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Charles Jones and Jimy Dudhia

Abstract

The Madden–Julian oscillation (MJO) is an important source of predictability. The boreal 2004/05 winter is used as a case study to conduct predictability experiments with the Weather Research and Forecasting (WRF) Model. That winter season was characterized by an MJO event, weak El Niño, strong North Atlantic Oscillation, and extremely wet conditions over the contiguous United States (CONUS). The issues investigated are as follows: 1) growth of forecast errors in the tropics relative to the extratropics, 2) propagation of forecast errors from the tropics to the extratropics, 3) forecast error growth on spatial scales associated with MJO and non-MJO variability, and 4) the relative importance of MJO and non-MJO tropical variability on predictability of precipitation over CONUS.

Root-mean-square errors in forecasts of normalized eddy kinetic energy (NEKE) (200 hPa) show that errors in initial conditions in the tropics grow faster than in the extratropics. Potential predictability extends out to about 4 days in the tropics and 9 days in the extratropics. Forecast errors in the tropics quickly propagate to the extratropics, as demonstrated by experiments in which initial conditions are only perturbed in the tropics. Forecast errors in NEKE (200 hPa) on scales related to the MJO grow slower than in non-MJO variability over localized areas in the tropics and short lead times. Potential predictability of precipitation extends to 1–5 days over most of CONUS but to longer leads (7–12 days) over regions with orographic precipitation in California. Errors in initial conditions on small scales relative to the MJO quickly grow, propagate to the extratropics, and degrade forecast skill of precipitation.

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Ethan D. Gutmann, Roy M. Rasmussen, Changhai Liu, Kyoko Ikeda, David J. Gochis, Martyn P. Clark, Jimy Dudhia, and Gregory Thompson

Abstract

Statistical downscaling is widely used to improve spatial and/or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse (50–200 km) and often misrepresent extremes in important meteorological variables, such as temperature and precipitation. However, these downscaling methods rely on current estimates of the spatial distributions of these variables and largely assume that the small-scale spatial distribution will not change significantly in a modified climate. In this study the authors compare data typically used to derive spatial distributions of precipitation [Parameter-Elevation Regressions on Independent Slopes Model (PRISM)] to a high-resolution (2 km) weather model [Weather Research and Forecasting model (WRF)] under the current climate in the mountains of Colorado. It is shown that there are regions of significant difference in November–May precipitation totals (>300 mm) between the two, and possible causes for these differences are discussed. A simple statistical downscaling is then presented that is based on the 2-km WRF data applied to a series of regional climate models [North American Regional Climate Change Assessment Program (NARCCAP)], and the downscaled precipitation data are validated with observations at 65 snow telemetry (SNOTEL) sites throughout Colorado for the winter seasons from 1988 to 2000. The authors also compare statistically downscaled precipitation from a 36-km model under an imposed warming scenario with dynamically downscaled data from a 2-km model using the same forcing data. Although the statistical downscaling improved the domain-average precipitation relative to the original 36-km model, the changes in the spatial pattern of precipitation did not match the changes in the dynamically downscaled 2-km model. This study illustrates some of the uncertainties in applying statistical downscaling to future climate.

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Daniel Argüeso, José M. Hidalgo-Muñoz, Sonia R. Gámiz-Fortis, María Jesús Esteban-Parra, Jimy Dudhia, and Yolanda Castro-Díez

Abstract

This paper evaluates the Weather Research and Forecasting model (WRF) sensitivity to eight different combinations of cumulus, microphysics, and planetary boundary layer (PBL) parameterization schemes over a topographically complex region in southern Spain (Andalusia) for the period 1990–99. The WRF configuration consisted of a 10-km-resolution domain nested in a coarser domain driven by 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) data, with spectral nudging above the PBL employed over the latter domain. The model outputs have been compared at different time scales with an observational dataset that comprises 438 rain gauges and 152 temperature stations with records of both daily maximum and minimum temperatures. To reduce the “representation error,” the validation with observations has been performed using a multistep regionalization that distinguishes five precipitation regions and four temperature regions.

The analysis proves that both cumulus and PBL schemes have a crucial impact on the description of precipitation in Andalusia, whereas no noticeable differences between microphysics options were appreciated. Temperature is described similarly by all the configurations, except for the PBL choice affecting minimum values.

WRF provides a definite improvement over ERA-40 in the climate description in terms of frequency, spatial distribution, and intensity of extreme events. It also captures more accurately the annual cycle and reduces the biases and the RMSE for monthly precipitation, whereas only a minor enhancement of these features was obtained for monthly-mean maximum and minimum temperatures. The results indicate that WRF is able to correctly reproduce Andalusian climate and produces useful added-value information for climate studies.

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Pedro A. Jiménez, Jordi Vilà-Guerau de Arellano, J. Fidel González-Rouco, Jorge Navarro, Juan P. Montávez, Elena García-Bustamante, and Jimy Dudhia

Abstract

Variations in the diurnal wind pattern associated with heat waves and drought conditions are investigated climatologically at a regional level (northeast of the Iberian Peninsula). The study, based on high-density observational evidence and fine spatial-scale mesoscale modeling for the 1992–2004 period, shows that wind speed can decrease up to 22% under situations characterized by extremely high temperatures and severe drought, such as the European summer of 2003. By examining the role of the different atmospheric scales of motion that determine the wind diurnal variability, it is found that the 2003 synoptic conditions are the main driver for changes in the wind speed field. In turn, these changes are modulated by mesoscale circulations influenced by the soil moisture availability. The results have implications for broad regional modeling studies of current climate and climate change simulations in as much as the study demonstrates that a correct representation of local soil moisture conditions impacts atmospheric circulation and therefore the regional climate state.

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Roy Rasmussen, Changhai Liu, Kyoko Ikeda, David Gochis, David Yates, Fei Chen, Mukul Tewari, Michael Barlage, Jimy Dudhia, Wei Yu, Kathleen Miller, Kristi Arsenault, Vanda Grubišić, Greg Thompson, and Ethan Gutmann

Abstract

Climate change is expected to accelerate the hydrologic cycle, increase the fraction of precipitation that is rain, and enhance snowpack melting. The enhanced hydrological cycle is also expected to increase snowfall amounts due to increased moisture availability. These processes are examined in this paper in the Colorado Headwaters region through the use of a coupled high-resolution climate–runoff model. Four high-resolution simulations of annual snowfall over Colorado are conducted. The simulations are verified using Snowpack Telemetry (SNOTEL) data. Results are then presented regarding the grid spacing needed for appropriate simulation of snowfall. Finally, climate sensitivity is explored using a pseudo–global warming approach. The results show that the proper spatial and temporal depiction of snowfall adequate for water resource and climate change purposes can be achieved with the appropriate choice of model grid spacing and parameterizations. The pseudo–global warming simulations indicate enhanced snowfall on the order of 10%–25% over the Colorado Headwaters region, with the enhancement being less in the core headwaters region due to the topographic reduction of precipitation upstream of the region (rain-shadow effect). The main climate change impacts are in the enhanced melting at the lower-elevation bound of the snowpack and the increased snowfall at higher elevations. The changes in peak snow mass are generally near zero due to these two compensating effects, and simulated wintertime total runoff is above current levels. The 1 April snow water equivalent (SWE) is reduced by 25% in the warmer climate, and the date of maximum SWE occurs 2–17 days prior to current climate results, consistent with previous studies.

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