Capacity Center for Climate & Weather Extremes

Increased Rainfall Volume from Future Convective Storms in the U.S.

Mesoscale Convective Systems (MCSs) are organized convective storms that contribute up to 70% of the Central United States’ warm season rainfall. Extreme MCSs can cause severe flooding especially when they are slow moving. The West Virginia flood of 2016, the Louisiana flooding during the same year, and the Houston flooding of 2017 are prominent examples of recent floods that were caused by stationary MCSs. Simulating MCSs in climate models is a long-standing challenge since state-of-the-art model resolution is insufficient to resolve important processes such as deep convection or cold-pool dynamics. In this study, we use the Weather Research and Forecasting (WRF) model at a North American convection-permitting scale (4 km horizontal grid spacing). The 13-year long control simulation is able to realistically capture MCS characteristics such as speed, peak rainfall, and rainfall volume compared to high-resolution precipitation observations.

MCS hourly peak rainfall rates are increasing by approximately 15% to 40% until the end of the 21st century assuming business as usual emissions (see figure). This is in line with theoretical expectations that predict a 6.5% increase per degree warming (we simulate ~4 °C warmer conditions). However, the area that is covered with high rainfall rates is increasing much faster and almost doubles (see figure). This leads to a close to doubling of the rainfall volume, which is particularly problematic for catchments with a high ratio of paved surfaces such as big cities (New York City is shown as an example below). This volume increase elevates the risk of flash flooding to unexpected levels. The speed of MCSs does not change systematically but extreme MCSs (peak rainfall rates >90 mm/h) are more than quadrupling in the future, which makes heavy precipitating, slow-moving MCSs much more likely. Current investments in long-lived infrastructures, such as flood protection and water management systems, need to take these changes into account to improve climate adaptation practices.

Figure: Average precipitation in the 40 MCSs with highest peak hourly precipitation in the Mid-Atlantic region
Figure: Average precipitation in the 40 MCSs with highest peak hourly precipitation in the Mid-Atlantic region in the current (a) and future (b) climate. The area with heavy precipitation (>10 mm/h) is highlighted in red. The area of New York City is shown as size reference.

Prein, A.F., Liu, C., Ikeda, K., Trier, S.B., Rasmussen, R.M., Holland, G.J. and Clark, M.P., 2017. Increased rainfall volume from future convective storms in the US. Nature Climate Change, 7(12), p.880

Decadal Variability of Western U.S. Winter Storms

Are we about to enter a multi-year drought? Will there be wild swings between wet and dry winters? These are critical questions for water resource managers, and advance warning of such events would allow for appropriate drought or flood risk mitigation. But do we understand these events? and can we predict them? NCAR conducted a modeling study to find out.

The very wet 2016/2017 winter across the Western U.S. was simulated many times using the global atmosphere-only Model for Prediction Across Scales (MPAS). Two sets of simulations were generated. The two sets were given ocean temperature patterns consistent with opposite phases of the mode of climate variability known as the Interdecadal Pacific Oscillation (IPO). The atmospheric responded very differently to each phase of the IPO. Under negative IPO, a subtropical high-pressure system off the California coast directed winter storms away from California towards the Pacific Northwest. Under positive IPO, however, strong winds directed winter storms southward into coastal and southern California (see figure). As a result, positive IPO drove twice the number of strong winter storms into coastal California compared to negative IPO. This work is particularly timely: our climate has recently shifted to a positive IPO state.

This work is part of the NSF-funded project UDECIDE (Understanding Decision-Climate Interactions on Decadal Scales), AGS-1419563. In this project NCAR partners with Colorado State University, the University of Pennsylvania, Jacobs (an engineering company), and a number of water agencies to advance our understanding of the role of decadal climate prediction for flood risk and water resource management.

Figure: Simulated difference in winter precipitation between the positive and negative phases of the Interdecadal Pacific Oscillation
Figure: Simulated difference in winter precipitation between the positive and negative phases of the Interdecadal Pacific Oscillation (IPO) (mm). The positive phase of the IPO drives precipitation away from northwestern regions towards coastal and southern California. Figure from Morss et al. (2018)

Morss, R.E., Done, J.M., Lazrus, H., Towler, E. and Tye, M.R. (2018) Assessing and communicating uncertainty in decadal climate predictions: Connecting predictive capacity to stakeholder needs. US CLIVAR Variations 16(3), doi:10.5065/D62N513R

Global Estimates of Damaging Hail Hazard

Hail storms cause significant economic damage in many areas around the globe. In 2018, U.S. economic losses from hail damage will exceed $10 B for the eleventh year in a row. Still, quantifying hail hazards remains highly uncertain due to missing or deficient observational datasets. In this study, MMM scientists in collaboration with the Insurance Australia Group (IAG) develop a damaging-hail probability index to improve our ability to document and understand global large-hail frequencies and their variability on seasonal to decadal timescales. Environmental ingredients that were present during observed U.S. large hail events (hailstone diameter ≥2.5 cm) are identified based on reanalysis data. Four of these enable a skillful separation between hail-producing and non-hail-producing environments. The index is developed using U.S. data and is evaluated with independent European and Australian hail observations. It has a high skill in capturing observed large hail events in all regions and outperforms existing indices that are used for hail forecasting and hazard estimates. The annual large hail probability estimates (see figure below) compare well with global hail frequency estimates based on satellite data.

The index is flexible and can be used in weather forecasting and climate models. It allows a skillful hazard estimate from city to global-scales and from daily to climate time-scales. The code is open-source and is applicable to a variety of tasks such as daily to seasonal forecasting of large hail frequencies and assessing climate change effects on hail hazard.

Figure: Annual average large hail probability estimates
Figure: Annual average large hail probability estimates in 100x100 km areas from 1979 to 2015 based on ERA-Interim reanalysis data.

Prein A.F., G.J. Holland, 2018: Global Estimates of Damaging Hail Hazard. Weather and Climate Extremes (accepted with revisions)