Realistically representing the present-day characteristics of extreme precipitation has been a challenge for global climate models, which is due in part to deficiencies in model resolution and ...physics, but is also due to a lack of consistency in gridded observations. In this study, we use three observation datasets, including gridded rain gauge and satellite data, to assess historical simulations from sixteen Coupled Model Intercomparison Project Phase 6 (CMIP6) models. We separately evaluate summer and winter precipitation over the United States (US) with a comprehensive set of extreme precipitation indices, including an assessment of precipitation frequency, intensity and spatial structure. The observations exhibit significant differences in their estimates of area-average intensity distributions and spatial patterns of the mean and extremes of precipitation over the US. In general, the CMIP6 multi-model mean performs better than most individual models at capturing daily precipitation distributions and extreme precipitation indices, particularly in comparison to gauge-based data. Also, the representation of the extreme precipitation indices by the CMIP6 models is better in the summer than winter. Although the 'standard' horizontal-resolution can vary significantly across CMIP6 models, from ∼0.7° to ∼2.8°, we find that resolution is not a good indicator of model performance. Overall, our results highlight common biases in CMIP6 models and demonstrate that no single model is consistently the most reliable across all indices.
Quantifying how climate change may impact precipitation extremes is a priority for informing adaptation and policy planning. In this study, Coupled Model Intercomparison Project phase 6 global ...climate models are analyzed to identify robust signals of projected changes in summer and winter precipitation extremes over the United States (US). Under a projected fossil-fuel based economic (i.e. high greenhouse gas emissions) scenario, our results show consistent changes in the seasonal patterns for many precipitation extremes by the end of the 21st century. We find a robust projected increase in the intensity of winter precipitation across models, with less agreement during the summer. Similarly, a robust projected amplification of heavy precipitation over the northern US is evident in winter, while intermodel spread is prevalent in summer projections. Specifically, the heavy and very heavy winter precipitation days (R10mm and R20mm) exhibit larger increases compared to other aspects of precipitation. Additionally, changes in dry extremes (e.g. consecutive dry days) are found to differ significantly across various subregions and seasons. Overall, our results suggest that the US may suffer more natural disasters such as floods and droughts in the future.
Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and ...innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that finescale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions.
Nudging as an assimilation technique has seen increased use in recent years in the development and evaluation of climate models. Constraining the simulated wind and temperature fields using global ...weather reanalysis facilitates more straightforward comparison between simulation and observation, and reduces uncertainties associated with natural variabilities of the large-scale circulation. On the other hand, the forcing introduced by nudging can be strong enough to change the basic characteristics of the model climate. In the paper we show that for the Community Atmosphere Model version 5 (CAM5), due to the systematic temperature bias in the standard model and the sensitivity of simulated ice formation to anthropogenic aerosol concentration, nudging towards reanalysis results in substantial reductions in the ice cloud amount and the impact of anthropogenic aerosols on long-wave cloud forcing. In order to reduce discrepancies between the nudged and unconstrained simulations, and meanwhile take the advantages of nudging, two alternative experimentation methods are evaluated. The first one constrains only the horizontal winds. The second method nudges both winds and temperature, but replaces the long-term climatology of the reanalysis by that of the model. Results show that both methods lead to substantially improved agreement with the free-running model in terms of the top-of-atmosphere radiation budget and cloud ice amount. The wind-only nudging is more convenient to apply, and provides higher correlations of the wind fields, geopotential height and specific humidity between simulation and reanalysis. Results from both CAM5 and a second aerosol–climate model ECHAM6-HAM2 also indicate that compared to the wind-and-temperature nudging, constraining only winds leads to better agreement with the free-running model in terms of the estimated shortwave cloud forcing and the simulated convective activities. This suggests nudging the horizontal winds but not temperature is a good strategy for the investigation of aerosol indirect effects since it provides well-constrained meteorology without strongly perturbing the model's mean climate.
Conventional low‐resolution (LR) climate models, including the Energy Exascale Earth System Model (E3SMv1), have well‐known biases in simulating the frequency, intensity, and timing of precipitation. ...Approaches to next‐generation E3SM, whether the high‐resolution (HR) or multiscale modeling framework (MMF) configuration, improve the simulation of the intensity and frequency of precipitation, but regional and seasonal deficiencies still exist. Here we apply a methodology to assess the contribution of tropical cyclones (TCs), extratropical cyclones (ETCs), and mesoscale convective systems (MCSs) to simulated precipitation in E3SMv1‐HR and E3SMv1‐MMF relative to E3SMv1‐LR. Across the United States, E3SMv1‐MMF provides the best simulation in terms of precipitation accumulation, frequency and intensity from MCSs and TCs compared to E3SMv1‐LR and E3SMv1‐HR. All E3SMv1 configurations overestimate precipitation amounts from and the frequency of ETCs over CONUS, with conventional E3SMv1‐LR providing the best simulation compared to observations despite limitations in precipitation intensity within these events.
