Search

Climate change has caused a more heterogeneous distribution of extreme precipitation, leading to the deterioration of drought-flood abrupt alternation (DFAA) events and soil erosion. The security and sustainable utilization of water and soil resources are severely threatened. Previous studies have focused separately on these two aspects, failing to comprehensively consider their interrelationship. Furthermore, these studies often rely on climate models with convection parameterization, resulting in substantial uncertainties. We use a 4 km convective permitting model (CPM) to generate reliable outputs for extreme precipitation. By incorporating the long-cycle drought-flood abrupt transition index and the Revised Universal Soil Loss Equation model, we analyze the changes in DFAA events and soil erosion, as well as their interconnectedness. The results show that the CPM outperforms coarse-resolution climate models in reproducing erosive rainfall and simulating the heterogeneous distribution of rainfall by capturing convection processes effectively. Projections indicate an escalation in the occurrence of DFAA events and soil erosion due to the more uneven distribution of precipitation. Specifically, the frequency of flood-to-drought (FTD) events within DFAA is projected to rise from 3.8 to 4.7 per decade. Soil loss is projected to increase 61 t·hm−2·a−1, with 73% of the area experiencing more severe soil erosion intensity. A positive correlation between FTD events and soil erosion is found throughout most of Guangdong. This correlation will be further amplified with an increase in the frequency of FTD events. Consequently, existing soil conservation measures are rendered inadequate, presenting substantial challenges for climate change adaptation and ecological protection in the region.

In this study, we investigated the performance of RegCM4 with a hydrostatic or non‐hydrostatic dynamic core and different land surface models at simulating the mean and extreme precipitation during 1991–1999 in China and the Beijing–Tianjin–Hebei urban agglomeration (JJJ) in northern China. The resolutions were 12 km with the whole of China included and 3 km nested over the JJJ region. The observed daily precipitation data comprised the CN05.1 data set from China Meteorological Administration. First, RegCM4 with a hydrostatic dynamic core forced by the Max Planck Institute Earth System Model (MPI‐ESM1.2‐HR) that participated in CMIP6 was used to simulate the east Asia area at a resolution of 12 km. Next, the RegCM4 results with 12 km resolution forced RegCM4 with a non‐hydrostatic dynamic core (RegCM4‐NH) to run over the JJJ region with a 3 km convection‐permitting scale through one‐way nesting. The results showed that RegCM4 with a hydrostatic and non‐hydrostatic core could obtain more detailed seasonal and annual precipitation in China and the JJJ region relative to MPI‐ESM1.2‐HR although their performance varied over different regions. RegCM4 with the community land surface model 4.5 (CLM4.5) generally performed better at simulating the mean and extreme precipitation over the four subregions of China than that with the Biosphere–Atmosphere Transfer Scheme because of more detailed descriptions of land surface processes of CLM4.5. RegCM4‐NH with convection‐permitting resolution improved the simulations of heavy precipitation extremes over JJJ compared with RegCM4 with a hydrostatic core and resolution of 12 km. In addition, RegCM4‐NH with CLM4.5 performed better at simulating heavy precipitation extremes over JJJ compared with all other simulations, with higher correlation coefficients and larger relative root mean squared errors. [ABSTRACT FROM AUTHOR]

A novel convection permitting modelling framework that combines a pseudo‐global warming approach with continuously forced deep soil moisture from prescribed perturbation storylines is applied in the Eastern European Alpine region and parts of the Pannonian Basin to investigate soil moisture precipitation (SMP) feedbacks on summertime precipitation and the feedbacks' role under changed climate conditions. A set of 1‐year convection‐permitting (3 km horizontal grid spacing) soil moisture sensitivity simulations with the regional climate model of the Consortium for Small‐Scale Modelling in Climate Mode are conducted. In order to account for global warming, end‐of‐the‐century climate change effects from four global climate models, projecting the greenhouse gas concentration scenario RCP 8.5, are imprinted. The simulations reveal that (1) the locations of precipitation events are highly sensitive to soil moisture modifications while intensities and the internal structure of precipitation events are nearly unaffected and (2) high precipitation intensities are more likely in combinations with positive temporal but distinctive (either strong positive or strong negative) spatial SMP coupling. Low precipitation intensities are in favour of combinations of negative temporal and positive spatial coupling. The analyses suggest that soil moisture at a given time acts as a guiding field for the location of the next precipitation event. Interestingly, this behaviour is independent of climate change, although the coupling strength's increase is 1.5–1.7 times larger than expected from linear climate change scaling when climate becomes 50% dryer. Finally, it is found that (1) local deviations in the climate change signal of summertime precipitation in the range of up to ±40% are caused by uncertainty in deep soil moisture in the range of ±10% and (2) these local deviations in the climate change signal are dominated by soil moisture uncertainty in future climate conditions. [ABSTRACT FROM AUTHOR]

Compound heat–precipitation events exert significant impacts on severe weather occurrences. Intense vertical air movement, driving vigorous convection, primarily contributes to the formation of extreme precipitation. Nevertheless, such compound events' temporal and spatial variation patterns at convection-permitting resolutions remain inadequately explored. This study assesses the performance of the Convection-Permitting Model (CPM) against a model of convection parameterization while investigating the spatial dynamics of compound heat–precipitation events in Guangdong, China. Our findings indicate that the CPM exhibits heightened reliability and precision in simulating temperature and precipitation patterns, especially in extreme precipitation simulation, which would be highly underestimated without a convection-permitting process. Projections from the CPM reveal that, across historical and future periods, the occurrence frequency and fraction of T-P events (instances of extreme heat followed by extreme precipitation) surpass those of P-T events (occurrences of extreme precipitation followed by extreme heat). For T-P events, the CPM exhibits better capability in capturing high-frequency occurrence areas, whereas the results of the relatively low-resolution model show less distinct spatial variations. Both types of events exhibit noticeable upward trends yearly within each period. By the close of this century, the provincial average frequency of P-T events is anticipated to decrease from 20.32 times to 14.55 times. In contrast, the frequency of T-P events is projected to increase from 87.7 times to 101.38 times. These projected changes underscore the shifting dynamics of compound heat–precipitation events in the study region. [ABSTRACT FROM AUTHOR]

