Future projections of hurricane intensity in the southeastern U.S.: sensitivity to different Pseudo-Global Warming methods

Tropical cyclones are prone to cause fatalities and damages reaching far into billions of US Dollars. There is evidence that these events could intensify under ongoing global warming, and accordingly disaster prevention and adaptation strategies are necessary. We apply Pseudo-Global Warming (PGW) as a computational cost-efficient alternative to conventional long-term modeling, enabling the assessment of historical events under future storylines. Not many studies specifically assess the sensitivity of PGW in the context of short-term extreme events in the United States. In an attempt to close this gap, this study explores the sensitivity of hurricane intensity to different PGW configurations, including a purely thermodynamic, a dynamic, and a more comprehensive modulation of initial and boundary conditions using the Weather and Research and Forecasting Model (WRF). The climate perturbations are calculated using two individual CMIP6 climate models with a relatively low and high temperature change and the CMIP6 ensemble mean, all under SSP5-8.5. WRF was set up in a two-way nesting framework using domains of 25 and 5 km spatial resolution. Results show that high uncertainties exist between the thermodynamic and dynamic approaches, whereas the deviations between the dynamic approach and the comprehensive variable modulation are low. Hurricanes modeled under the thermodynamic approach tend toward higher intensities, whereas the perturbation of wind under the dynamic approach may impose unwanted effects on cyclogenesis, for example due to increased vertical wind shear. The highest sensitivity, however, stems from the selected CMIP6 model. We conclude that PGW studies should thoroughly assess uncertainties imposed by the PGW scheme, similar to those imposed by model parameterizations. All simulation results suggest an increase in maximum wind speeds and precipitation for the high impact model and the ensemble mean. An unfolding of the inspected events in a warmer world could therefore exacerbate the impacts on nature and society

Added value of an atmospheric circulation pattern‐based statistical downscaling approach for daily precipitation distributions in complex terrain

Reliable prediction of heavy precipitation events causing floods in a world of changing climate is crucial for the development of appropriate adaption strategies. Many attempts to provide such predictions have already been conducted but there is still much potential for improvement left. This is particularly true for statistical downscaling of heavy precipitation due to changes present in the corresponding atmospheric drivers. In this study, a circulation pattern (CP) conditional downscaling to the station level is proposed which considers occurring frequency changes of CPs. Following a strict circulation-to-environment approach we use atmospheric predictors to derive CPs. Subsequently, precipitation observations are used to derive CP conditional cumulative distribution functions (CDFs) of daily precipitation. Raw precipitation time series are sampled from these CDFs. Bias correction is applied to the sampled time series with quantile mapping (QM) and parametric transfer functions (PTFs) as methods being tested. The added value of this CP conditional downscaling approach is evaluated against the corresponding common non-CP conditional approach. The performance evaluation is conducted by using Kling–Gupta Efficiency (KGE), root mean squared error (RMSE), and mean absolute error (MAE) metrics. In both cases the applied bias correction is identical. Potential added value can therefore only be attributed to the CP conditioning. It can be shown that the proposed CP conditional downscaling approach is capable of yielding more reliable and accurate downscaled daily precipitation time series in comparison to a non-CP conditional approach. This can be seen in particular for the extreme parts of the distribution. Above the 95th percentile, an average performance gain of +0.24 and a maximum gain of +0.6 in terms of KGE is observed. These findings support the assumption of conserving and utilizing atmospheric information through CPs can be beneficial for more reliable statistical precipitation downscaling. Due to the availability of these atmospheric predictors in climate model output, the presented method is potentially suitable for downscaling precipitation projections

Added value of an atmospheric circulation pattern‐based statistical downscaling approach for daily precipitation distributions in complex terrain

Reliable prediction of heavy precipitation events causing floods in a world of changing climate is crucial for the development of appropriate adaption strategies. Many attempts to provide such predictions have already been conducted but there is still much potential for improvement left. This is particularly true for statistical downscaling of heavy precipitation due to changes present in the corresponding atmospheric drivers. In this study, a circulation pattern (CP) conditional downscaling to the station level is proposed which considers occurring frequency changes of CPs. Following a strict circulation-to-environment approach we use atmospheric predictors to derive CPs. Subsequently, precipitation observations are used to derive CP conditional cumulative distribution functions (CDFs) of daily precipitation. Raw precipitation time series are sampled from these CDFs. Bias correction is applied to the sampled time series with quantile mapping (QM) and parametric transfer functions (PTFs) as methods being tested. The added value of this CP conditional downscaling approach is evaluated against the corresponding common non-CP conditional approach. The performance evaluation is conducted by using Kling–Gupta Efficiency (KGE), root mean squared error (RMSE), and mean absolute error (MAE) metrics. In both cases the applied bias correction is identical. Potential added value can therefore only be attributed to the CP conditioning. It can be shown that the proposed CP conditional downscaling approach is capable of yielding more reliable and accurate downscaled daily precipitation time series in comparison to a non-CP conditional approach. This can be seen in particular for the extreme parts of the distribution. Above the 95th percentile, an average performance gain of +0.24 and a maximum gain of +0.6 in terms of KGE is observed. These findings support the assumption of conserving and utilizing atmospheric information through CPs can be beneficial for more reliable statistical precipitation downscaling. Due to the availability of these atmospheric predictors in climate model output, the presented method is potentially suitable for downscaling precipitation projections

