Contribution of the North Atlantic subtropical high to regional climate model (RCM) skill in simulating southeastern United States summer precipitation

Abstract

This study assesses the skill of advanced regional climate models (RCMs) in simulating southeastern United States (SE US) summer precipitation and explores the physical mechanisms responsible for the simulation skill at a process level. Analysis of the RCM output for the North American Regional Climate Change Assessment Program indicates that the RCM simulations of summer precipitation show the largest biases and a remarkable spread over the SE US compared to other regions in the contiguous US. The causes of such a spread are investigated by performing simulations using the Weather Research and Forecasting (WRF) model, a next-generation RCM developed by the US National Center for Atmospheric Research. The results show that the simulated biases in SE US summer precipitation are due mainly to the misrepresentation of the modeled North Atlantic subtropical high (NASH) western ridge. In the WRF simulations, the NASH western ridge shifts 7° northwestward when compared to that in the reanalysis ensemble, leading to a dry bias in the simulated summer precipitation according to the relationship between the NASH western ridge and summer precipitation over the southeast. Experiments utilizing the four dimensional data assimilation technique further suggest that the improved representation of the circulation patterns (i.e., wind fields) associated with the NASH western ridge substantially reduces the bias in the simulated SE US summer precipitation. Our analysis of circulation dynamics indicates that the NASH western ridge in the WRF simulations is significantly influenced by the simulated planetary boundary layer (PBL) processes over the Gulf of Mexico. Specifically, a decrease (increase) in the simulated PBL height tends to stabilize (destabilize) the lower troposphere over the Gulf of Mexico, and thus inhibits (favors) the onset and/or development of convection. Such changes in tropical convection induce a tropical–extratropical teleconnection pattern, which modulates the circulation along the NASH western ridge in the WRF simulations and contributes to the modeled precipitation biases over the SE US. In conclusion, our study demonstrates that the NASH western ridge is an important factor responsible for the RCM skill in simulating SE US summer precipitation. Furthermore, the improvements in the PBL parameterizations for the Gulf of Mexico might help advance RCM skill in representing the NASH western ridge circulation and summer precipitation over the SE US.

Appendix: Pattern recognition algorithm and its application to selecting a sample simulation period

To select a simulation period representative of SE US summer precipitation climatology, an optimization algorithm is designed. The procedure of the algorithm is as follows:

1st: select the years when the areal-averaged SE US summer precipitation anomaly is within one standard deviation of the 1979–2010 sample (Fig. 11a). As shown in Fig. 11a, 22 out of 32 summers fulfill this criterion.

Fig. 11

a Time series of SE US summer precipitation anomalies during 1979–2010; b pattern correlation (solid blue curve, left axis) and root mean square error (RMSE) (dashed red curve, right axis) between each individual year’s precipitation and the 1979–2010 precipitation climatology. In a the black (gray) bars represent the years when the precipitation anomaly exceeds (within) one standard deviation of the 32-year precipitation; the dashed lines in a denote one standard deviation of precipitation, and the line in b denotes the year 2001

2nd: calculate the pattern correlation coefficient (PCC) and root mean square error (RMSE) between precipitation in each individual summer and the 32-year precipitation climatology (Fig. 11b). The PCC and RMSE are calculated as:

$$PCC = \frac{{\frac{1}{N - 1}\left\{ {\mathop \sum \nolimits_{i = 1}^{N} \left[ {\left( {x_{i} - \overline{x} } \right)\left( {y_{i} - \overline{y} } \right)} \right]} \right\}}}{{\left\{ {\frac{1}{N - 1}\left[ {\mathop \sum \nolimits_{i = 1}^{N} \left( {x_{i} - \overline{x} } \right)^{2} } \right]} \right\}^{\frac{1}{2}} \left\{ {\frac{1}{N - 1}\left[ {\mathop \sum \nolimits_{i = 1}^{N} \left( {y_{i} - \overline{y} } \right)^{2} } \right]} \right\}^{\frac{1}{2}} }}$$

(1)

$$RMSE = \left[ {\frac{1}{N}\sum\limits_{i = 1}^{N} {\left( {x_{i} - y_{i} } \right)^{2} } } \right]^{\frac{1}{2}}$$

(2)

where x represents precipitation in a specific summer, and y represents the 1979–2010 summer precipitation climatology.

3rd: rank the PCCs (RMSEs) from high to low (low to high). Here, only the years that fulfill the criterion in the first step are considered. The final rank for each summer period is calculated by adding the PCC and RMSE ranks. The years with the highest combined rank are selected as the simulation period.

According to the algorithm, the summer of 2001 is selected, because the precipitation anomaly is within one standard deviation (Fig. 11a) and the combined rank is the highest (Fig. 11b). Furthermore, the results are not sensitive to the choice of simulation period, according to our analysis of the WRF output for NARCCAP.