Extratropical windstorms can lead to extreme impacts in the UK; however, future changes in windstorm frequency and intensity are uncertain due to natural variability and lack of model consensus. Further uncertainty arises due to unresolved or poorly represented processes which can reduce the validity of traditional coarse resolution climate models. This study demonstrates the added value of higher resolution simulations in their representation of extreme windstorms. It also assesses future changes in windstorms in the first ensemble of convection-permitting model (CPM, 2.2 km grid spacing) projections over the UK, released as part of the UKCP18 project, and compares these to changes in the driving 60 km global climate model (GCM) and 12 km regional climate model (RCM). The representation of wind gusts in the CPM and RCM is improved with respect to ERA-Interim reanalysis data compared to observations. Both models are largely similar except over areas with orographic influences where the CPM provides greater detail and simulates higher wind gusts. In contrast, the GCM simulates lower wind gusts and underestimates windstorm intensity; the improvements from the RCM and CPM are partially linked to better representation of cold conveyor belts and sting jets at km-scale. The CPM projects an increase in the frequency of extreme windstorms. In line with a previous study, a large contribution comes from storms that develop sting jets which now seems to be a robust response of windstorms to a warming climate. Similar projected increases are also found in the coarser resolution models, although differences in projections exist at a regional scale in the CPM compared to the RCM and GCM. Overall, higher resolution simulations improve the representation of windstorms, offering greater detail at the local scale which may provide better information for impact modelling, though large-scale projected changes in the assessed storm severity index are insensitive to the model resolutions in the simulations tested here.

The Tibetan Plateau (TP) is often referred to as 'the Third Pole' and plays an essential role in the global climate. However, it remains challenging for most global and regional models to realistically simulate the characteristics of climate over the TP. In this study, two Weather Research and Forecasting model (WRF) experiments using spectral nudging with gray-zone (GZ9) and convection-permitting (CP3) resolution are conducted for summers from 2009 to 2018. The surface air temperature (T2m) and precipitation from the two simulations and the global reanalysis ERA5 are evaluated against in-situ observations. The results show that ERA5 has a general cold bias over southern TP, especially in maximum T2m (Tmax), and wet bias over whole TP. Both experiments can successfully capture the spatial pattern and daily variation of T2m and precipitation, though cold bias for temperature and dry bias for precipitation exist especially over the regions south of 35° N. Compared with ERA5, the added value of the two WRF experiments is mainly reflected in the reduced cold bias especially for Tmax with more improvement found in CP3 and the reduced wet bias. However, the ability of the convection-permitting WRF experiment in improving the simulation of precipitation seems limited when compared to the gray-zone WRF experiment, which may be related to the biases in physical parameterization and lack of representativeness of station observation. Further investigation into surface radiation budget reveals that the underestimation of net shortwave radiation contributes a lot to the cold bias of T2m over the southeastern TP in GZ9 which is improved in CP3. Compared with GZ9, CP3 shows that larger specific humidity at low-level (mid-high level) coexists with more precipitation (clouds) over the southern TP. This improvement is achieved by better depiction of topographic details, underlying surface and atmospheric processes, land–atmosphere interactions and so on, leading to stronger northward water vapor transport (WVT) in CP3, providing more water vapor for precipitation at surface and much wetter condition in the mid-high level. [ABSTRACT FROM AUTHOR]

Regional and local wind systems are often complex, particularly near coastal areas with a highly variable orography. Thus, the realistic representation of regional wind systems in weather and climate models is of strong relevance. Here, we evaluate the ability of a 13‐year convection‐permitting climate simulation in reproducing the interaction of several regional summer wind systems over the complex orography in the eastern Mediterranean region. The COSMO‐CLM simulations are driven by hourly ERA‐5 reanalysis and have a spatial resolution of 2.8 and 7.0 km. The simulated near‐surface wind fields are compared with unique very high‐resolution wind observations collected within the "Dead Sea Research Venue" project (DESERVE) and data from the Israel Meteorological Service synop network. The high‐resolution COSMO‐CLM simulations largely reproduce the main characteristics of the regional wind systems (Mediterranean and Dead Sea breeze, slope winds in the Judean Mountains and winds along the Jordan Rift valley), whereas ERA‐5 is only able to represent the Mediterranean Sea breeze. The high‐resolution simulations substantially improve the representation of regional winds, particularly over complex orography. Indeed, the 2.8 km simulation outperforms the 7.0 km run, on 88% of the days. Two mid‐July 2015 case studies show that only the 2.8 simulation can realistically simulate the penetration of the Mediterranean Sea Breeze into the Jordan Rift valley and complex interactions with other wind systems like the Dead Sea breeze. Our results may have profound implications for regional weather and climate prediction since very high‐resolution information seems to be necessary to reproduce the main summertime climatic features in this region. We envisage that such simulations may also be required at other regions with complex orography. [ABSTRACT FROM AUTHOR]

Recent studies over different geographical regions of the world have proven that regional climate models at the convection-permitting scale (CPMs) improve the simulation of precipitation in many aspects, such as the diurnal cycle, precipitation frequency, intensity, and extremes at daily—but even more at hourly—time scales. Here, we present an evaluation of climate simulations with the newly developed RegCM4-NH model run at the convection-permitting scale (CP-RegCM4-NH) for a decade-long period, over three domains covering a large European area. The simulations use a horizontal grid spacing of ~3 km and are driven by the ERA-Interim reanalysis through an intermediate driving RegCM4-NH simulation at ~12 km grid spacing with parameterized deep convection. The km-scale simulations are evaluated against a suite of hourly observation datasets with high spatial resolutions and are compared to the coarse-resolution driving simulation in order to assess improvements in precipitation from the seasonal to hourly scale. The results show that CP-RegCM4-NH produces a more realistic representation of precipitation than the coarse-resolution simulation over all domains. The most significant improvements were found for intensity, heavy precipitation, and precipitation frequency, both on daily and hourly time scales in all seasons. In general, CP-RegCM4-NH tends to correctly produce more intense precipitation and to reduce the frequency of events compared to the coarse-resolution one. On the daily scale, improvements in CP simulations are highly region dependent, with the best results over Italy, France, and Germany, and the largest biases over Switzerland, the Carpathians, and Greece, especially during the summer seasons. At the hourly scale, the improvement in CP simulations for precipitation intensity and spatial distribution is clearer than at the daily timescale. In addition, the representation of extreme events is clearly improved by CP-RegCM4-NH, particularly at the hourly time scale, although an overestimation over some subregions can be found. Although biases between the model simulations at the km-scale and observations still exist, this first application of CP-RegCM4-NH at high spatial resolution indicates a clear benefit of convection-permitting simulations and encourages further assessments of the added value of km-scale model configurations for regional climate change projections. [ABSTRACT FROM AUTHOR]

