Convection-permitting Climate Model Research Articles

Climatology of severe hail potential in Europe based on a convection-permitting simulation

AbstractWe present a new approach to identify severe hailstorms in a convection-permitting climate model, and build a climatology of severe hail potential in Europe using an ingredients-based approach based on a 20-year long hindcast simulation. Severe hail in Europe occurs mostly in southern regions (up to 40 times a year per 10,000 km2 around Northern Italy), and from May to August. It peaks from afternoon to evening hours on land, whilst sea areas are prone to hail at any time of the day. The Mediterranean Sea experiences severe hailstorms mostly in autumn: the central Mediterranean has the highest frequency among all regions studied, and may be considered as an unknown alley for hailstorms in Europe. Results derived from the high-resolution model are in very good agreement with existing hail climatologies constructed from observations, including the fine scale spatial variation. We conclude that our approach provides a reliable proxy for studying future changes in severe hail in convection-permitting simulations.

Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0

Abstract. The spatial heterogeneity related to complex topography in California demands high-resolution (< 5 km) modeling, but global convection-permitting climate models are computationally too expensive to run multi-decadal simulations. We developed a 3.25 km California climate modeling framework by leveraging regional mesh refinement (CARRM) using the U.S. Department of Energy (DOE)'s global Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) version 0. Four 5-year time periods (2015–2020, 2029–2034, 2044–2049, and 2094–2099) were simulated by nudging CARRM outside California to 1° coupled simulation of E3SMv1 under the Shared Socioeconomic Pathways (SSP)5-8.5 future scenario. The 3.25 km grid spacing adds considerable value to the prediction of the California climate changes, including more realistic high temperatures in the Central Valley and much improved spatial distributions of precipitation and snowpack in the Sierra Nevada and coastal stratocumulus. Under the SSP5-8.5 scenario, CARRM simulation predicts widespread warming of 6–10 °C over most of California, a 38 % increase in statewide average 30 d winter–spring precipitation, a near-complete loss of the alpine snowpack, and a sharp reduction in shortwave cloud radiative forcing associated with marine stratocumulus by the end of the 21st century. We note a climatological wet precipitation bias for the CARRM and discuss possible reasons. We conclude that SCREAM RRM is a technically feasible and scientifically valid tool for climate simulations in regions of interest, providing an excellent bridge to global convection-permitting simulations.

Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research?

Abstract. Snow dynamics play a critical role in the climate system, as they affect the water cycle, ecosystems, and society. In climate modelling, the representation of the amount and extent of snow on the land surface is crucial for simulating the mass and energy balance of the climate system. Here, we evaluate simulations of daily snow depths against 83 station observations in southern Germany in an elevation range of 150 to 1000 m over the time period 1987–2018. Two simulations stem from high-resolution regional climate models – the Weather Research & Forecasting (WRF) model at 1.5 km resolution and the COnsortium for Small scale MOdelling model in CLimate Mode (COSMO-CLM; abbreviated to CCLM hereafter) at 3 km resolution. Additionally, the hydrometeorological snow model Alpine MUltiscale Numerical Distributed Simulation ENgine (AMUNDSEN) is run at point scale at the locations of the climate stations, based on the atmospheric output of CCLM. To complement the comparison, the ERA5-Land dataset (9 km), a state-of-the-art reanalysis land-surface product, is also compared. All four simulations are driven by the atmospheric boundary conditions of ERA5. Due to an overestimation of the snow albedo, the WRF simulation features a cold bias of 1.2 °C, leading to the slight overestimation of the snow depth in low-lying areas, whereas the snow depth is underestimated at snow-rich stations. The number of snow days (days with a snow depth above 1 cm) is reproduced well. The WRF simulation can recreate extreme snow depths, i.e. annual maxima of the snow depth, their timings, and inter-station differences, and thereby shows the best performance of all models. The CCLM reproduces the climatic conditions with very low bias and error metrics. However, all snow-related assessments show a strong systematic underestimation, which we relate to deficiencies in the snow module of the land-surface model. When driving AMUNDSEN with the atmospheric output of the CCLM, the results show a slight tendency to overestimate snow depth and number of snow days, especially in the northern parts of the study area. Snow depth extremes are reproduced well. For ERA5-Land (ERA5L), the coarser spatial resolution leads to larger differences between the model elevation and the station elevation, which contributes to a significant correlation of climatic biases with the elevation bias. In addition, the mean snow depth and number of snow days are strongly overestimated, with conditions that are too snowy in the late winter. Extreme snow depth conditions are reproduced well in the low-lying areas, whereas strong deviations occur with more complex topography. In sum, due to the high spatial resolution of convection-permitting climate models, they show the potential to reproduce the winter climate (temperature and precipitation) in southern Germany. However, different sources of uncertainties, i.e. the spatial resolution, the snow albedo parametrisation, and other parametrisations within the snow model, prevent their further use in a straightforward manner for impact research. Hence, careful evaluation is needed before any impact-related interpretation of the simulations, such as in the context of climate change research.