Plain Language Summary
Precipitation has direct and major impacts on society, both locally and globally, and thus understanding how precipitation may change in the future is important. Climate models, or mathematical representations of the Earth system, are the tools‐of‐choice, albeit imperfect for projecting future changes in precipitation. Precipitation occurs in many different environments and is produced by a variety of weather phenomena in the United States, including tropical cyclones (TCs), extratropical cyclones (ETCs), and mesoscale convective systems (MCSs). This work acts to quantify the characteristics of precipitation in configurations of the Energy Exascale Earth System Model by these storm‐types to better inform future development of the climate model and produce more accurate projections of future precipitation.
Key Points
Multiscale modeling framework E3SMv1 captures precipitation from mesoscale convective systems (MCSs) better than low‐ and high‐resolution
High‐resolution and multiscale modeling framework E3SMv1 improve tropical cyclone (TC) precipitation
All configurations capture realistic extratropical cyclone precipitation in contrast to TC and MCS, and conventional low‐resolution E3SMv1 does best
Improving the representation of precipitation in Earth system models is essential for understanding and projecting water cycle changes across scales. Progress has been hampered by persistent ...deficiencies in representing precipitation frequency, intensity, and timing in current models. Here, we analyze simulated US precipitation in the low‐resolution (LR) configuration of the Energy Exascale Earth System Model (E3SMv1) and assess the effect of two approaches to enhance the range of explicitly resolved scales: high‐resolution (HR) and multiscale modeling framework (MMF), which incur similar computational expense. Both E3SMv1‐MMF and E3SMv1‐HR capture more intense and less frequent precipitation on hourly and daily timescales relative to E3SMv1‐LR. E3SMv1‐HR improves the intensity over the Eastern and Northwestern US during winter, while E3SMv1‐MMF improves the intensity over the Eastern US and summer diurnal timing over the Central US. These results indicate that both methods may be needed to improve simulations of different storm types, seasons, and regions.
Plain Language Summary
Extreme storms and precipitation are expected to become more intense with climate change. However, current global‐scale numerical models that are used for climate projections often misrepresent the intensity and timing of precipitation compared to observations, which can lower confidence in projected changes. The sources of these deficiencies are associated with the low‐resolution and simplified representation of physical processes (e.g., convection) used in these models in order to make century‐long simulations computationally feasible. Here, we investigate how enhancing the range of scales represented in the Energy Exascale Earth System Model (E3SMv1) can improve precipitation. We focus on two configurations that incur significant but similar computational cost and have drastically different approaches to enhance the range of explicitly represented scales: high horizontal‐resolution (HR; ∼25‐km) and multiscale modeling framework (MMF; 2‐km cloud‐resolving models embedded within each grid column of E3SMv1). Both methods improve the frequency and intensity of precipitation over the United States. However, due to the different scales represented by each method, improvements occur in different seasons and regions, primarily during winter in E3SMv1‐HR and summer in E3SMv1‐MMF. These results indicate which configuration may be most useful for studies of different storm types and suggest both methods may be needed to represent precipitation overall.
Key Points
Multiscale modeling framework (MMF) and high‐resolution (HR) capture more extreme and less frequent precipitation than conventional E3SMv1
E3SMv1‐HR improves intensity and timing of precipitation in the Eastern and Northwestern US during winter in association with ETCs and ARs
E3SMv1‐MMF improves intensity of precipitation in the Eastern US and summer diurnal timing in the Central US related to propagating MCSs
Natural modes of variability on many timescales influence aerosol particle distributions and cloud properties such that isolating statistically significant differences in cloud radiative forcing due ...to anthropogenic aerosol perturbations (indirect effects) typically requires integrating over long simulations. For state‐of‐the‐art global climate models (GCM), especially those in which embedded cloud‐resolving models replace conventional statistical parameterizations (i.e., multiscale modeling framework, MMF), the required long integrations can be prohibitively expensive. Here an alternative approach is explored, which implements Newtonian relaxation (nudging) to constrain simulations with both pre‐industrial and present‐day aerosol emissions toward identical meteorological conditions, thus reducing differences in natural variability and dampening feedback responses in order to isolate radiative forcing. Ten‐year GCM simulations with nudging provide a more stable estimate of the global‐annual mean net aerosol indirect radiative forcing than do conventional free‐running simulations. The estimates have mean values and 95% confidence intervals of −1.19 ± 0.02 W/m2 and −1.37 ± 0.13 W/m2for nudged and free‐running simulations, respectively. Nudging also substantially increases the fraction of the world's area in which a statistically significant aerosol indirect effect can be detected (66% and 28% of the Earth's surface for nudged and free‐running simulations, respectively). One‐year MMF simulations with and without nudging provide global‐annual mean net aerosol indirect radiative forcing estimates of −0.81 W/m2 and −0.82 W/m2, respectively. These results compare well with previous estimates from three‐year free‐running MMF simulations (−0.83 W/m2), which showed the aerosol‐cloud relationship to be in better agreement with observations and high‐resolution models than in the results obtained with conventional cloud parameterizations.
