Jonathan M. Wilkinson

Do convection-permitting regional climate models improve projections of future precipitation change?

AbstractRegional climate projections are used in a wide range of impact studies, from assessing future flood risk to climate change impacts on food and energy production. These model projections are typically at 12–50-km resolution, providing valuable regional detail but with inherent limitations, in part because of the need to parameterize convection. The first climate change experiments at convection-permitting resolution (kilometer-scale grid spacing) are now available for the United Kingdom; the Alps; Germany; Sydney, Australia; and the western United States. These models give a more realistic representation of convection and are better able to simulate hourly precipitation characteristics that are poorly represented in coarser-resolution climate models. Here we examine these new experiments to determine whether future midlatitude precipitation projections are robust from coarse to higher resolutions, with implications also for the tropics. We find that the explicit representation of the convective st...

Strong control of Southern Ocean cloud reflectivity by ice-nucleating particles

Significance Simulated clouds over the Southern Ocean reflect too little solar radiation compared with observations, which results in errors in simulated surface temperatures and in many other important features of the climate system. Our results show that the radiative properties of the most biased types of clouds in cyclonic systems are highly sensitive to the concentration of ice-nucleating particles. The uniquely low concentrations of ice-nucleating particles in this remote marine environment strongly inhibit precipitation and allow much brighter clouds to be sustained. Large biases in climate model simulations of cloud radiative properties over the Southern Ocean cause large errors in modeled sea surface temperatures, atmospheric circulation, and climate sensitivity. Here, we combine cloud-resolving model simulations with estimates of the concentration of ice-nucleating particles in this region to show that our simulated Southern Ocean clouds reflect far more radiation than predicted by global models, in agreement with satellite observations. Specifically, we show that the clouds that are most sensitive to the concentration of ice-nucleating particles are low-level mixed-phase clouds in the cold sectors of extratropical cyclones, which have previously been identified as a main contributor to the Southern Ocean radiation bias. The very low ice-nucleating particle concentrations that prevail over the Southern Ocean strongly suppress cloud droplet freezing, reduce precipitation, and enhance cloud reflectivity. The results help explain why a strong radiation bias occurs mainly in this remote region away from major sources of ice-nucleating particles. The results present a substantial challenge to climate models to be able to simulate realistic ice-nucleating particle concentrations and their effects under specific meteorological conditions.

Collaborative Efforts between the United States and United Kingdom to Advance Prediction of High-Impact Weather

AbstractIn recent years, a growing partnership has emerged between the Met Office and the designated U.S. national centers for expertise in severe weather research and forecasting, that is, the National Oceanic and Atmospheric Administration (NOAA) National Severe Storms Laboratory (NSSL) and the NOAA Storm Prediction Center (SPC). The driving force behind this partnership is a compelling set of mutual interests related to predicting and understanding high-impact weather and using high-resolution numerical weather prediction models as foundational tools to explore these interests.The forum for this collaborative activity is the NOAA Hazardous Weather Testbed, where annual Spring Forecasting Experiments (SFEs) are conducted by NSSL and SPC. For the last decade, NSSL and SPC have used these experiments to find ways that high-resolution models can help achieve greater success in the prediction of tornadoes, large hail, and damaging winds. Beginning in 2012, the Met Office became a contributing partner in ann...

A pan-Africa convection-permitting regional climate simulation with the Met Office Unified Model: CP4-Africa

AbstractA convection-permitting multiyear regional climate simulation using the Met Office Unified Model has been run for the first time on an Africa-wide domain. The model has been run as part of the Future Climate for Africa (FCFA) Improving Model Processes for African Climate (IMPALA) project, and its configuration, domain, and forcing data are described here in detail. The model [Pan-African Convection-Permitting Regional Climate Simulation with the Met Office UM (CP4-Africa)] uses a 4.5-km horizontal grid spacing at the equator and is run without a convection parameterization, nested within a global atmospheric model driven by observations at the sea surface, which does include a convection scheme. An additional regional simulation, with identical resolution and physical parameterizations to the global model, but with the domain, land surface, and aerosol climatologies of CP4-Africa, has been run to aid in the understanding of the differences between the CP4-Africa and global model, in particular to ...

Cloud Microphysical Factors Affecting Simulations of Deep Convection During the Presummer Rainy Season in Southern China

The sensitivity of subtropical deep convection to the parameterization of cloud microphysics is elucidated through high-resolution modeling of extreme presummer rainfall over southern China. An ensemble of physics configuration experiments is used to identify several drivers of model errors in comparison to radar observations from the South China Monsoon Rainfall Experiment (SCMREX) and remotely sensed estimates of cloud, precipitation, and radiation from satellites in the A-train constellation. The benefits of increasing the number of prognostic variables in the microphysics scheme is assessed, relative to the effects of the parameterization of cloud microphysical properties and cloud fraction diagnosis. By matching individual parameterizations between the microphysical configurations, it is shown that a small subset of the parameterization changes can reproduce most of the dependence of model performance on physics configuration. In particular, biases that are due to the low-level clouds and rain are strongly influenced by cloud fraction diagnosis and raindrop size distribution, whereas variations in the effects of high clouds are strongly influenced by differences in the parameterization of ice crystal sedimentation. Hence, for the case studied here, these parameterizations give more insight into the causes of variability in model performance than does the number of model prognostics per se.

Simulated Lightning in a Convection Permitting Global Model: SIMULATED GLOBAL LIGHTNING

High‐resolution (grid spacing 10 km in midlatitudes) model simulations using explicitly resolved convection in the Met Office Unified Model, as part of the Horizon 2020 PRIMAVERA project, are used to provide a global lightning climatology. The results show for the first time that global simulations can capture the strong diurnal flash rate variation as well as the seasonal variation. The lightning parametrization uses information about the graupel and ice water path to estimate a total lightning flash rate. Comparisons are made with the World Lightning Location Network (that mainly detects cloud to ground lightning) and combined Lightning Imaging Sensor and Optical Transients Detector data set (that provides an estimate of total flash rate). The model results generally capture the temporal behavior and spatial distribution of the lightning over land. Over the ocean, the lightning in the Intertropical Convergence Zone appears excessive.