May 23 - July 29
Climate Change Projections of Severe Weather Environments by Synoptic Patterns in GFDL-CM3
Sofia Avila Nevarez (Virginia Tech), Dr. Kim Hoogewind (OU/CIWRO & NOAA/NSSL), & Dr. Harold Brooks (NOAA/NSSL)
What is already known:
- Moisture, instability (convective available potential energy; CAPE), and strong deep-layer vertical wind shear are necessary for severe thunderstorms, while convective inhibition (CIN) can suppress storm initiation
- Mean CAPE and CIN are projected to increase, while 0-6 km shear decreases by the end of the century according to global climate models
- Days with favorable CAPE and shear combinations (“severe weather days”) are projected to increase,although the summer is most uncertain
- Storm initiation is a major uncertainty
- Potential changes in atmospheric circulation due to climate change is relatively unexplored in relation to severe weather, and may provide additional information important for the initiation problem
What this study adds:
- This work explores the connection between synoptic patterns and the ingredients needed for severe thunderstorms
- Synoptic patterns defined by 500hPa standardized height anomalies, and objectively classified into similar patterns by a self-organizing map
- Western/central U.S. ridge patterns are projected to become more frequent by the end of the 21st century, which are relatively unfavorable for severe weather
- Ridge patterns are projected to increase more during the summer than other seasons
- Severe weather days increase overall in the same ridge pattern that increases
- Mean CAPE and CIN are projected to be greater in magnitude in the central U.S. than other regions in the ridge pattern
- Shear is projected to decrease in all synoptic patterns in the entire U.S
- Changes in severe weather environments are dependent on synoptic patterns
Abstract:
The research on impacts of climate change on severe weather has mostly focused on estimating changes of environmental parameters (e.g., convective available potential energy, deep-layer shear) in global climate models, however, this approach does not take into account the synoptic pattern that can indirectly influence severe weather. Using a self-organizing map, a type of artificial neural network, sixteen distinct synoptic patterns based on 500 hPa geopotential height anomalies are identified. Changes in daily pattern frequencies are assessed within the Geophysical Fluid Dynamics Laboratory Climate Model v3 under future representative concentration pathways (RCP) scenario, RCP4.5 and RCP8.5. Environmental parameters commonly related with severe weather environments are associated with each synoptic pattern type, and the changes of the parameters under those patterns are evaluated within the future simulations. In both RCP4.5 and RCP8.5, western/central U.S. ridge patterns are projected to become more frequent, especially during the summer, and most distinctly in RCP8.5. However, this synoptic pattern is typically unfavorable for severe weather for the U.S. as a whole. Within these patterns, convective available potential energy (CAPE) and the magnitude of convective inhibition (CIN) increase significantly, particularly in the central U.S. The increase in CIN, as well as declining frequencies in other summer patterns, could explain the overall projected decrease severe weather environment days by 2100 during JJA in RCP8.5., but there is still uncertainty as global climate models generally conflict in their projections of environmental parameters during the summer months.