James M. Done

Modeling high-impact weather and climate: lessons from a tropical cyclone perspective

Although the societal impact of a weather event increases with the rarity of the event, our current ability to assess extreme events and their impacts is limited by not only rarity but also by current model fidelity and a lack of understanding and capacity to model the underlying physical processes. This challenge is driving fresh approaches to assess high-impact weather and climate. Recent lessons learned in modeling high-impact weather and climate are presented using the case of tropical cyclones as an illustrative example. Through examples using the Nested Regional Climate Model to dynamically downscale large-scale climate data the need to treat bias in the driving data is illustrated. Domain size, location, and resolution are also shown to be critical and should be adequate to: include relevant regional climate physical processes; resolve key impact parameters; and accurately simulate the response to changes in external forcing. The notion of sufficient model resolution is introduced together with the added value in combining dynamical and statistical assessments to fill out the parent distribution of high-impact parameters.

Importance of Regional Climate Model Grid Spacing for the Simulation of Heavy Precipitation in the Colorado Headwaters

AbstractSummer and winter daily heavy precipitation events (events above the 97.5th percentile) are analyzed in regional climate simulations with 36-, 12-, and 4-km horizontal grid spacing over the headwaters of the Colorado River. Multiscale evaluations are useful to understand differences across horizontal scales and to evaluate the effects of upscaling finescale processes to coarser-scale features associated with precipitating systems.Only the 4-km model is able to correctly simulate precipitation totals of heavy summertime events. For winter events, results from the 4- and 12-km grid models are similar and outperform the 36-km simulation. The main advantages of the 4-km simulation are the improved spatial mesoscale patterns of heavy precipitation (below ~100 km). However, the 4-km simulation also slightly improves larger-scale patterns of heavy precipitation.

Projections of future summertime ozone over the U.S.

We use a regional coupled chemistry-transport model to assess changes in surface ozone over the summertime U.S. between present and a 2050 future time period at high spatial resolution under the A2 climate and Representative Concentration Pathway (RCP) 8.5 anthropogenic precursor emission scenarios. Predicted changes in regional climate and globally enhanced ozone are estimated to increase surface ozone over most of the U.S.; the 95th percentile for daily 8 h maximum surface ozone increases from 79 ppb to 87 ppb. The analysis suggests that changes in meteorological drivers likely will add to increasing ozone, but the simulations do not allow separating meteorological feedbacks from that due to enhanced global ozone. Stringent emission controls can counteract these feedbacks; if implemented as in RCP8.5, the 95th percentile for surface ozone is reduced to 55 ppb. A comparison of regional to global model projections shows that the global model is biased high in surface ozone compared to the regional model and compared to observations. On average, both the global and the regional model predict similar future changes but reveal pronounced differences in urban and rural regimes that cannot be resolved at the coarse resolution of the considered global model. This study confirms the key role of emission control strategies in future air quality projections and demonstrates the need for considering degradation of air quality with future climate change in policy making. It also illustrates the need for high-resolution modeling when the objective is to address regional and local air quality or establish links to human health and society.

Bias corrections of global models for regional climate simulations of high-impact weather

Abstract All global circulation models (GCMs) suffer from some form of bias, which when used as boundary conditions for regional climate models may impact the simulations, perhaps severely. Here we present a bias correction method that corrects the mean error in the GCM, but retains the six-hourly weather, longer-period climate-variability and climate change from the GCM. We utilize six different bias correction experiments; each correcting different bias components. The impact of the full bias correction and the individual components are examined in relation to tropical cyclones, precipitation and temperature. We show that correcting of all boundary data provides the greatest improvement.

High-Resolution Hurricane Forecasts

Widely varying scales of atmospheric motion make it extremely difficult to predict hurricane intensity even after decades of research. A new model capable of resolving a hurricanes deep convection motions was tested on a large sample of Atlantic tropical cyclones. Results show that using finer resolution can improve storm intensity predictions.

Evaluation of the Advanced Hurricane WRF Data Assimilation System for the 2009 Atlantic Hurricane Season

AbstractReal-time analyses and forecasts using an ensemble Kalman filter (EnKF) and the Advanced Hurricane Weather Research and Forecasting Model (AHW) are evaluated from the 2009 North Atlantic hurricane season. This data assimilation system involved cycling observations that included conventional in situ data, tropical cyclone (TC) position, and minimum SLP and synoptic dropsondes each 6 h using a 96-member ensemble on a 36-km domain for three months. Similar to past studies, observation assimilation systematically reduces the TC position and minimum SLP errors, except for strong TCs, which are characterized by large biases due to grid resolution. At 48 different initialization times, an AHW forecast on 12-, 4-, and 1.33-km grids is produced with initial conditions drawn from a single analysis member. Whereas TC track analyses and forecasts exhibit a pronounced northward bias, intensity forecast errors are similar to (lower than) the NWS Hurricane Weather Research Model (HWRF) and GFDL forecasts for for...