Physically based hydrologic models have been extensively used for hydroclimatic projections, but key challenges remain owing to the heavy computational burden and structural variability of physically based models. In this study, we develop a vine copula‐based polynomial chaos framework for improving multi‐model projections of hydroclimatic regimes at a convection‐permitting scale over the Dongjiang River Basin located in South China. Specifically, a deep neural network (DNN)‐based polynomial chaos expansion (PCE) is developed to significantly improve the efficiency of probabilistic hydrologic predictions. A vine copula multi‐model ensemble approach is also proposed to robustly combine hydrologic predictions generated from multiple DNN‐based PCEs to improve reliability and accuracy. To assess regional hydrologic responses to changing climate, multi‐decadal nested‐grid climate projections over the Guangdong‐Hong Kong‐Macao Greater Bay Area (GBA) are developed using the convection‐permitting Weather Research and Forecasting (WRF) model with 4‐km horizontal grid spacing. Our findings reveal that the DNN‐based PCEs achieve comparable performance to the physically based hydrologic predictions with an extremely low computational cost. The vine copula multi‐model ensemble approach outperforms the Bayesian model averaging (BMA) by generating more accurate and reliable hydrologic predictions. The developed framework and physical models also lead to consistent projections of future changes in streamflow regimes. Our findings reveal that the projected increases in the frequency and intensity of extreme precipitation can lead to substantial increases in flood magnitudes, but the increases may not be obvious for river basins affected by multiple reservoirs. Key Points: A deep neural network‐based polynomial chaos expansion was developed to improve the efficiency of probabilistic hydrologic predictionA vine copula multi‐model ensemble approach was proposed to improve the accuracy of ensemble hydrologic predictionConvection‐permitting climate simulations were conducted for improving the reliability of assessing regional hydrologic responses to changing climate [ABSTRACT FROM AUTHOR]

High latitude regions are undergoing substantial land use and land cover change (LULCC) arising partly from a changing climate (e.g. greening of the Arctic) and climate mitigation policies (e.g. afforestation). Despite these ongoing changes, the impacts of LULCC in high latitudes are poorly understood. Studies to reduce this knowledge deficit primarily deploy regional climate models (RCMs) as observations of key variables for LULCC studies are scarce in high latitude regions. As such it is important to understand the limitations of RCMs and identify best practices for designing regional climate modelling experiments for LULCC studies at high latitudes. In this study, twelve 10-year simulations are performed over the Scandinavian Peninsula; six at convection permitting scales (dx ∼ 3 km) and six at non-convection permitting scales (dx ∼ 15 km). Two of the convection permitting simulations model the present and future climate conditions over the Scandinavian Peninsula. The present-day simulation is comprehensively evaluated for multiple variables (e.g. surface air temperature at 2 m, precipitation) using multiple sources of observations (stations, reanalysis & blended satellite products) where available. Results from the model evaluation points to the need for further model improvement in simulating precipitation and related snow processes as well as the need for observations of surface energy fluxes at high latitudes for evaluation. The remaining eight simulations differ in terms of grid spacing, background climate state (present or future climate), and land cover (conversion of grasslands to evergreen needleleaf or mixed forest). Our study highlights the strengths and limitations of common RCM design considerations, such as simulation length (single year vs. multi-years), background climate state (present vs. future climate) and model resolution (convection permitting vs. non-convection permitting). A key recommendation is that high-latitude modeling studies of LULCC should prioritize computational resources for multi-decadal and ensemble simulations over short, single experiment simulations at convection permitting scales.

Climate extremes affected cities and their populations during the last decades. Future climate projections indicate climate extremes will increasingly impact urban areas during the 21st century. Humidity related fluctuations and extremes directly underpin convective processes, as well as can influence human health conditions. Regional climate models are a powerful tool to understand regional-to-local climate change processes for cities and their surroundings. Convection-permitting regional climate models, operating on very high resolutions, indicate improved simulation of convective extremes, particularly on sub-daily timescales and in regions with complex terrain such as cities. This research aims to understand how crossing spatial resolutions from ~12.5 km to ~3 km grid size affect humidity extremes and related variables under future climate change for urban areas and its surroundings. Taking Berlin and its surroundings as the case study area, the research identifies two categories of unprecedented future extreme atmospheric humidity conditions happening under 1.5 °C and 2.0 °C mean warming based on statistical distributions, respectively near surface specific humidity >0.02 kg/kg and near surface relative humidity

Abstract A fundamental issue when evaluating the simulation of precipitation is the difficulty of quantifying specific sources of errors and recognizing compensation of errors. We assess how well a large ensemble of high‐resolution simulations represents the precipitation associated with strong cyclones. We propose a framework to breakdown precipitation errors according to different dynamical (vertical velocity) and thermodynamical (vertically integrated water vapor) regimes and the frequency and intensity of precipitation. This approach approximates the error in the total precipitation of each regime as the sum of three terms describing errors in the large‐scale environmental conditions, the frequency of precipitation and its intensity. We show that simulations produce precipitation too often, that its intensity is too weak, that errors are larger for weak than for strong dynamical forcing and that biases in the vertically integrated water vapor can be large. Using the error breakdown presented above, we define four new error metrics differing on the degree to which they include the compensation of errors. We show that convection‐permitting simulations consistently improve the simulation of precipitation compared to coarser‐resolution simulations using parameterized convection, and that these improvements are revealed by our new approach but not by traditional metrics which can be affected by compensating errors. These results suggest that convection‐permitting models are more likely to produce better results for the right reasons. We conclude that the novel decomposition and error metrics presented in this study give a useful framework that provides physical insights about the sources of errors and a reliable quantification of errors.

