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Huanping Huang
,
Jonathan M. Winter
,
Erich C. Osterberg
,
Radley M. Horton
, and
Brian Beckage

Abstract

The northeastern United States has experienced a large increase in precipitation over recent decades. Annual and seasonal changes of total and extreme precipitation from station observations in the Northeast were assessed over multiple time periods spanning 1901–2014. Spatially averaged, both annual total and extreme precipitation across the Northeast increased significantly since 1901, with changepoints occurring in 2002 and 1996, respectively. Annual extreme precipitation experienced a larger increase than total precipitation; extreme precipitation from 1996 to 2014 is 53% higher than from 1901 to 1995. Spatially, coastal areas receive more total and extreme precipitation on average, but increases across the changepoints are distributed fairly uniformly across the domain. Increases in annual total precipitation across the 2002 changepoint are driven by significant total precipitation increases in fall and summer, while increases in annual extreme precipitation across the 1996 changepoint are driven by significant extreme precipitation increases in fall and spring. The ability of gridded observed and reanalysis precipitation data to reproduce station observations was also evaluated. Gridded observations perform well in reproducing averages and trends of annual and seasonal total precipitation, but extreme precipitation trends show significantly different spatial and domain-averaged trends than station data. The North American Regional Reanalysis generally underestimates annual and seasonal total and extreme precipitation means and trends relative to station observations, and also shows substantial differences in the spatial pattern of total and extreme precipitation trends within the Northeast.

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Jonathan M. Winter
,
Huanping Huang
,
Erich C. Osterberg
, and
Justin S. Mankin
Free access
Janel Hanrahan
,
Jessica Langlois
,
Lauren Cornell
,
Huanping Huang
,
Jonathan M. Winter
,
Patrick J. Clemins
,
Brian Beckage
, and
Cindy Bruyère

Abstract

Most inland water bodies are not resolved by general circulation models, requiring that lake surface temperatures be estimated. Given the large spatial and temporal variability of the surface temperatures of the North American Great Lakes, such estimations can introduce errors when used as lower boundary conditions for dynamical downscaling. Lake surface temperatures (LSTs) influence moisture and heat fluxes, thus impacting precipitation within the immediate region and potentially in regions downwind of the lakes. For this study, the Advanced Research version of the Weather Research and Forecasting Model (WRF-ARW) was used to simulate precipitation over the six New England states during a 5-yr historical period. The model simulation was repeated with perturbed LSTs, ranging from 10°C below to 10°C above baseline values obtained from reanalysis data, to determine whether the inclusion of erroneous LST values has an impact on simulated precipitation and synoptic-scale features. Results show that simulated precipitation in New England is statistically correlated with LST perturbations, but this region falls on a wet–dry line of a larger bimodal distribution. Wetter conditions occur to the north and drier conditions occur to the south with increasing LSTs, particularly during the warm season. The precipitation differences coincide with large-scale anomalous temperature, pressure, and moisture patterns. Care must therefore be taken to ensure reasonably accurate Great Lakes surface temperatures when simulating precipitation, especially in southeastern Canada, Maine, and the mid-Atlantic region.

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