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Wayne Higgins and David Gochis

Abstract

An international team of scientists from the United States, Mexico, and Central America carried out a major field campaign during the summer of 2004 to develop an improved understanding of the North American monsoon system leading to improved precipitation forecasts. Results from this campaign, which is the centerpiece of the North American Monsoon Experiment (NAME) Process Study, are reported in this issue of the Journal of Climate. In addition to a synthesis of key findings, this brief overview article also raises some important unresolved issues that require further attention. More detailed background information on NAME, including motivating science questions, where NAME 2004 was conducted, when, and the experimental design, was published previously by Higgins et al.

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Wei Yu, Weiqing Han, and David Gochis

Abstract

Atmospheric intraseasonal variability in the tropical Atlantic is analyzed using satellite winds, outgoing longwave radiation (OLR), and reanalysis products during 2000–08. The analyses focus on assessing the effects of dominant intraseasonal atmospheric convective processes, the Madden–Julian oscillation (MJO), and Rossby waves on surface wind and convection of the tropical Atlantic Ocean and African monsoon area. The results show that contribution from each process varies in different regions. In general, the MJO events dominate the westward-propagating Rossby waves in affecting strong convection in the African monsoon region. The Rossby waves, however, have larger contributions to convection in the western Atlantic Ocean. Both the westward- and eastward-propagating signals contribute approximately equally in the central Atlantic basin. The effects of intraseasonal signals have evident seasonality. Both convection amplitude and the number of strong convective events associated with the MJO are larger during November–April than during May–October in all regions. Convection associated with Rossby wave events is stronger during November–April for all regions, and the numbers of Rossby wave events are higher during November–April than during May–October in the African monsoon region, and are comparable for the two seasons in the western and central Atlantic basins. Of particular interest is that the MJOs originating from the Indo-Pacific Ocean can be enhanced over the tropical Atlantic Ocean while they propagate eastward, amplifying their impacts on the African monsoon. On the other hand, Rossby waves can originate either in the eastern equatorial Atlantic or West African monsoon region, and some can strengthen while they propagate westward, affecting surface winds and convection in the western Atlantic and Central American regions.

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Melissa S. Bukovsky, David J. Gochis, and Linda O. Mearns

Abstract

The authors examine 17 dynamically downscaled simulations produced as part of the North American Regional Climate Change Assessment Program (NARCCAP) for their skill in reproducing the North American monsoon system. The focus is on precipitation and the drivers behind the precipitation biases seen in the simulations of the current climate. Thus, a process-based approach to the question of model fidelity is taken in order to help assess confidence in this suite of simulations.

The results show that the regional climate models (RCMs) forced with a reanalysis product and atmosphere-only global climate model (AGCM) time-slice simulations perform reasonably well over the core Mexican and southwest United States regions. Some of the dynamically downscaled simulations do, however, have strong dry biases in Arizona that are related to their inability to develop credible monsoon flow structure over the Gulf of California. When forced with different atmosphere–ocean coupled global climate models (AOGCMs) for the current period, the skill of the RCMs subdivides largely by the skill of the forcing or “parent” AOGCM. How the inherited biases affect the RCM simulations is investigated. While it is clear that the AOGCMs have a large influence on the RCMs, the authors also demonstrate where the regional models add value to the simulations and discuss the differential credibility of the six RCMs (17 total simulations), two AGCM time slices, and four AOGCMs examined herein. It is found that in-depth analysis of parent GCM and RCM scenarios can identify a meaningful subset of models that can produce credible simulations of the North American monsoon precipitation.

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Brant Liebmann, Ileana Bladé, Nicholas A. Bond, David Gochis, Dave Allured, and Gary T. Bates

Abstract

The core region of the North American summer monsoon is examined using spatially averaged daily rainfall observations obtained from gauges, with the objective of improving understanding of its climatology and variability. At most grid points, composite and interannual variations of the onset and end of the wet season are well defined, although, among individual stations that make up a grid average, variability is large. The trigger for monsoon onset in southern and eastern Mexico appears to be related to a change in vertical velocity, while for northwestern Mexico, Arizona, and New Mexico it is related to a reduction in stability, as indicated by a decrease in the lifted index. The wet-season rain rate is a combination of the wet-day rain rate, which decreases with distance from the coast, and the wet-day frequency, which is largest over the Sierra Madre Occidental. Thus the maximum total rate lies slightly to the west of the highest orography. As has been previously noted, onset is not always well correlated with total seasonal precipitation, so in these areas, variations of wet-day frequency and wet-day rain rate must be important. Correlations are small between the wet-day frequency and the wet-day rate, and the former is better correlated than the latter with the seasonal rain rate. Summer rainfall in central to southern Mexico exhibits moderate negative correlations with the leading pattern of sea surface temperature (SST) anomalies in the equatorial Pacific, which projects strongly onto El Niño. The influence of equatorial SSTs on southern Mexico rainfall seems to operate mainly through variability of the wet-day frequency, rather than through variations of the wet-day rain rate.