An ensemble-based assessment of bias adjustment performance, changes in hydrometeorological predictors and compound extreme events in EAS-CORDEX

The effectiveness of adaptive measures tackling the effects of climate change is dependent on robust climate projections. This becomes even more important in the face of intensifying extreme events. One example of these events is flooding, which embodies a major threat to highly vulnerable coastal urban areas. This includes eastern Asia, where multiple coastal megacities are located, e.g. Shanghai and Shenzhen. While the ability of general circulation models (GCMs) and regional climate models (RCMs) to project atmospheric changes associated with these events has improved, systematic errors (biases) remain. This study therefore assess capabilities of improving the quality of regional climate projections for eastern Asia. This is performed by evaluating an ensemble consisting of bias adjustment methods, GCM-RCM model runs and future emission scenarios based on representative concentration pathways (RCP) obtained from EAS-CORDEX. We show that bias adjustment significantly improves the quality of model output and best results are obtained by applying quantile delta mapping. Based on these results we evaluate potential future changes in crucial hydrometeorological predictors, univariate extreme events and compound extreme events, focusing on high wind speeds and extreme precipitation. Key findings include an increase in daily maximum temperature of 1.5 to nearly 4 C, depending on the scenario, as well as increased levels of precipitation under RCP 8.5. Furthermore, a distinct intensification of extreme events including high temperatures and heavy precipitation is detected and this increase exceeds the increase of the overall mean of these predictors. The annual number of compound events including heavy precipitation and extreme wind speeds shows a significant increase of up to 50% for RCP 8.5 in the South China Sea as well as the adjacent coastal areas

Key ingredients in regional climate modelling for improving the representation of typhoon tracks and intensities

There is evidence of an increased frequency of rapid intensification events of tropical cyclones (TCs) in global offshore regions. This will not only result in increased peak wind speeds but may lead to more intense heavy precipitation events, leading to flooding in coastal regions. Therefore, high impacts are expected for urban agglomerations in coastal regions such as the densely populated Pearl River Delta (PRD) in China. Regional climate models (RCMs) such as the Weather Research and Forecasting (WRF) model are state-of-the-art tools commonly applied to predict TCs. However, typhoon simulations are connected with high uncertainties due to the high number of parameterization schemes of relevant physical processes (including possible interactions between the parameterization schemes) such as cumulus (CU) and microphysics (MP), as well as other crucial model settings such as domain setup, initial times, and spectral nudging. Since previous studies mostly focus on either individual typhoon cases or individual parameterization schemes, in this study a more comprehensive analysis is provided by considering four different typhoons of different intensity categories with landfall near the PRD, i.e. Typhoon Neoguri (2008), Typhoon Hagupit (2008), Typhoon Hato (2017), and Typhoon Usagi (2013), as well as two different schemes for CU and MP, respectively. Moreover, the impact of the model initialization and the driving data is studied by using three different initial times and two spectral nudging settings. Compared with the best-track reference data, the results show that the four typhoons show some consistency. For track bias, nudging only horizontal wind has a positive effect on reducing the track distance bias; for intensity, compared with a model explicitly resolving cumulus convection, i.e. without cumulus parameterization (CuOFF; nudging potential temperature and horizontal wind; late initial time), using the Kain–Fritsch scheme (KF; nudging only horizontal wind; early initial time) configuration shows relatively lower minimum sea level pressures and higher maximum wind speeds, which means stronger typhoon intensity. Intensity shows less sensitivity to two MP schemes compared with the CuOFF, nudging, and initial time settings. Furthermore, we found that compared with the CuOFF, using the KF scheme shows a relatively larger latent heat flux and higher equivalent potential temperature, providing more energy to typhoon development and inducing stronger TCs. This study could be used as a reference to configure WRF with the model\u27s different combinations of schemes for historical and future TC simulations and also contributes to a better understanding of the performance of principal TC structures

An ensemble-based assessment of bias adjustment performance, changes in hydrometeorological predictors and compound extreme events in EAS-CORDEX

