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Sea surface temperature (SST) variability has been shown to have predictive value for land precipitation, although SSTs are unable to fully predict intraseasonal to interannual hydrologic extremes. The possible remote effects of large-scale land surface temperature (LST) and subsurface temperature (SUBT) anomalies in geographical areas upstream and closer to the areas of drought/flood have largely been ignored. Here evidence from climate observations and model simulations addresses these effects. Evaluation of observational data using Maximum Covariance Analysis identifies significant correlations between springtime 2-m air temperature (T2 m) cold (warm) anomalies in both the western U.S. and the Tibetan Plateau and downstream drought (flood) events in late spring/summer. To support these observational findings, climate models are used in sensitivity studies, in which initial LST/SUBT anomaly is imposed to produce observed T2 m anomaly, to demonstrate a causal relationship for two important cases: between spring warm T2 m/LST/SUBT anomalies in western U.S. and the extraordinary 2015 flood in Southern Great Plains and adjacent regions and between spring cold T2 m/LST/SUBT anomalies in the Tibetan Plateau and the severe 2003 drought south of the Yangtze River region. The LST/SUBT downstream effects in North America are associated with a large-scale atmospheric stationary wave extending eastward from the LST/SUBT anomaly region. The effects of SST in these cases are also tested and compared with the LST/SUBT effects. These results suggest that consideration of LST/SUBT anomalies has the potential to add value to intraseasonal prediction of dry and wet conditions, especially extreme drought/flood events. The results suggest the importance of developing land data and models capable of preserving observed soil memory.

Regional climate models (RCMs) have been developed and extensively applied for dynamically downscaling coarse resolution information from different sources, such as general circulation models (GCMs) and reanalyses, for different purposes including past climate simulations and future climate projection. Thus far, the nature, the methods, and a number of crucial issues concerning the use of dynamic downscaling are still not well understood. The most important issue is whether, and if so, under what conditions dynamic downscaling is really capable of adding more information at different scales compared to the GCM or reanalysis that imposes lateral boundary conditions (LBCs) to the RCMs. There are controversies regarding the downscaling ability. In this review paper we present several factors that have consistently demonstrated strong impact on dynamic downscaling ability in intraseasonal and seasonal simulations/predictions and future projection. Those factors include setting of the RCM experiment (e.g. imposed LBC quality, domain size and position, LBC coupling, and horizontal resolution); as well as physical processes, mainly convective schemes and vegetation and soil processes that include initializations, vegetation specifications, and planetary boundary layer and surface coupling. These studies indicate that RCMs have downscaling ability in some aspects but only under certain conditions. Any significant weaknesses in one of these aspects would cause an RCM to lose its dynamic downscaling ability. This paper also briefly presents challenges faced in current RCM dynamic downscaling and future prospective, which cover the application of coupled ocean-atmosphere RCMs, ensemble applications, and future projections. © 2014 Elsevier B.V.

This study investigates the capability of the dynamic downscaling method (DDM) in a North American regional climate study using the Eta/Simplified Simple Biosphere (SSiB) Regional Climate Model (RCM). The main objective is to understand whether the Eta/SSiB RCM is capable of simulating North American regional climate features, mainly precipitation, at different scales under imposed boundary conditions. The summer of 1998 was selected for this study and the summers of 1993 and 1995 were used to confirm the 1998 results. The observed precipitation, NCEP–NCAR Global Reanalysis (NNGR), and North American Regional Reanalysis (NARR) were used for evaluation of the model’s simulations and/or as lateral boundary conditions (LBCs). A spectral analysis was applied to quantitatively examine the RCM’s downscaling ability at different scales. The simulations indicated that choice of domain size, LBCs, and grid spacing were crucial for the DDM. Several tests with different domain sizes indicated that the model in the North American climate simulation was particularly sensitive to its southern boundary position because of the importance of moisture transport by the southerly low-level jet (LLJ) in summer precipitation. Among these tests, only the RCM with 32-km resolution and NNGR LBC or with 80-km resolution and NARR LBC, in conjunction with appropriate domain sizes, was able to properly simulate precipitation and other atmospheric variables—especially humidity over the southeastern United States—during all three summer months—and produce a better spectral power distribution than that associated with the imposed LBC (for the 32-km case) and retain spectral power for large wavelengths (for the 80-km case). The analysis suggests that there might be strong atmospheric components of high-frequency variability over the Gulf of Mexico and the southeastern United States. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) have been developed and extensively applied for dynamically downscaling coarse resolution information from different sources, such as general circulation models (GCMs) and reanalyses, for different purposes including past climate simulations and future climate projection. Thus far, the nature, the methods, and a number of crucial issues concerning the use of dynamic downscaling are still not well understood. The most important issue is whether, and if so, under what conditions dynamic downscaling is really capable of adding more information at different scales compared to the GCM or reanalysis that imposes lateral boundary conditions (LBCs) to the RCMs. There are controversies regarding the downscaling ability. In this review paper we present several factors that have consistently demonstrated strong impact on dynamic downscaling ability in intraseasonal and seasonal simulations/predictions and future projection. Those factors include setting of the RCM experiment (e.g. imposed LBC quality, domain size and position, LBC coupling, and horizontal resolution); as well as physical processes, mainly convective schemes and vegetation and soil processes that include initializations, vegetation specifications, and planetary boundary layer and surface coupling. These studies indicate that RCMs have downscaling ability in some aspects but only under certain conditions. Any significant weaknesses in one of these aspects would cause an RCM to lose its dynamic downscaling ability. This paper also briefly presents challenges faced in current RCM dynamic downscaling and future prospective, which cover the application of coupled ocean-atmosphere RCMs, ensemble applications, and future projections. [ABSTRACT FROM AUTHOR]