ENSO Teleconnection Pattern Changes over the Southeastern United States under a Climate Change Scenario in CMIP5 Models

A strong teleconnection exists between the sea surface temperature (SST) over the tropical Pacific and the winter precipitation in the southeastern United States (SE US).This feature is adopted to validate the fidelity of Coupled Model Intercomparison Project Phase 5 (CMIP5) in this study. In addition, the authors examine whether the teleconnection pattern persists in the future under a global warming scenario. Generally, most of the eight selected models show a positive correlation between November SST over Ni˜no 3 region and December–February (DJF) mean daily precipitation anomalies over the SE US, consistent with the observation. However, the models with poor realization of skewness of Ni˜no indices fail to simulate the realistic teleconnection pattern in the historical simulation. In the Representative Concentration Pathways 8.5 (RCP8.5) run, all of the models maintain positive and slightly increased correlation patterns. It is noteworthy that the region with strong teleconnection pattern shifts northward in the future. Increased variance of winter precipitation due to the SST teleconnection is shown over Alabama and Georgia rather than over Florida under the RCP8.5 scenario in most of themodels, differing fromthe historical run in which the precipitation in Florida is the most attributable to the eastern Pacific SST

ENSO Teleconnection Pattern Changes over the Southeastern United States under a Climate Change Scenario in CMIP5 Models

A strong teleconnection exists between the sea surface temperature (SST) over the tropical Pacific and the winter precipitation in the southeastern United States (SE US).This feature is adopted to validate the fidelity of Coupled Model Intercomparison Project Phase 5 (CMIP5) in this study. In addition, the authors examine whether the teleconnection pattern persists in the future under a global warming scenario. Generally, most of the eight selected models show a positive correlation between November SST over Ni˜no 3 region and December–February (DJF) mean daily precipitation anomalies over the SE US, consistent with the observation. However, the models with poor realization of skewness of Ni˜no indices fail to simulate the realistic teleconnection pattern in the historical simulation. In the Representative Concentration Pathways 8.5 (RCP8.5) run, all of the models maintain positive and slightly increased correlation patterns. It is noteworthy that the region with strong teleconnection pattern shifts northward in the future. Increased variance of winter precipitation due to the SST teleconnection is shown over Alabama and Georgia rather than over Florida under the RCP8.5 scenario in most of themodels, differing fromthe historical run in which the precipitation in Florida is the most attributable to the eastern Pacific SST

Simulation of the Indian Summer Monsoon Using Comprehensive Atmosphere-land Interactions, in the Absence of Two-way Air-sea Interactions

Community Land Model version 2 (CLM2) as a comprehensive land surface model and a simple land surface model (SLM) were coupled to an atmospheric climate model to investigate the role of land surface processes in the development and the persistence of the South Asian summer monsoon. Two-way air-sea interactions were not considered in order to identify the reproducibility of the monsoon evolution by the comprehensive land model, which includes more realistic vertical soil moisture structures, vegetation and 2-way atmosphere-land interactions at hourly intervals. In the monsoon development phase (May and June). comprehensive land-surface treatment improves the representation of atmospheric circulations and the resulting convergence/divergence through the improvements in differential heating patterns and surface energy fluxes. Coupling with CLM2 also improves the timing and spatial distribution of rainfall maxima, reducing the seasonal rainfall overestimation by approx.60 % (1.8 mm/d for SLM, 0.7 mm/dI for CLM2). As for the interannual variation of the simulated rainfall, correlation coefficients of the Indian seasonal rainfall with observation increased from 0.21 (SLM) to 0.45 (CLM2). However, in the mature monsoon phase (July to September), coupling with the CLM2 does not exhibit a clear improvement. In contrast to the development phase, latent heat flux is underestimated and sensible heat flux and surface temperature over India are markedly overestimated. In addition, the moisture fluxes do not correlate well with lower-level atmospheric convergence, yielding correlation coefficients and root mean square errors worse than those produced by coupling with the SLM. A more realistic representation of the surface temperature and energy fluxes is needed to achieve an improved simulation for the mature monsoon period

Dynamically and Statistically Downscaled Seasonal Simulations of Maximum Surface Air Temperature Over the Southeastern United States

Coarsely resolved surface air temperature (2 m height) seasonal integrations from the Florida State University/Center for Ocean-Atmospheric Prediction Studies Global Spectral Model (FSU/COAPS GSM) (~1.8º lon.-lat. (T63)) for the period of 1994 to 2002 (March through September each year) are downscaled to a fine spatial scale of ~20 km. Dynamical and statistical downscaling methods are applied for the southeastern United States region, covering Florida, Georgia, and Alabama. Dynamical downscaling is conducted by running the FSU/COAPS Nested Regional Spectral Model (NRSM), which is nested into the domain of the FSU/COAPS GSM. We additionally present a new statistical downscaling method. The rationale for the statistical approach is that clearer separation of prominent climate signals (e.g., seasonal cycle, intraseasonal, or interannual oscillations) in observation and GSM, respectively, over the training period can facilitate the identification of the statistical relationship in climate variability between two data sets. Cyclostationary Empirical Orthogonal Function (CSEOF) analysis and multiple regressions are trained with those data sets to extract their statistical relationship, which eventually leads to better prediction of regional climate from the large-scale simulations. Downscaled temperatures are compared with the FSU/COAPS GSM fields and observations. Downscaled seasonal anomalies exhibit strong agreement with observations and a reduction in bias relative to the direct GSM simulations. Interannual temperature change is also reasonably simulated at local grid points. A series of evaluations including mean absolute errors, anomaly correlations, frequency of extreme events, and categorical predictability reveal that both downscaling techniques can be reliably used for numerous seasonal climate applications