The effectiveness of adaptive measures tackling the effects of climate change is dependent on robust climate projections. This becomes even more important in the face of intensifying extreme events. One example of these events is flooding, which embodies a major threat to highly vulnerable coastal urban areas. This includes eastern Asia, where multiple coastal megacities are located, e.g. Shanghai and Shenzhen. While the ability of general circulation models (GCMs) and regional climate models (RCMs) to project atmospheric changes associated with these events has improved, systematic errors (biases) remain. This study therefore assess capabilities of improving the quality of regional climate projections for eastern Asia. This is performed by evaluating an ensemble consisting of bias adjustment methods, GCM-RCM model runs and future emission scenarios based on representative concentration pathways (RCP) obtained from EAS-CORDEX. We show that bias adjustment significantly improves the quality of model output and best results are obtained by applying quantile delta mapping. Based on these results we evaluate potential future changes in crucial hydrometeorological predictors, univariate extreme events and compound extreme events, focusing on high wind speeds and extreme precipitation. Key findings include an increase in daily maximum temperature of 1.5 to nearly 4 °C, depending on the scenario, as well as increased levels of precipitation under RCP 8.5. Furthermore, a distinct intensification of extreme events including high temperatures and heavy precipitation is detected and this increase exceeds the increase of the overall mean of these predictors. The annual number of compound events including heavy precipitation and extreme wind speeds shows a significant increase of up to 50% for RCP 8.5 in the South China Sea as well as the adjacent coastal areas

Future projections of hurricane intensity in the southeastern U.S.: sensitivity to different Pseudo-Global Warming methods

Tropical cyclones are prone to cause fatalities and damages reaching far into billions of US Dollars. There is evidence that these events could intensify under ongoing global warming, and accordingly disaster prevention and adaptation strategies are necessary. We apply Pseudo-Global Warming (PGW) as a computational cost-efficient alternative to conventional long-term modeling, enabling the assessment of historical events under future storylines. Not many studies specifically assess the sensitivity of PGW in the context of short-term extreme events in the United States. In an attempt to close this gap, this study explores the sensitivity of hurricane intensity to different PGW configurations, including a purely thermodynamic, a dynamic, and a more comprehensive modulation of initial and boundary conditions using the Weather and Research and Forecasting Model (WRF). The climate perturbations are calculated using two individual CMIP6 climate models with a relatively low and high temperature change and the CMIP6 ensemble mean, all under SSP5-8.5. WRF was set up in a two-way nesting framework using domains of 25 and 5 km spatial resolution. Results show that high uncertainties exist between the thermodynamic and dynamic approaches, whereas the deviations between the dynamic approach and the comprehensive variable modulation are low. Hurricanes modeled under the thermodynamic approach tend toward higher intensities, whereas the perturbation of wind under the dynamic approach may impose unwanted effects on cyclogenesis, for example due to increased vertical wind shear. The highest sensitivity, however, stems from the selected CMIP6 model. We conclude that PGW studies should thoroughly assess uncertainties imposed by the PGW scheme, similar to those imposed by model parameterizations. All simulation results suggest an increase in maximum wind speeds and precipitation for the high impact model and the ensemble mean. An unfolding of the inspected events in a warmer world could therefore exacerbate the impacts on nature and society

PESFOR-W: Improving the design and environmental effectiveness of woodlands for water Payments for Ecosystem Services

ABSTRACT: The EU Water Framework Directive aims to ensure restoration of Europe?s water bodies to ?good ecological status? by 2027. Many Member States will struggle to meet this target, with around half of EU river catchments currently reporting below standard water quality. Diffuse pollution from agriculture represents a major pressure, affecting over 90% of river basins. Accumulating evidence shows that recent improvements to agricultural practices are benefiting water quality but in many cases will be insufficient to achieve WFD objectives. There is growing support for land use change to help bridge the gap, with a particular focus on targeted tree planting to intercept and reduce the delivery of diffuse pollutants to water. This form of integrated catchment management offers multiple benefits to society but a significant cost to landowners and managers. New economic instruments, in combination with spatial targeting, need to be developed to ensure cost effective solutions - including tree planting for water benefits - are realised. Payments for Ecosystem Services (PES) are flexible, incentive-based mechanisms that could play an important role in promoting land use change to deliver water quality targets. The PESFOR-W COST Action will consolidate learning from existing woodlands for water PES schemes in Europe and help standardize approaches to evaluating the environmental effectiveness and cost-effectiveness of woodland measures. It will also create a European network through which PES schemes can be facilitated, extended and improved, for example by incorporating other ecosystem services linking with aims of the wider forestscarbon policy nexus