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is the world's largest bay area in terms of land area and population, which has been increasingly suffering from weather and climate extremes under global warming. It is thus desired to produce reliable high-resolution climate information at a regional scale in order to enhance resilience to climate change over the GBA. For the first time, this study develops the multi-decadal nested-grid climate projections at a convection-permitting scale for the GBA, and assesses the abilities of the Weather Research and Forecasting (WRF) model with 36-, 12- and 4 km resolutions in representing precipitation, temperature and their extremes. Our findings indicate the added value of the convection-permitting WRF model for simulating the spring and summertime precipitation as well as extreme heavy rainfall events with daily amounts larger than 30 mm over the GBA. Increasing the spatial resolution of the WRF model does not necessarily lead to a significant improvement on temperature simulations. In addition, our findings reveal that the GBA is expected to have an increasing number of heavy and extreme heavy rainfall events by the end of the twenty-first century. Moreover, the GBA is projected to have a large temperature change across different seasons, and an enhanced warming will appear in autumn. The GBA is also expected to have more summer days with longer durations, thereby leading to an increasing risk of heatwaves and heat stress. [ABSTRACT FROM AUTHOR]

A fundamental issue when evaluating the simulation of precipitation is the difficulty of quantifying specific sources of errors and recognizing compensation of errors. We assess how well a large ensemble of high‐resolution simulations represents the precipitation associated with strong cyclones. We propose a framework to breakdown precipitation errors according to different dynamical (vertical velocity) and thermodynamical (vertically integrated water vapor) regimes and the frequency and intensity of precipitation. This approach approximates the error in the total precipitation of each regime as the sum of three terms describing errors in the large‐scale environmental conditions, the frequency of precipitation and its intensity. We show that simulations produce precipitation too often, that its intensity is too weak, that errors are larger for weak than for strong dynamical forcing and that biases in the vertically integrated water vapor can be large. Using the error breakdown presented above, we define four new error metrics differing on the degree to which they include the compensation of errors. We show that convection‐permitting simulations consistently improve the simulation of precipitation compared to coarser‐resolution simulations using parameterized convection, and that these improvements are revealed by our new approach but not by traditional metrics which can be affected by compensating errors. These results suggest that convection‐permitting models are more likely to produce better results for the right reasons. We conclude that the novel decomposition and error metrics presented in this study give a useful framework that provides physical insights about the sources of errors and a reliable quantification of errors. Plain Language Summary: The simulations of complex physical processes always entail various sources of errors. These errors can be of different sign and can consequently cancel each other out when using traditional performance metrics such as the bias error metric. We present a formal framework that allows us to approximate precipitation according to three terms that describe different aspects of the rainfall field including large‐scale environmental conditions and the frequency and intensity of rainfall. We apply the methodology to a large ensemble of high‐resolution simulations representing the precipitation associated with strong cyclones in eastern Australia. We show that simulations produce precipitation too often, with an intensity that is too weak leading to strong compensation. We further define new error metrics that explicitly quantify the degree of error compensation when simulating precipitation. We show that convection‐permitting simulations consistently improve the performance compared to coarser resolution simulations using parameterized convection and that these improvements are only revealed when using the new error metrics but are not apparent in traditional metrics (e.g., bias). Key Points: Multiple high‐resolution simulations produce precipitation too often with underestimated intensity leading to strong error compensationErrors in precipitation are quantified using novel metrics that prevent error compensation showing value compared with traditional metricsConvection permitting simulations outperform the representation of precipitation compared to simulations using parameterized convection [ABSTRACT FROM AUTHOR]

Open cellular convection (OCC) over, for example, the North Sea is often observed in connection with cold-air outbreaks. It is accompanied by large temporal and spatial variability in wind speed, which affects offshore wind energy in the area. This study uses the global Model for Prediction Across Scales (MPAS), with regional mesh refinement down to convection-permitting scales of 2 km, to simulate an OCC episode in the North Sea, with a focus on wind-speed variability. Modelled data are combined with wind speeds retrieved from satellite data and in situ measurements to investigate the spatial and temporal variability of offshore wind speeds under OCC conditions from a synoptic to mesoscale perspective, and to examine the model's ability to represent the OCC structures and wind-speed variability. The model can simulate realistic OCC structures and mesoscale wind-speed variability within the limits set by the effective model resolution. Under OCC conditions, significant differences from climatological conditions are found in the spatial wind-speed power spectrum and in 10-min wind-speed step changes. The very high horizontal mesh-cell spacing in the refinement region of 2 km, and the focus on OCC wind-speed variability, makes this the first investigation of this kind using the MPAS modelling framework with mesh refinement. [ABSTRACT FROM AUTHOR]

Climate projections at very high resolution (kilometre-scale grid spacing) are becoming affordable. These 'convection-permitting' models (CPMs), commonly used for weather forecasting, better represent land-surface characteristics and small-scale processes in the atmosphere such as convection. They provide a step change in our understanding of future changes at local scales and for extreme weather events. For short-duration precipitation extremes, this includes capturing local storm feedbacks, which may modify future increases. Despite the major advance CPMs offer, there are still key challenges and outstanding science issues. Heavy rainfall tends to be too intense; there are challenges in representing land-surface processes; sub-kilometre scale processes still need to be parametrized, with existing parametrization schemes often requiring development for use in CPMs; CPMs rely on the quality of lateral boundary forcing and typically do not include ocean-coupling; large CPM ensembles that comprehensively sample future uncertainties are costly. Significant progress is expected over the next few years: scale-aware schemes may improve the representation of unresolved convective updrafts; work is underway to improve the modelling of complex land-surface fluxes; CPM ensemble experiments are underway and methods to synthesize this information with larger coarser-resolution model ensembles will lead to local-scale predictions with more comprehensive uncertainty context for user application. Large-domain (continental or tropics-wide) CPM climate simulations, potentially with additional earth-system processes such as ocean and wave coupling and terrestrial hydrology, are an exciting prospect, allowing not just improved representation of local processes but also of remote teleconnections. This article is part of a discussion meeting issue 'Intensification of short-duration rainfall extremes and implications for flash flood risks'. [ABSTRACT FROM AUTHOR]