As the Wind Blows? Understanding Hurricane Damages at the Local Level through a Case Study Analysis

An understanding of the potential drivers of local-scale hurricane losses is developed through a case study analysis. Two recent category-3 U.S. landfalling hurricanes (Ivan in 2004 and Dennis in 2005) are analyzed that, although similar in terms of maximum wind speed at their proximate coastal landfall locations, caused vastly different loss amounts. In contrast to existing studies that assess loss mostly at the relatively aggregate level, detailed local factors related to hazard, exposure, and vulnerability are identified. State-level raw wind insured loss data split by personal, commercial, and auto business lines are downscaled to the census tract level using the wind field. At this scale, losses are found to extend far inland and across business lines. Storm size is found to play an important role in explaining the different loss amounts by controlling not only the size of the impacted area but also the duration of damaging winds and the likelihood of large changes in wind direction. An empirical analysis of census tract losses provides further evidence for the importance of wind duration and wind directional change in addition to wind speed. The importance of exposure values however is more sensitive to assumptions in how loss data are downscaled. Appropriate consideration of these local drivers of hurricane loss may improve historical loss assessments and may also act upscale to impact future projections of hurricane losses under climate and socioeconomic change.

Internal variability of North Atlantic tropical cyclones

Using a regional model initial condition ensemble, this study quantifies the magnitude of internal variability of North Atlantic tropical cyclone frequency for a case study year and identifies potential physical sources. For tropical cyclone formations from easterly waves, the simulated internal variability of tropical cyclone frequency for 1998 is approximately two fifths of the total (externally forced and internal) variability of observed tropical cyclone frequency. The simulated internal variability of tropical cyclone frequency is found to arise in approximately equal measure from variability of easterly wave occurrence and development and variability of the transition from incipient warm cores to tropical cyclones. Variable interaction between developing tropical cyclones and vertical wind shear associated with upper level cyclones is identified as a potentially important contributing factor to tropical cyclone internal variability.

The thermohaline circulation and tropical cyclones in past, present, and future climates

WHat: About 40 junior faculty discussed interactions between fast (tropical cyclones) and slow (oceanic overturning circulation) extreme events in the climate system and methods using both paleoclimate proxies and models that could improve our understanding of such events. WHen: 8–10 July 2008 WHere: Boulder, Colorado nderstanding the interactions between tropical cyclones and the thermohaline circulation (THC) and their contributions to climate and climate change is an intriguing, challenging, and important area of research. The oceanic overturning circulation is a slow process whereas tropical cyclones are a fast process, and both may be subject to abrupt or long-term changes. The Early Career Scientist Association’s Junior Faculty Forum explored ways to incorporate modeling, observations, and geologic reconstructions into understanding these interacting components of the climate system in past, present, and future climates. Following an extensive review of the current level of knowledge, including plenary talks by invited speakers, the bulk of the meeting was dedicated to two parallel discussion sessions focusing on the oceanic overturning circulation and tropical cyclones. Topic I of the forum focused on the observational needs as well as the modeling and decadal prediction of the THC [or meridional overturning circulation (MOC)]. The terminology used to describe THC has been much discussed and debated. The exact definition and proper use of the terms “THC” and “MOC” have been clarified because of the confusion in the recent literature (e.g., Wunsch 2002), and no ubiquitous metric exists to quantify either. Often, the general term MOC is used to refer specifically to the Atlantic MOC (AMOC), when in reality each ocean basin contains meridional overturning cells that are interconnected through the global circulation. It is agreed in Annex AFFILIATIONS: Done anD Hu—National Center for Atmospheric Research, Boulder, Colorado; Farmer—Hofstra University, Hempstead, New York; yin—The Florida State University, Tallahassee, Florida; bates—University of Washington, Seattle, Washington; Frappier—Boston College, Chestnut Hill, Massachusetts; HalkiDes—NASA JPL, California Institute of Technology, Pasadena, California; kilbourne—McDaniel College, Westminster, Maryland; sriver—The Pennsylvania State University, University Park, Pennsylvania; WooDruFF—MIT/ WHOI Joint Program, Woods Hole, Massachusetts CORRESPONDING AUTHOR: James M. Done, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000 E-mail: done@ucar@edu DOI:10.1175/2009BAMS2762.1

Potential predictability sources of the 2012 U.S. drought in observations and a regional model ensemble

The 2012 drought was the most severe and extensive summertime US drought in half a century with substantial economic loss and impacts on food security and commodity prices. A unique aspect of the 2012 drought was its rapid onset and intensification over the Southern Rockies, extending to the Great Plains during late spring and early summer, and the absence of known precursor large-scale patterns. Drought prediction therefore remains a major challenge. This study evaluates relationships among snow, soil moisture, and precipitation to identify sources of potential predictability of the 2012 summer drought using observations and a Weather Research and Forecasting (WRF) Model multi-physics ensemble experiment. Although underestimated in intensity, the drought signal is robust to the way atmospheric physical processes are represented in the model. For the Southern Rockies, soil moisture exhibits stronger persistence than precipitation in observations and the ensemble experiment. Correlations between winter/spring snowmelt and concurrent and following season soil moisture, and between soil moisture and concurrent and following season precipitation, in both observations and the model ensemble, suggest potential predictability beyond 1 and 2-month lead-time reside in the land surface conditions for apparent flash droughts such as the 2012 drought.