Convection-permitting climate models offer more certain extreme rainfall projections

Extreme precipitation events lead to dramatic impacts on society and the situation will worsen under climate change. Decision-makers need reliable estimates of future changes as a basis for effective adaptation strategies, but projections at local scale from regional climate models (RCMs) are highly uncertain. Here we exploit the km-scale convection-permitting multi-model (CPM) ensemble, generated within the FPS Convection project, to provide new understanding of the changes in local precipitation extremes and related uncertainties over the greater Alpine region. The CPM ensemble shows a stronger increase in the fractional contribution from extreme events than the driving RCM ensemble during the summer, when convection dominates. We find that the CPM ensemble substantially reduces the model uncertainties and their contribution to the total uncertainties by more than 50%. We conclude that the more realistic representation of local dynamical processes in the CPMs provides more reliable local estimates of change, which are essential for policymakers to plan adaptation measures.

Predicting extreme sub-hourly precipitation intensification based on temperature shifts

Abstract. Extreme sub-hourly precipitation, typically convective in nature, is capable of triggering natural disasters such as floods and debris flows. A key component of climate change adaptation and resilience is quantifying the likelihood that sub-hourly extreme precipitation will exceed historical levels in future climate scenarios. Despite this, current approaches to estimating future sub-hourly extreme precipitation return levels are deemed insufficient. The reason for this can be attributed to two factors: there is limited availability of data from convection-permitting climate models (capable of simulating sub-hourly precipitation adequately) and the statistical methods we use to extrapolate extreme precipitation return levels do not capture the physics governing global warming. We present a novel physical-based statistical method for estimating the extreme sub-hourly precipitation return levels. The proposed model, named TEmperature-dependent Non-Asymptotic statistical model for eXtreme return levels (TENAX), is based on a parsimonious non-stationary and non-asymptotic theoretical framework that incorporates temperature as a covariate in a physically consistent manner. We first explain the theory and present the TENAX model. Using data from several stations in Switzerland as a case study, we demonstrate the model's ability to reproduce sub-hourly precipitation return levels and some observed properties of extreme precipitation. We then illustrate how the model can be utilized to project changes in extreme sub-hourly precipitation in a future warmer climate only based on climate model projections of temperatures during wet days and on foreseen changes in precipitation frequency. We conclude by discussing the uncertainties associated with the model, its limitations, and its advantages. With the TENAX model, one can project sub-hourly precipitation extremes at different return levels based on daily scale projections from climate models in any location globally where observations of sub-hourly precipitation data and near-surface air temperature are available.

Correction: Aerosol sensitivity simulations over East Asia in a convection-permitting climate model

Correction: Aerosol sensitivity simulations over East Asia in a convection-permitting climate model

Using sub-daily precipitation for grid-based hydrological modelling across Great Britain: Assessing model performance and comparing flood impacts under climate change