Abstract Afforestation is gaining popularity as a climate mitigation policy in many countries, including high latitude regions such as Norway. However, the impacts of afforestation on local‐to‐regional climate is poorly understood. This study uses the Weather Research and Forecasting model to investigate the biogeophysical impacts of different forestry scenarios on the local‐to‐regional climate of Norway. The forestry scenarios considered are the conversion of open spaces to either evergreen forest by active afforestation or to mixed forest by natural succession. Results show both forestry scenarios lead to additional warming of surface temperatures in winter and spring (between 1.0°C and 1.5°C) and cooling in summer (between −1.6°C and −1.3°C). A temperature decomposition analysis shows that the warming in winter and spring is driven by surface albedo changes while summer cooling is driven by changes in sensible heat fluxes for afforestation and surface albedo for natural succession. Maximum 2 m air temperature increases considerably in spring in both forestry scenarios (∼0.8°C to 1.0°C). Analysis of precipitation, multiple climate indices and extremes, reveal little or no response to the different forestry scenarios. The largest societally relevant response to the forestry scenarios is the partial mitigation of the reduction in snow days expected from global warming by 10–20 days. This suggests that implementing current afforestation policies for climate mitigation in Norway may adversely exacerbate some effects of global warming locally while mitigating others. As such, the impacts of afforestation on the local‐to‐regional climate at high latitudes are complex and cannot support or dismiss afforestation as a climate mitigation policy.

Afforestation is gaining popularity as a climate mitigation policy in many countries, including high latitude regions such as Norway. However, the impacts of afforestation on local‐to‐regional climate is poorly understood. This study uses the Weather Research and Forecasting model to investigate the biogeophysical impacts of different forestry scenarios on the local‐to‐regional climate of Norway. The forestry scenarios considered are the conversion of open spaces to either evergreen forest by active afforestation or to mixed forest by natural succession. Results show both forestry scenarios lead to additional warming of surface temperatures in winter and spring (between 1.0°C and 1.5°C) and cooling in summer (between −1.6°C and −1.3°C). A temperature decomposition analysis shows that the warming in winter and spring is driven by surface albedo changes while summer cooling is driven by changes in sensible heat fluxes for afforestation and surface albedo for natural succession. Maximum 2 m air temperature increases considerably in spring in both forestry scenarios (∼0.8°C to 1.0°C). Analysis of precipitation, multiple climate indices and extremes, reveal little or no response to the different forestry scenarios. The largest societally relevant response to the forestry scenarios is the partial mitigation of the reduction in snow days expected from global warming by 10–20 days. This suggests that implementing current afforestation policies for climate mitigation in Norway may adversely exacerbate some effects of global warming locally while mitigating others. As such, the impacts of afforestation on the local‐to‐regional climate at high latitudes are complex and cannot support or dismiss afforestation as a climate mitigation policy. Plain Language Summary: The conversion of existing open spaces such as grasslands and shrublands to forests is gaining popularity as a climate mitigation policy in many countries, including high latitude regions such as Norway. However, the impacts of such afforestation policies on the local‐to‐regional climate is poorly understood, particularly at high latitudes. This study uses a state‐of‐the‐art regional climate model to show that at high latitudes afforestation warms surface (or skin) temperature in winter and spring (between 1.0°C and 1.5°C) and cooling in summer (between −1.6°C and −1.3°C). This study also shows that afforestation considerably increases the maximum 2 m air temperature in spring (∼0.8°C to 1.0°C) but has no impact on precipitation, multiple climate indices, and extremes. The largest societally relevant response to the forestry scenarios is the partial mitigation of the reduction in snow days expected from global warming by 10–20 days. This suggests that implementing current afforestation policies for climate mitigation in Norway may adversely exacerbate some effects of global warming locally while mitigating others. As such, the impacts of afforestation on the local‐to‐regional climate at high latitudes are complex and cannot support or dismiss afforestation as a climate mitigation policy. Key Points: Impacts of land use and land cover changes on temperature and snow are localized and most pronounced in springTemperature response to land use and land cover changes is largely independent of forestry scenario in these experimentsAfforestation enhances surface warming under RCP8.5 but mitigates other impacts [ABSTRACT FROM AUTHOR]

Understanding future river flood risk is a prerequisite for developing climate change adaptation strategies and enhancing disaster resilience. Previous flood risk assessments can barely take into account future changes of fine‐scale hydroclimatic characteristics and hardly quantify multivariate interactions among flood variables, thereby resulting in an unreliable assessment of flood risk. In this study, for the first time, we develop probabilistic projections of multidimensional river flood risks at a convection‐permitting scale through the Weather Research and Forecasting (WRF) climate simulations with 4‐km horizontal grid spacing. Vine copula has been widely used to assess the multidimensional dependence structure of hydroclimate variables, but the commonly used frequentist approach may fail to identify the correct vine model and to obtain the uncertainty interval. Thus, a Bayesian vine copula approach is proposed to explicitly address the multidimensional dependence of flood characteristics (i.e., flood peak, volume, and duration) and underlying uncertainties. The proposed approach enables a robust assessment of return periods of future floods for Guadalupe and Mission river basins located in South Texas of the United States. Our findings reveal that the South Texas region is projected to experience more flood events with longer duration and greater discharge volume. The flood peak, however, will not necessarily increase even though precipitation extremes are expected to become more frequent. The projected flood return periods over the Guadalupe river basin do not show an obvious increase while the Mission river basin is projected to face a dramatic increase in flood risk with exposed to 100‐year and even severer floods nearly every 2 years, on average, when considering the combined effects of flood peak, volume, and duration. Key Points: Probabilistic projections of multidimensional flood risks were developed at a convection‐permitting scaleHigh‐resolution climate projections with the 4‐km horizontal grid spacing were developed using the convection‐permitting Weather Research and Forecasting modelBayesian vine copula approach was proposed to uncover potential interactions among flood variables and associated uncertainties [ABSTRACT FROM AUTHOR]

