Does Nudging Squelch the Extremes in Regional Climate Modeling?

Abstract

An important question in regional climate downscaling is whether to constrain (nudge) the interior of the limited-area domain toward the larger-scale driving fields. Prior research has demonstrated that interior nudging can increase the skill of regional climate predictions originating from historical data. However, there is concern that nudging may also inhibit the regional model's ability to properly develop and simulate mesoscale features, which may reduce the value added from downscaling by altering the representation of local climate extremes. Extreme climate events can result in large economic losses and human casualties, and regional climate downscaling is one method for projecting how climate change scenarios will affect extreme events locally. In this study, the effects of interior nudging are explored on the downscaled simulation of temperature and precipitation extremes. Multidecadal, continuous Weather Research and Forecasting model simulations of the contiguous United States are performed using coarse reanalysis fields as proxies for global climate model fields. The results demonstrate that applying interior nudging improves the accuracy of simulated monthly means, variability, and extremes over the multidecadal period. The results in this case indicate that interior nudging does not inappropriately squelch the prediction of temperature and precipitation extremes and is essential for simulating extreme events that are faithful in space and time to the driving largescale fields.

The geographic distribution and economic value of climate change-related ozone health impacts in the United States in 2030

“…The RCP 6.0 scenario (Fujino et al, 2006) assumes a modest degree of mitigation of greenhouse gas emissions such that total radiative forcing will increase over the next century before stabilizing at 6.0 W m −2 in 2100. We downscaled these GCM projections using the Weather Research and Forecasting (WRF) model following techniques described by Bowden et al (2012) and Otte et al (2012). We conducted WRF simulations at 36-km horizontal grid spacing over the United States, incorporating the global climate forcing at the lateral boundaries, through the interior of the domain (following Otte et al, 2012), and in the sea-surface temperatures.…”

“…We downscaled these GCM projections using the Weather Research and Forecasting (WRF) model following techniques described by Bowden et al (2012) and Otte et al (2012). We conducted WRF simulations at 36-km horizontal grid spacing over the United States, incorporating the global climate forcing at the lateral boundaries, through the interior of the domain (following Otte et al, 2012), and in the sea-surface temperatures. It is important to recognize that climate simulated by and downscaled from GCMs for a particular historical (or future) day cannot be compared directly with the actual meteorology that occurred (or will occur) on that day.…”

The WASCAL high-resolution regional climate simulation ensemble for West Africa: concept, dissemination and assessment

“…While an individual model run can provide a plausible representation of the future under a given climate change scenario, it does not allow an estimate of the range of outcomes expected for the assessment of risks and opportunities (Buontempo et al, 2015). Further, large uncertainties and errors are associated with the result of each model run as a consequence of imperfect initial conditions, with the model being an imperfect abstraction of reality, and from numerical errors and artifacts accumulating in long-term simulations (for example, Laprise, 2003;Park et al, 2014).…”

Towards a probabilistic regional reanalysis system for Europe: evaluation of precipitation from experiments

“…. In general, nudging has been used for many applications up to present including research and development of NWP Seaman, 1990, 1991;Seaman et al, 1995;Schraff, 1997;Leidner et al, 2001;Deng et al, 2004;Schroeder et al, 2006), research in the area of hybrid data assimilation methods (Lei et al, 2012a(Lei et al, , 2012b, initialisation of climate runs (Otte et al, 2012;Baehr et al, 2014) and in ocean data assimilation (Chen et al, 2013). Even though it is not mathematically optimal in a least-squares or maximumlikelihood sense, nudging has a good performance-cost ratio that argues for its usability for the purpose of ensemble reanalyses.…”