Study regionGreat Britain. Study focusNational-scale grid-based hydrological models are usually run at fine spatial and temporal resolutions, but driving data are often not available at the required resolutions. Here, a recent observation-based hourly 1 km gridded precipitation dataset is applied with a 1 km hydrological model to simulate daily mean river flows. Performance is compared to use of equally-disaggregated and profile-disaggregated daily data, for a large number of catchments. Hourly and daily precipitation from a high-resolution convection-permitting climate model (CPM) are then used to drive the hydrological model for baseline (1980–2000) and future (2060–2080) periods, to investigate differences in potential peak flow changes. New hydrological insightsOn average, use of observation-based hourly data provides a clear improvement over equally-disaggregated daily data for high flows and peak flow bias, a small improvement for average flows and mean flow bias, but little difference for low flows. Performance in faster-responding catchments typically improves more; performance in some catchments degrades. Use of profile-disaggregated daily data provides the small mean flow bias improvement and some peak flow bias improvement, but other factors degrade. On average, future changes in peak flows from hourly CPM precipitation are only slightly larger than from equally-disaggregated daily data. Future work will look at simulation of hourly mean flows.

Potential intensification of hourly precipitation extremes in Western Canada: A comprehensive understanding of precipitation-temperature scaling

The convection-permitting climate model (CPCM), WRF-ARW at 4 km resolution, is able to capture the observed relationships between precipitation extremes and temperature (PT scaling) in western Canada. By analyzing the CPCM simulated PT scalings, we found they have robust patterns at different percentiles of precipitation intensity and even between the current and future climate. This is due to the stable annual cycle of the regional climate. The PT scaling pattern is physically governed by the amount of water vapour and the ascending velocity of air. Approximately 95% of the precipitation intensity variation can be explained by the vertical velocity and precipitable water in western Canada. The PT scaling for the current climate does not tell how precipitation extremes would response to a warmer climate. Trend scaling theory was utilized to estimate the intensification of precipitation extremes in a warmer climate. It shows that, in western Canada, the coast is particularly vulnerable to precipitation extremes under global warming. Precipitation extremes are projected to increase at a super Clausius-Clapeyron (CC) scale over the coast, approximately at a CC scale over the prairies and mountains, and a sub-CC scale over the northern region. The warming effect on precipitation extremes is even stronger when the concept of”wet-day trend scaling” is introduced.

Climatological features of future MCSs over the Canadian Prairies using convection-permitting climate models

This study explores the potential effects of global warming on Mesoscale Convective Systems (MCSs) initiation and duration in the Canadian Prairies. By utilizing high-resolution convection-permitting climate models (CPCMs), the study examines 15 parameters to identify the most significant factor for predicting future changes, using the Pseudo Global Warming (PGW) approach. The results suggest that the frequency of MCSs will significantly increase in the future, with varying initiation and duration patterns between daytime and nighttime. The study highlights Preciptable Water (PW), Isentropic Potential Vorticity (IPV), and Most Unstable Convective Potential Energy (MUCAPE) as the most significant parameters for long-lived nighttime MCSs, while Relative Humidity at 850 hPa (RH850), PW, and Storm Relative Helicity 0-1 km (SRH1km) are identified as the most significant parameters for long-lived daytime MCSs. The findings are supported by violin plots that show the differences in the distribution of the 15 parameters in the initiation regions of short- and long-lived MCSs. Additionally, this study partially confirms previous research findings, such as the Mountain-plains solenoids, which suggest that increased PW and RH850 in the future are in agreement with increased total precipitation in the northern region. The study emphasizes the importance of utilizing newly released forcing datasets to investigate the potential impact of global warming on MCSs initiation and duration in the Canadian Prairies.

Assessing the co-benefits of urban greening coupled with rainwater harvesting management under current and future climates across USA cities

Globally, urban areas face multiple challenges owing to climate change. Urban greening (UG) is an excellent option for mitigating flood risk and excess urban heat. Rainwater harvesting (RWH) systems can cope with plant irrigation needs and urban water management. In this study, we investigated how UG and RWH work together to mitigate environmental risks. By incorporating a new RWH module into the urban ecohydrological model Urban Tethys-Chloris (UT&C), we tested different uses of intervention approaches for 28 cities in the USA, spanning a variety of climates, population densities, and urban landscapes. UT&C was forced by the latest generation convection-permitting climate model simulations of the current (2001–2011) and end-of-century (RCP8.5) climate. Our results showed that neither UG nor RWH, through the irrigation of vegetation, could significantly contribute to mitigating the expected strong increase in 2 m urban canyon temperatures under a high-emission scenario. RWH alone can sufficiently offset the intensifying surface flood risk, effectively enhance water saving, and support UG to sustain a strong urban carbon sink, especially in dry regions. However, in these regions, RWH cannot fully fulfill plant water needs, and additional measures to meet irrigation demand are required to maximize carbon sequestration by urban vegetation.