Key Points
Nudged simulations provide more stable estimates of aerosol indirect effects
Nudging increases the area a statistically significant signal can be detected
Nudging enables computation‐expensive GCMs to estimate aerosol indirect effects
Abstract
Accurate simulation of the present-day characteristics of mean and extreme precipitation at regional scales remains a challenge for Earth system models, which is due in part to deficiencies ...in model physics such as convective parameterization (CP), and coarse resolution. High horizontal resolution (HR, ∼25 km) and multiscale modeling framework (MMF, i.e. replacing conventional CP with embedded km-scale cloud-resolving models) are two promising directions that could help improve the interaction between subgrid-scale physical processes and large-scale climate. Here, we evaluate simulated extreme precipitation over the United States (US) across three configurations (i.e. low-resolution LR, HR, and MMF) of the Energy Exascale Earth System Model (E3SMv1) and intercompare them against two gridded observation datasets (climate prediction center daily US precipitation and integrated multi-satellite retrievals for global precipitation measurement). We assess the model’s ability to simulate very heavy seasonal precipitation (illustrated by the difference between the 99th and 90th percentile values) as well as the spatial distributions of several extreme precipitation indices defined by the expert team on climate change detection and indices. Our results show that both the dry (i.e. consecutive dry days (CDD)) and wet (i.e. consecutive wet days, maximum 5 day precipitation, and very wet days) extremes evaluated herein show some improvement as well as degradation with MMF and HR relative to LR. These results vary across seasons and US subregions. For instance, only the very heavy precipitation of winter is improved with MMF and HR. Both configurations alleviate the well-known drizzling bias evident in LR across both winter and summer in many parts of the US, largely due to the overall improvement in intensity and frequency of precipitation. Additionally, our results suggest that while E3SMv1-MMF has higher intensity rates when it does rain, it has too many CDD during the summer, contributing to a low mean precipitation bias.
Improving the representation of precipitation in Earth system models is essential for understanding and projecting water cycle changes across scales. Progress has been hampered by persistent ...deficiencies in representing precipitation frequency, intensity, and timing in current models. Here, we analyze simulated US precipitation in the low-resolution (LR) configuration of the Energy Exascale Earth System Model (E3SMv1) and assess the effect of two approaches to enhance the range of explicitly resolved scales: high-resolution (HR) and multiscale modeling framework (MMF), which incur similar computational expense. Both E3SMv1-MMF and E3SMv1-HR capture more intense and less frequent precipitation on hourly and daily timescales relative to E3SMv1-LR. E3SMv1-HR improves the intensity over the Eastern and Northwestern US during winter, while E3SMv1-MMF improves the intensity over the Eastern US and summer diurnal timing over the Central US. Furthermore, these results indicate that both methods may be needed to improve simulations of different storm types, seasons, and regions.
Improving the representation of precipitation in Earth system models is essential for understanding and projecting water cycle changes across scales. Progress has been hampered by persistent ...deficiencies in representing precipitation frequency, intensity, and timing in current models. Here, we analyze simulated US precipitation in the low-resolution (LR) configuration of the Energy Exascale Earth System Model (E3SMv1) and assess the effect of two approaches to enhance the range of explicitly resolved scales: high-resolution (HR) and multiscale modeling framework (MMF), which incur similar computational expense. Both E3SMv1-MMF and E3SMv1-HR capture more intense and less frequent precipitation on hourly and daily timescales relative to E3SMv1-LR. E3SMv1-HR improves the intensity over the Eastern and Northwestern US during winter, while E3SMv1-MMF improves the intensity over the Eastern US and summer diurnal timing over the Central US. Furthermore, these results indicate that both methods may be needed to improve simulations of different storm types, seasons, and regions.
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