Specification of Monthly Precipitation in the Western United States from Monthly Mean Circulation

Weare, Bryan C.; Hoeschele, M.

doi:10.1175/1520-0450(1983)022<1000:sompit>2.0.co;2

Cited by 25 publications

(18 citation statements)

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“…The importance of stability in interpreting links between propinquitous 700-mbar height and surface temper- ature has been demonstrated by Kline and Klein [1986]. Vorticity has been used by Cayan and Roads [1984] and by Weare and Hoeschele [1983], but not in conjunction with any other variable as a predictor. Cayan and Roads found that "local" vorticity (i.e., based on a single grid point) was not a good predictor for area average precipitation along the U.S. west coast, whereas Weare and Hoeschele obtained good results using the larger-scale empirical orthogonal functions of vorticity as predictors.…”

Section: The Above Examples Show That Useful Small-scale Climate Chanmentioning

confidence: 99%

“…These There is a considerable body of related work on the links between large-scale and local-scale climate, mostly developed in the context of long-range weather forecasting. The majority of this work considers relationships between the 700-mbar height field and either temperature [e.g., Klein, 1962Klein, , 1983Klein, , 1985Walsh et al, 1982a;Klein and Walsh, 1983;Klein and Kline, 1984;Kline and Klein, 1986;Klein and Yang, 1986] or precipitation [e.g., Klein, 1963;Harnack, 1979;Weare and Hoeschele, 1983;Klein and Bloom, 1987] (but see also Pittock [1977]). The main differences between these works and the present paper are in the spatial extent of the predictor variable field (much larger in all of the above) and in the number of different types of predictor variables (many more used here).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Obtaining sub‐grid‐scale information from coarse‐resolution general circulation model output

Wigley¹,

Jones²,

Briffa³

et al. 1990

J. Geophys. Res.

271

157

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The predictor variables employed are area averages (over ~2.5 × 106 km2) of temperature and precipitation and propinquitous grid point values of mean sea level pressure and 700 mbar height, together with the zonal and meridional gradients of these two variables. Regression analyses are performed using monthly-mean data from Oregon, with separate analyses for each month. In independent verification, Spatial-mean explained variances range from 58 to 87% for temperature and from 39 to 76% for precipitation. Most of the variance explained arises from the area average of the variable which is the predictand: in other words, if the temperature, say, at point x is to be estimated, the best predictor is generally the area average temperature

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Section: The Above Examples Show That Useful Small-scale Climate Chanmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Obtaining sub‐grid‐scale information from coarse‐resolution general circulation model output

Wigley¹,

Jones²,

Briffa³

et al. 1990

J. Geophys. Res.

271

157

View full text Add to dashboard Cite

show abstract

“…As with SLP we develop two conditioned daily models, and aggregate these to two conditioned monthly models and then combine both into an induced monthly model. Cayan & Peterson (1989) and Weare & Hoeschele (1983) found, for California, that patterns in atmospheric circulation (as exemplified by SLP) do have a strong effect on precipitation over much of the state, during the wet months, October to May. At Valentia, Ireland, precipitation is spread more evenly throughout the year, with a mean varying from about 70-90 mm per month in April, May, June and July to 100-160 mm per month for all other months.…”

Section: Introductionmentioning

confidence: 97%

Conditioning stochastic properties of daily precipitation on indices of atmospheric circulation

Kiely

Albertson

Parlange

et al. 1998

Meteorological Applications

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“…The reality and significance of such changes in the historical past have already been demonstrated (see, e.g., Weare and Hoeschele, 1983;Wilks, 1989;Schubert, 1994;Wilby, 1994). For the future, the predictability of the changes is still an open question.…”

Section: Regression Methodsmentioning

confidence: 99%

Downscaling general circulation model output: a review of methods and limitations

Wilby

Wigley

1997

Progress in Physical Geography: Earth and Environment

1,251

819

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General circulation models (GCMs) suggest that rising concentrations of greenhouse gases may have significant consequences for the global climate. What is less clear is the extent to which local (subgrid) scale meteorological processes will be affected. So-called 'downscaling' techniques have subsequently emerged as a means of bridging the gap between what climate modellers are currently able to provide and what impact assessors require. This article reviews the present generation of downscaling tools under four main headings: regression methods; weather pattern (circulation)-based approaches; stochastic weather generators; and limited-area climate models. The penultimate section summarizes the results of an international experiment to intercompare several precipitation models used for downscaling. It shows that circulation-based downscaling methods perform well in simulating present observed and model-generated daily precipitation characteristics, but are able to capture only part of the daily precipitation variability changes associated with model-derived changes in climate. The final section examines a number of ongoing challenges to the future development of climate downscaling. I The rationale for downscalingEven if global climate models in the future are run at high resolution there will remain the need to 'downscale' the results from such models to individual sites or localities for impact studies. Downscaling methodologies are still under development and more work needs to be done in intercomparing these methodologies and quantifying the accuracy of such methods (DOE, 1996: 34).The present generation of general circulation models (GCMs) of the climate system are restricted in their usefulness for many subgrid scale applications by their coarse spatial Source: Modified after Hostetler (1994) and temporal resolution (Wigley et al., 1990;Carter et al., 1994). For example, hydrological models are frequently concerned with small, subcatchment (even hillslope) scale processes, occurring on spatial scales much smaller than those resolved in GCMs (see Figure 1). GCMs deal most proficiently with fluid dynamics at the continental scale and parameterize regional and smaller-scale processes. These scale-related sensitivities and mismatch problems are further exacerbated because they usually involve the most uncertain components of climate models, water vapour and cloud feedback effects (Rind et al., 1992). As Hostetler (1994) has observed, the greatest errors in the parameterizations of both GCMs and hydrological models occur on the scale(s) at which climate and terrestrial impact models interface. These mismatch problems, which affect both the temporal and spatial dimensions, have important implications for the credence of impact studies derived by the output of models of climate change, especially as research into potential human-induced modifications to hydrological and ecological cycles is assuming increasing significance. Downloaded from 532 change. A major focus of the BAHC (Biological Aspects of the Hydrol...

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Specification of Monthly Precipitation in the Western United States from Monthly Mean Circulation

Cited by 25 publications

References 0 publications

Obtaining sub‐grid‐scale information from coarse‐resolution general circulation model output

Obtaining sub‐grid‐scale information from coarse‐resolution general circulation model output

Conditioning stochastic properties of daily precipitation on indices of atmospheric circulation

Downscaling general circulation model output: a review of methods and limitations

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