physics with non-local components (stratiform precipitation) the covariances that comprise the ETLM are localized with a Schur product matrix using a Gaussian localization shape with tunable length. The optimal lengths increased with ensemble size from ∼2-3 km for 50 members to ∼10 km for 1000 members. ETLMs for “all cloud physics” are also constructed and evaluated.