On the application of rainfall projections from a convection-permitting climate model to lumped catchment models

Climate change is predicted to increase rainfall intensity in tropical regions. Convection permitting (CP) climate models have been developed to address deficiencies in conventional climate models that use parameterised convection. However, to date, precipitation projections from CP climate models have not been used in conjunction with hydrological models to explore potential impacts of explicit modelling of convective rainfall on river flows in the tropics. Here we apply the outputs of a continental scale CP climate model as inputs to lumped rainfall-runoff models in Africa for the first time. Applied to five catchments in the Lake Victoria Basin, we show that the CP climate model produces greater river flows than an equivalent model using parameterised convection in both the current and future (c. 2100) climate. However, the location of the catchments near to Lake Victoria results in limited changes in extreme rainfall and river flows relative to changes in mean rainfall and river flows. Application of CP model rainfall data from an area where rainfall extremes change more than the change in mean rainfall to the rainfall-runoff model does not result in significant changes in river flows. Instead, this is shown to be a result of the rainfall-runoff model structure and parameterisation, which we posit is due to large-scale storage in the catchments associated with wetland cover, that buffers the impact of rainfall extremes. Based on an assessment of hydrological attributes (wetland coverage, water table depth, topography, precipitation, evapotranspiration and river flow) using global-scale datasets for the catchments in this research, this buffering may be extensive across humid regions. Application of CP climate model data to lumped catchment models in these areas are unlikely to result in significant increases in extreme river flows relative to increases in mean flows.

Climate Change Impacts on Extreme Rainfall in Eastern Africa in a Convection-Permitting Climate Model

Abstract Climate change is expected to increase the frequency and intensity of rainfall extremes. Understanding future changes in rainfall is necessary for adaptation planning. Eastern Africa is vulnerable to rainfall extremes because of low adaptive capacity and high future population growth. Convection-permitting climate models have been found to better represent moderate (yearly) rainfall extremes than parameterized convection models, but there is limited analysis of rare extremes that occur less frequently than once per year. These events often have the largest socioeconomic impacts. We use extreme value theory and regional frequency analysis to quantify rare rainfall extremes over East Africa in a convection-permitting climate model (CP4A). We compare the results with its parameterized counterpart (P25), the Coordinated Regional Climate Downscaling Experiment for the African region (CORDEX-Africa) ensemble, and observations to understand how the convection parameterization impacts the results. We find that CP4A better matches observations than the parameterized models. With climate change, we find the parameterized convection models have unrealistically high changes in the shape parameter of the extreme value distribution, which controls the tail behavior (i.e., the most extreme events), leading to large increases in return levels of events with a return period of >20 years. This suggests that parameterized convection models may not be suitable for looking at relative changes in rare rainfall events with climate change and that convection-permitting models should be preferred for this type of work. With the more realistic CP4A, RCP8.5 end-of-century climate change leads to 1-in-100-yr events becoming 1-in-23-yr events, which will necessitate serious adaptation efforts to avoid devastating socioeconomic impacts. Significance Statement We use a new, high-resolution climate model to examine how rare extreme rainfall events in East Africa might change in the future with climate change and compare the results with those from standard-resolution climate models. We find that the standard-resolution models have unrealistically large increases in rainfall for events that occur less frequently than every 20 years. The high-resolution model is more realistic and is required to illustrate possible future changes in rare rainfall extremes. Extreme events will become more common with climate change, and in the more realistic model we show that a 1-in-100-yr event may become a 1-in-23-yr event by the end of the century if greenhouse gas emissions are not significantly reduced.