High latitude regions are undergoing substantial land use and land cover change (LULCC) arising partly from a changing climate (e.g. greening of the Arctic) and climate mitigation policies (e.g. afforestation). Despite these ongoing changes, the impacts of LULCC in high latitudes are poorly understood. Studies to reduce this knowledge deficit primarily deploy regional climate models (RCMs) as observations of key variables for LULCC studies are scarce in high latitude regions. As such it is important to understand the limitations of RCMs and identify best practices for designing regional climate modelling experiments for LULCC studies at high latitudes. In this study, twelve 10-year simulations are performed over the Scandinavian Peninsula; six at convection permitting scales (dx ∼ 3 km) and six at non-convection permitting scales (dx ∼ 15 km). Two of the convection permitting simulations model the present and future climate conditions over the Scandinavian Peninsula. The present-day simulation is comprehensively evaluated for multiple variables (e.g. surface air temperature at 2 m, precipitation) using multiple sources of observations (stations, reanalysis & blended satellite products) where available. Results from the model evaluation points to the need for further model improvement in simulating precipitation and related snow processes as well as the need for observations of surface energy fluxes at high latitudes for evaluation. The remaining eight simulations differ in terms of grid spacing, background climate state (present or future climate), and land cover (conversion of grasslands to evergreen needleleaf or mixed forest). Our study highlights the strengths and limitations of common RCM design considerations, such as simulation length (single year vs. multi-years), background climate state (present vs. future climate) and model resolution (convection permitting vs. non-convection permitting). A key recommendation is that high-latitude modeling studies of LULCC should prioritize computational resources for multi-decadal and ensemble simulations over short, single experiment simulations at convection permitting scales. [ABSTRACT FROM AUTHOR]

The reliable projection of future changes in hydrological drought characteristics plays a crucial role in providing meaningful insights into agricultural development and water resources planning under climate change. In this study, we develop probabilistic projections of hydrological drought characteristics through a convection‐permitting climate simulation and a multimodel hydrological prediction for two major river basins in South Texas of the United States. The probabilistic hydrological drought projection depicts the future evolution of spatiotemporal characteristics of droughts under best‐ and worst‐case scenarios. Our findings reveal that there is a considerable variation in hydrological drought regimes near the urban area in South Texas. And the prolonged severe drought events are expected to be punctuated by the increasing extreme precipitation in a changing climate. This could lead to an increasing number of the dry‐wet abrupt alternation events. Moreover, hydrological droughts are projected to occur more frequently for the fall season in the Guadalupe River Basin and for the winter season in the Blanco River Basin, advancing our understanding of future changes in seasonal characteristics of hydrological droughts at a river basin scale. Key Points: Probabilistic projections of hydrological drought characteristics are developed at a river basin scaleHydrological droughts are expected to occur more frequently by the end of the 21st centuryThe number of dry‐wet abrupt alternation event is projected to increase in a changing climate [ABSTRACT FROM AUTHOR]

This research work focuses on the problem of climate simulation of rainfall over West Africa and particularly over coastal countries of the Gulf of Guinea by Regional Climate Models. The sensitivities of Weather Research and Forecasting (WRF) Model were tested for changes in horizontal resolution (convection permitting versus parameterized) on the replication of West African monsoon for the year 2014. Sensitivity test was also performed for response of rainfall to changes in microphysics (MP) and planetary boundary layer (PBL) schemes. Generally, the result shows that WRF is able to replicates rainfall distribution with an adequate representation of the dynamical features of West African monsoon system. The high-resolution (wrf-4km) shows dry bias along the coast of the Gulf of Guinea but generally outperforms wrf-24km run especially in replication of the extreme rainfall distribution. The dry bias along the coastal area is suggested to not to be only related to convection but mostly to microphysics and PBL parameterisation schemes. Differences were noticed between the dynamics of WRF and ERA-interim outputs despite the use of spectral nudging in the experiment which then suggest strong interactions between scales. These differences were observed to be restricted mainly to the low-layer monsoon flow in JJA. Both runs at 24 and 4 km hardly simulate the typical diurnal distribution of rainfall. The sensitivity of WRF to MP (only sophisticated MP were tested) and PBL reveals a stronger impact of PBL than MP on rainfall distribution and the most significant added value over the Guinean coast and surroundings area was provided by the configurations using non-local PBL scheme (as ACM2). The changes in MP and PBL schemes in general seem to have less effect on the explicit runs (wrf-4km) in the replication of the rainfall over the Gulf of Guinea and the surroundings seaboard. [ABSTRACT FROM AUTHOR]

Exploring the dynamic evolution of the abrupt alternation between wet and dry spells in adjacent months plays a crucial role in water resources planning and agricultural development in a changing climate. The dry‐wet abrupt alternation (DWAA) has been studied based on hydrometeorological observations over the past several years. However, little effort has been made to explore DWAA from a climate projection standpoint. Furthermore, few studies have investigated potential interrelationships between DWAA and heavy rainfall. In this study, the interrelationships between DWAA events and heavy rainfall with various intensities as well as potential evapotranspiration are revealed explicitly through the convection‐permitting climate simulations for 10 climate divisions over Texas in the United States. Our findings disclose that the increasing heavy rainfall and potential evapotranspiration lead to more frequent occurrence of DWAA events over a larger spatial extent. Heavy rainfall with daily precipitation greater than 20 mm contributes most to the occurrence of DWAA. In addition, a severe phenomenon of dry‐wet‐dry alternation is projected to appear due to the increasing number of heavy rainfall and drought events as well as the deteriorated soil water holding capacity under global warming. Plain Language Summary: The dry‐wet abrupt alternation (DWAA) has dramatic negative impacts on water security and agricultural production. DWAA is a devastating natural disaster which is characterized by an abrupt alternation between wet and dry spells in adjacent months. Exploring the underlying mechanism and complex evolution of DWAA is thus crucial to helping policymakers and stakeholders develop sound adaptation and mitigation plans for reducing potential risks of natural extreme events. We find that the DWAA event is projected to occur simultaneously in multiple climate divisions, especially for the most populous region of South Texas. Heavy rainfall with daily precipitation greater than 20 mm contributes most to the occurrence of DWAA. Due to the increasing number of drought and heavy rainfall events, a severe super extreme event of the dry‐wet‐dry alternation is projected to appear by the end of this century, whereas there is no historical record on the occurrence of such an extreme event over Texas in the period of 1981–1995. Key Points: Heavy rainfall with daily precipitation greater than 20 mm contributes most to the occurrence of dry‐wet abrupt alternation eventsAbrupt alternation between wet and dry spells is projected to occur more frequently over a larger spatial extent by the end of the 21st centuryAbrupt alternation (dry‐wet‐dry) is expected to occur due to the increasing number of heavy rainfall and drought events in a changing climate [ABSTRACT FROM AUTHOR]