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David J. Gochis, Christopher J. Watts, Jaime Garatuza-Payan, and Julio Cesar-Rodriguez

Abstract

Detailed information on the spatial and temporal characteristics of precipitation intensity from the mountainous region of northwest Mexico has, until recently, been lacking. As part of the 2004 North American Monsoon Experiment (NAME) enhanced observing period (EOP) surface rain gauge networks along with weather radar and orbiting satellites were employed to observe precipitation in a manner heretofore unprecedented for this semiarid region. The NAME Event Rain gauge Network (NERN), which has been in operation since 2002, contributed to this effort. Building on previous work, this paper presents analyses on the spatial and temporal characteristics of precipitation intensity as observed by NERN gauges. Analyses from the 2004 EOP are compared with the 2002–04 period and with long-term gauge observations. It was found that total precipitation from July to August of 2004 was similar in spatial extent and magnitude to the long-term average, though substantially wetter than 2003. Statistical analyses of precipitation intensity data from the NERN reveal that large precipitation events at hourly and daily time scales are restricted to coastal and low-elevation areas west of the Sierra Madre Occidental. At 10-min time scales, maximum intensity values equal to those at low elevations could be observed at higher elevations though they were comparatively infrequent. It is also shown that the inclusion of NERN observations in existing operational analyses helps to correct significant biases, which, on the seasonal time scale, are of similar magnitude as the interannual variability in precipitation in key headwater regions of northwest Mexico.

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Melissa S. Bukovsky, Carlos M. Carrillo, David J. Gochis, Dorit M. Hammerling, Rachel R. McCrary, and Linda O. Mearns

Abstract

This study presents climate change results from the North American Regional Climate Change Assessment Program (NARCCAP) suite of dynamically downscaled simulations for the North American monsoon system in the southwestern United States and northwestern Mexico. The focus is on changes in precipitation and the processes driving the projected changes from the regional climate simulations and their driving coupled atmosphere–ocean global climate models. The effect of known biases on the projections is also examined. Overall, there is strong ensemble agreement for a large decrease in precipitation during the monsoon season; however, this agreement and the magnitude of the ensemble-mean change is likely deceiving, as the greatest decreases are produced by the simulations that are the most biased in the baseline/current climate. Furthermore, some of the greatest decreases in precipitation are being driven by changes in processes/phenomena that are less credible (e.g., changes in El Niño–Southern Oscillation, when it is initially not simulated well). In other simulations, the processes driving the precipitation change may be plausible, but other biases (e.g., biases in low-level moisture or precipitation intensity) appear to be affecting the magnitude of the projected changes. The most and least credible simulations are clearly identified, while the other simulations are mixed in their abilities to produce projections of value.

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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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Michael A. Brunke, Patrick Broxton, Jon Pelletier, David Gochis, Pieter Hazenberg, David M. Lawrence, L. Ruby Leung, Guo-Yue Niu, Peter A. Troch, and Xubin Zeng

Abstract

One of the recognized weaknesses of land surface models as used in weather and climate models is the assumption of constant soil thickness because of the lack of global estimates of bedrock depth. Using a 30-arc-s global dataset for the thickness of relatively porous, unconsolidated sediments over bedrock, spatial variation in soil thickness is included here in version 4.5 of the Community Land Model (CLM4.5). The number of soil layers for each grid cell is determined from the average soil depth for each 0.9° latitude × 1.25° longitude grid cell. The greatest changes in the simulation with variable soil thickness are to baseflow, with the annual minimum generally occurring earlier. Smaller changes are seen in latent heat flux and surface runoff primarily as a result of an increase in the annual cycle amplitude. These changes are related to soil moisture changes that are most substantial in locations with shallow bedrock. Total water storage (TWS) anomalies are not strongly affected over most river basins since most basins contain mostly deep soils, but TWS anomalies are substantially different for a river basin with more mountainous terrain. Additionally, the annual cycle in soil temperature is partially affected by including realistic soil thicknesses resulting from changes in the vertical profile of heat capacity and thermal conductivity. However, the largest changes to soil temperature are introduced by the soil moisture changes in the variable soil thickness simulation. This implementation of variable soil thickness represents a step forward in land surface model development.

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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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