Aerosol sensitivity simulations over East Asia in a convection-permitting climate model

Aerosol sensitivity simulations over East Asia in a convection-permitting climate model

Climate change information over Fenno-Scandinavia produced with a convection-permitting climate model

This paper presents results from high-resolution climate change simulations that permit convection and resolve mesoscale orography at 3-km grid spacing over Fenno-Scandinavia using the HARMONIE-Climate (HCLIM) model. Two global climate models (GCMs) have been dynamically down-scaled for the RCP4.5 and RCP8.5 emission scenarios and for both near and far future periods in the 21st century. The warmer and moister climate conditions simulated in the GCMs lead to changes in precipitation characteristics. Higher precipitation amounts are simulated in fall, winter and spring, while in summer, precipitation increases in northern Fenno-Scandinavia and decreases in the southern parts of the domain. Both daily and sub-daily intense precipitation over Fenno-Scandinavia become more frequent at the expense of low-intensity events, with most pronounced shifts in summer. In the Scandinavian mountains, pronounced changes occur in the snow climate with a shift in precipitation falling as snow to rain, reduced snow cover and less days with a significant snow depth. HCLIM at 3-km grid spacing exhibits systematically different change responses in several aspects, e.g. a smaller shift from snow to rain in the western part of the Scandinavian mountains and a more consistent decrease in the urban heat island effect by the end of the 21st century. Most importantly, the high-resolution HCLIM shows a significantly stronger increase in summer hourly precipitation extremes compared to HCLIM at the intermediate 12-km grid spacing. In addition, an analysis of the statistical significance of precipitation changes indicates that simulated time periods of at least a couple of decades is recommended to achieve statistically robust results, a matter of important concern when running such high-resolution climate model experiments. The results presented here emphasizes the importance of using “convection-permitting” models to produce reliable climate change information over the Fenno-Scandinavian region.

Aerosol sensitivity simulations over East Asia in a convection-permitting climate model (2/2)

Aerosol sensitivity simulations over East Asia in a convection-permitting climate model (2/2)

Aerosol sensitivity simulations over East Asia in a convection-permitting climate model (1/2)

Aerosol sensitivity simulations over East Asia in a convection-permitting climate model (1/2)

Converting tropical forests to agriculture increases fire risk by fourfold

Deforestation exacerbates climate change through greenhouse gas emissions, but other climatic alterations linked to the local biophysical changes from deforestation remain poorly understood. Here, we assess the impact of tropical deforestation on fire weather risk—defined as the climate conditions conducive to wildfires—using high-resolution convection-permitting climate simulations. We consider two land cover scenarios for the island of Borneo: land cover in 1980 (forest scenario) and land cover in 2050 (deforestation scenario) to force a convection-permitting climate model, using boundary conditions from ERA-Interim reanalysis for the 2002–2016 period. Our findings revealed significant alterations in post-deforestation fire precursors such as increased temperature, wind speed and potential evapotranspiration and decreased humidity, cloud cover and precipitation. As a result, fire weather events that would occur once a year in the forested scenario, are likely to occur four times a year following deforestation. Likewise, for extreme conditions, such as those occurring on longer time-horizons than 20 years, the magnitude of extreme fire weather is likely to double following deforestation. These increases in extreme fire weather conditions demonstrate the key role of tropical forests in regulating regional climate processes, including reduced fire weather risk.

Features of MCSs in the Central United States Using Simulations of ERA5-Forced Convection-Permitting Climate Models

Abstract In this work, we characterized the occurrences and conditions before the initiations of mesoscale convective systems (MCSs) in the central United States, using 15 years of observations and convection-permitting climate model simulations. The variabilities of MCSs in summer were obtained using high-resolution (4 km) observation data [Stage-IV (stIV)] and ECMWF Re-Analysis v5 (ERA5)-forced Weather Research and Forecasting (WRF) Model simulations (E5RUN). MCSs were identified using the object tracking algorithm MODE-time domain (MTD). MTD-determined MCSs were divided into daytime short-lived MCSs (SLM12), daytime long-lived MCSs (LLM12), nighttime short-lived MCSs (SLM00), and nighttime long-lived MCSs (LLM00). E5RUN showed skill to simulate MCSs by obtaining similar statistics in occurrences, areal coverages, and propagation speeds compared to those of stIV. We calculated the 15 parameters using sounding data from E5RUN before an MCS was initiated (−1, −3, −6, and −9 h) at each location of an MCS. The parameters were tested to figure out the significance of predicting the longevities of MCSs. The key findings are 1) LLM12 showed favorable thermodynamic variables compared to that of SLM12 and 2) LLM00 showed significant conditions of vertically rotating winds and sheared environments that affect the longevity of MCSs. Moreover, storm-relative helicity of 0–3 km, precipitable water, and vertical wind shear of 0–6 km are the most significant parameters to determine the longevities of MCSs (both daytime and nighttime MCSs). Significance Statement The purpose of this study is to understand the features of mesoscale convective systems (MCSs) in observational data and convection-permitting climate model simulations. We tested long-term simulations using new forcing data (ERA5) to see the benefits and limitations. We designed a novel approach to obtain the distributions of meteorological parameters (instead of obtaining one value for one event of MCS) before initiations of MCSs to understand preconvective conditions (times from −9 to −1 h from initiation). We also divided MCSs into daytime/nighttime and short-/long-lived MCSs to help predict MCSs longevity considering the initiation times. Our results provide hints for the forecasters to predict MCS longevity based on preconvective conditions from parameters discussed in this work.