Understanding future changes in hydroclimatic variables plays a crucial role in improving resilience and adaptation to extreme weather events such as floods and droughts. In this study, we develop high-resolution climate projections over Texas by using the convection-permitting Weather Research and Forecasting (WRF) model with 4 km horizontal grid spacing, and then produce the Markov chain Monte Carlo (MCMC)-based hydrologic forecasts in the Guadalupe River basin which is the primary concern of the Texas Water Development Board and the Guadalupe-Blanco River Authority. The Parameter-elevation Regressions on Independent Slopes Model (PRISM) dataset is used to verify the WRF climate simulations. The Model Parameter Estimation Experiment (MOPEX) dataset is used to validate probabilistic hydrologic predictions. Projected changes in precipitation, potential evapotranspiration (PET) and streamflow at different temporal scales are examined by dynamically downscaling climate projections derived from 15 Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs). Our findings reveal that the Upper Coast Climate Division of Texas is projected to experience the most remarkable wetting caused by precipitation and PET changes, whereas the most significant drying is expected to occur for the North Central Texas Climate Division. The dry Guadalupe River basin is projected to become drier with a substantial increase in future drought risks, especially for the summer season. And the extreme precipitation events are projected to increase in frequency and intensity with a reduction in overall precipitation frequency, which may result in more frequent occurrences of flash floods and drought episodes in the Guadalupe River basin. [ABSTRACT FROM AUTHOR]

Probabilistic projections of future drought characteristics play a crucial role in climate change adaptation and disaster risk reduction. This study presents a copula‐based probabilistic framework for projecting future changes in multivariable drought characteristics through convection‐permitting Weather Research and Forecasting simulations with 4‐km horizontal grid spacing. A probabilistic multivariate drought index is introduced to examine the joint effects of drought indicators with uncertainty intervals for four major river basins located in South Central Texas of the United States. Markov chain Monte Carlo is used to address uncertainties in assessing copula parameters and in predicting climate‐induced changes in hydrological regimes. Our findings reveal that the severity and intensity of drought episodes can be amplified when considering the compound effects of soil moisture and runoff regimes by using the probabilistic multivariate drought index. The South Central Texas region is projected to experience more drought events with shorter duration and higher intensity in a changing climate. The drought severity will not necessarily increase due to the decreasing drought duration. In addition, our findings indicate that the intensity of future droughts is expected to increase as a result of the deficiency of soil moisture even though precipitation extremes are projected to become more frequent. Moreover, climate change impacts on multivariate drought characteristics will intensify with the increasing temporal scales (i.e., short‐, medium‐, and long‐term droughts) although the number of future drought events may decrease by the end of this century. Plain Language Summary: Projecting future changes in drought characteristics plays a crucial role in climate change adaptation and disaster risk reduction. Here we present a copula‐based probabilistic framework for conducting high‐resolution projections of multivariable drought characteristics through convection‐permitting Weather Research and Forecasting simulations with the 4‐km horizontal grid spacing. A probabilistic multivariate drought index is also introduced to examine the joint effects of drought indicators for four major river basins located in South Central Texas of the United States. Our findings reveal that the severity and intensity of drought episodes can be amplified when considering the compound effects of soil moisture and runoff regimes. Furthermore, the long‐lasting droughts are expected to evolve into a series of short‐duration droughts due to the projected increase in the frequency and intensity of extreme precipitation in a changing climate. This study provides meaningful insights into the evolution of future drought characteristics in view of the increasing trend in extreme precipitation, which plays an important role in facilitating sustainable agricultural development and water resources planning under climate change. Key Points: A copula‐based probabilistic framework was proposed for conducting high‐resolution projections of multivariate drought characteristicsHigh‐resolution climate projections were developed using the convection‐permitting WRF model with the 4‐km horizontal grid spacingA probabilistic multivariate drought index was introduced to examine joint effects of soil moisture and runoff deficits [ABSTRACT FROM AUTHOR]

We show that there is a strong sensitivity of cloud microphysics to model time step in idealized convection‐permitting simulations using the COnsortium for Small‐scale MOdeling model. Specifically, we found a 53% reduction in precipitation when the time step is increased from 1 to 15 s, changes to the location of precipitation and hail reaching the surface, and changes to the vertical distribution of hydrometeors. The effect of cloud condensation nuclei perturbations on precipitation also changes both magnitude and sign with the changing model time step. The sensitivity arises because of the numerical implementation of processes in the model, specifically the so‐called "splitting" of the dynamics (e.g., advection and diffusion) and the parameterized physics (e.g., microphysics scheme). Calculating one step at a time (sequential‐update splitting) gives a significant time step dependence because large supersaturation with respect to liquid is generated in updraft regions, which strongly affect parameterized microphysical process rates—in particular, ice nucleation. In comparison, calculating both dynamics and microphysics using the same inputs of temperature and water vapor (hybrid parallel splitting) or adding an additional saturation adjustment within the dynamics reduces the time step sensitivity of surface precipitation by limiting the supersaturation seen by the microphysics, although sensitivity to time step remains for some processes. Key Points: Changing model time step affects amount and location of surface precipitation and vertical distribution of hydrometeorsTime step choice affects many processes and potentially masks aerosol‐cloud interaction impactsReplacing sequential‐update splitting by a hybrid parallel splitting reduces the time step sensitivity [ABSTRACT FROM AUTHOR]