Characteristics of the daytime and nighttime MCSs over the Canadian Prairies using an ERA5-forced convection-permitting climate model

This paper describes the occurrences and conditions before Mesoscale Convective Systems (MCSs) are initiated over the Canadian Prairies, using 10 years of observations and convection-permitting climate model simulations. The features of MCSs occurring in summer were analyzed using the Regional Deterministic Reforecast System (RDRS, hourly and 10-km grid spacing) and ECMWF Reanalysis v5 (ERA5)-forced Weather Research and Forecasting (WRF) model simulations (FSCT, 4-km grid spacing). MCSs were defined and tracked using the Method for Object-Based Diagnostic Evaluation-Time Domain (MTD). MTD-identified MCSs were divided into short−/long-lived and daytime/nighttime, considering the longevities and initiation times. FCST showed the skills to simulate MCSs but overestimated MCS features compared to RDRS. Fifteen meteorological parameters were calculated using sounding data from FCST to determine pre-conventional conditions of MCSs (at init. -9, −6, −3, and − 1 h). The distributions of parameters were tested to determine the significance of differences between short- and long-lived MCSs. The key findings are as follows: 1) long-lived daytime MCSs (LLM12) showed favorable thermodynamic processes and 2) long-lived nighttime MCSs (LLM00) were initiated based on dynamic processes. We also found that the most appropriate parameters (i.e., those that were statistically different in short- and long-lived MCSs) to determine the longevities of MCSs were 1) most unstable convective available potential energy and 2) vertical wind shear of 0–3 km.

Mapping storm spatial profiles for flood impact assessments

Synthetic design storms are often used to plan new drainage systems or assess flood impacts on infrastructure. To simulate extreme rainfall events under climate change, design storms can be modified to match a different return frequency of extreme rainfall events as well as a modified temporal distribution of rainfall intensities. However, the same magnitude of change to the rainfall intensities is often applied in space. Several hydrological applications are limited by this. Climate change impacts on urban pluvial floods, for example, require the use of 2D design storms (rainfall fields) at sub-kilometer and sub-hourly scales. Recent kilometer scale climate models, also known as convection-permitting climate models (CPM), provide rainfall outputs at a high spatial resolution, although rainfall simulations are still restricted to a limited number of climate scenarios and time periods. We nevertheless explored the potential use of rainfall data obtained from these models for hydrological flood impact studies by introducing a method of spatial quantile mapping (SQM). To demonstrate the new methodology, we extracted high-resolution rainfall simulations from a CPM for four domains representing different urban areas in Switzerland. Extreme storms that are plausible under the present climate conditions were simulated with a 2D stochastic rainfall model. Based on the CPM-informed stochastically generated rainfall fields, we modified the design storms to fit the future climate scenario using three different methods: the SQM, a uniform quantile mapping, and a uniform adjustment based on a rainfall–temperature relationship. Throughout all storms, the temporal distribution of rainfall was the same. Using a flood model, we assessed the impact of different rainfall adjustment methods on urban flooding. Significant differences were found in the flood water depths and areas between the three methods. In general, the SQM method results in a higher flood impact than the storms that were modified otherwise. The results suggest that spatial storm profiles may need to be re-adjusted when assessing flood impacts.