The impact of assimilating radar radial velocities and reflectivities from the French and German radar network on nowcasting to short-range quantitative precipitation forecasting (SRQPF) was investigated during COPS IOP10. For this purpose, the Weather Research and Forecasting (WRF) model was applied in a convection permitting configuration and a resolution of 3.6 km covering Central Europe. Four different experiments were carried out to study the influence of assimilating 3-dimensional radar data by employing the first developed convection permitting WRF rapid update cycle (RUC) with a 3-h cycle interval over Central Europe. For the control experiment, only conventional observations were selected for assimilation. For the other experiments, radar radial velocities and reflectivities were added. Additionally, the reflectivity operator was slightly modified to study the influence of a smaller lower boundary for the rain water mixing ratio. The results show a positive impact on SRQPF over Eastern France when applying radar radial velocities in addition to conventional observations and Global Positioning System Zenith Total Delay (GPS-ZTD). Radial velocities reduce the overestimation of 3-h precipitation compared to the control experiment without radar data. The bias and RMSE show an improvement of about 10 % respectively. When applying also radar reflectivities in addition, the 3-h precipitation bias is reduced by about 50 % compared to the assimilation of radial velocities only. The frequency bias for different precipitation thresholds now shows values

Inadequate representation and parameterization of sub-grid scale features and processes are one of the main sources for uncertainties in regional climate change projections, particularly for the Arctic regions where the climate change signal is amplified. Increasing model resolution to a couple of kilometers will be helpful in resolving some of these challenges, for example to better simulate convection and refined land heterogeneity and thus land−atmosphere interactions. A set of multi-year simulations has been carried out for the Canadian Arctic domain at 12 km and 3 km resolutions using limited-area version of the global environmental multi-scale (GEM) model. The model is integrated for five years driven by the fifth generation of the European Centre for medium-range weather forecast reanalysis (ERA-5) at the lateral boundaries. The aim of this study is to investigate the role of horizontal model resolution on the simulated surface climate variables. Results indicate that although some aspects of the seasonal mean values are deteriorated at times, substantial improvements are noted in the higher resolution simulation. The representation of extreme precipitation events during summer and the simulation of winter temperature are better captured in the convection-permitting simulation. Moreover, the observed temperature−extreme precipitation scaling is realistically reproduced by the higher resolution simulation. These results advocate for the use of convective-permitting resolution models for simulating future climate projections over the Arctic to support climate impact assessment studies such as those related to engineering applications and where high spatial and temporal resolution are beneficial.

To improve the landfalling tropical cyclone (TC) forecasting, the pseudo inner-core observations derived from the optimal-member forecast (OPT) and its probability-matched mean (OPTPM) of a mesoscale ensemble prediction system, namely TREPS, were assimilated in a partial-cycle data assimilation (DA) system based on the three-dimensional variational method. The impact of assimilating the derived data on the 12-h TC forecasting was evaluated over 17 TCs making landfall on Southern China during 2014⁻2016, based on the convection-permitting Global/Regional Assimilation and Prediction System (GRAPES) model with the horizontal resolution of 0.03°. The positive impacts of assimilating the OPT-derived data were found in predicting some variables, such as the TC intensity, lighter rainfall, and stronger surface wind, with statistically significant impacts at partial lead times. Compared with assimilation of the OPT-derived data, assimilation of the OPTPM-derived data generally brought improvements in the forecasts of TC track, intensity, lighter rainfall, and weaker surface wind. When the data with higher accuracy was assimilated, the positive impacts of assimilating the OPTPM-derived data on the forecasts of heavier rainfall and stronger surface wind were more evident. The improved representation of initial TC circulation due to assimilating the derived data improved the TC forecasting, which was intuitively illustrated in the case study of Mujigae.

Inadequate representation and parameterization of sub-grid scale features and processes are one of the main sources for uncertainties in regional climate change projections, particularly for the Arctic regions where the climate change signal is amplified. Increasing model resolution to a couple of kilometers will be helpful in resolving some of these challenges, for example to better simulate convection and refined land heterogeneity and thus land–atmosphere interactions. A set of multi-year simulations has been carried out for the Canadian Arctic domain at 12 km and 3 km resolutions using limited-area version of the global environmental multi-scale (GEM) model. The model is integrated for five years driven by the fifth generation of the European Centre for medium-range weather forecast reanalysis (ERA-5) at the lateral boundaries. The aim of this study is to investigate the role of horizontal model resolution on the simulated surface climate variables. Results indicate that although some aspects of the seasonal mean values are deteriorated at times, substantial improvements are noted in the higher resolution simulation. The representation of extreme precipitation events during summer and the simulation of winter temperature are better captured in the convection-permitting simulation. Moreover, the observed temperature–extreme precipitation scaling is realistically reproduced by the higher resolution simulation. These results advocate for the use of convective-permitting resolution models for simulating future climate projections over the Arctic to support climate impact assessment studies such as those related to engineering applications and where high spatial and temporal resolution are beneficial. [ABSTRACT FROM AUTHOR]

To improve the landfalling tropical cyclone (TC) forecasting, the pseudo inner-core observations derived from the optimal-member forecast (OPT) and its probability-matched mean (OPTPM) of a mesoscale ensemble prediction system, namely TREPS, were assimilated in a partial-cycle data assimilation (DA) system based on the three-dimensional variational method. The impact of assimilating the derived data on the 12-h TC forecasting was evaluated over 17 TCs making landfall on Southern China during 2014–2016, based on the convection-permitting Global/Regional Assimilation and Prediction System (GRAPES) model with the horizontal resolution of 0.03°. The positive impacts of assimilating the OPT-derived data were found in predicting some variables, such as the TC intensity, lighter rainfall, and stronger surface wind, with statistically significant impacts at partial lead times. Compared with assimilation of the OPT-derived data, assimilation of the OPTPM-derived data generally brought improvements in the forecasts of TC track, intensity, lighter rainfall, and weaker surface wind. When the data with higher accuracy was assimilated, the positive impacts of assimilating the OPTPM-derived data on the forecasts of heavier rainfall and stronger surface wind were more evident. The improved representation of initial TC circulation due to assimilating the derived data improved the TC forecasting, which was intuitively illustrated in the case study of Mujigae. [ABSTRACT FROM AUTHOR]