ALBERT

Description: An assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Niño–Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible causes of model biases generated from simulated large-scale climate conditions, are documented in the paper. The model experiments are carried out by the Hurricane Work Group under the U.S. Climate Variability and Predictability Research Program (CLIVAR) using five global climate models (GCMs) with a total of 16 ensemble members forced by the observed sea surface temperature and spanning the 28-yr period from 1982 to 2009. The results show GISS and GFDL model ensemble means best simulate the interannual variability of TCs, and the multimodel ensemble mean (MME) follows. Also, the MME has the closest climate mean annual number of WNP TCs and the smallest root-mean-square error to the observation. Most GCMs can simulate the interannual variability of WNP TCs well, with stronger TC activities during two types of El Niño—namely, eastern Pacific (EP) and central Pacific (CP) El Niño—and weaker activity during La Niña. However, none of the models capture the differences in TC activity between EP and CP El Niño as are shown in observations. The inability of models to distinguish the differences in TC activities between the two types of El Niño events may be due to the bias of the models in response to the shift of tropical heating associated with CP El Niño.

Description: Composites based on observations and model outputs from the Climate Variability and Predictability (CLIVAR) drought experiments were used to examine the impact of El Niño–Southern Oscillation (ENSO) and the Atlantic multidecadal oscillation (AMO) on drought over the United States. Because drought implies persistent dryness, the 6-month standardized precipitation index, standardized runoff index, and soil moisture anomalies are used to represent drought. The experiments were performed by forcing an AGCM with prescribed sea surface temperature anomalies (SSTAs) superimposed on the monthly mean SST climatology. Four model outputs from the NCEP Global Forecast System (GFS), NASA’s Seasonal-to-Interannual Prediction Project, version 1 (NSIPP1), GFDL’s global atmospheric model, version 2.1 (AM2.1), and the Lamont-Doherty Earth Observatory (LDEO)/NCAR Community Climate System Model, version 3 (CCM3) were analyzed in this study. Each run lasts from 36 to 51 yr. The impact of ENSO on drought over the United States is concentrated over the Southwest, the Great Plains, and the lower Colorado River basin, with cold (warm) ENSO events favoring drought (wet spells). Over the East Coast and the Southeast, the impact of ENSO is small because the precipitation responses to ENSO are opposite in sign for winter and summer. For these areas, a prolonged ENSO does not always favor either drought or wet spells. The direct influence of the AMO on drought is small. The major influence of the AMO is to modulate the impact of ENSO on drought. The influence is large when the SSTAs in the tropical Pacific and in the North Atlantic are opposite in phase. A cold (warm) event in a positive (negative) AMO phase amplifies the impact of the cold (warm) ENSO on drought. The ENSO influence on drought is much weaker when the SSTAs in the tropical Pacific and in the North Atlantic are in phase.

Description: This study examines the sensitivity of the North American warm season diurnal cycle of precipitation to changes in horizontal resolution in three atmospheric general circulation models, with a primary focus on how the parameterized moist processes respond to improved resolution of topography and associated local/regional circulations on the diurnal time scale. It is found that increasing resolution (from approximately 2° to ½° in latitude–longitude) has a mixed impact on the simulated diurnal cycle of precipitation. Higher resolution generally improves the initiation and downslope propagation of moist convection over the Rockies and the adjacent Great Plains. The propagating signals, however, do not extend beyond the slope region, thereby likely contributing to a dry bias in the Great Plains. Similar improvements in the propagating signals are also found in the diurnal cycle over the North American monsoon region as the models begin to resolve the Gulf of California and the surrounding steep terrain. In general, the phase of the diurnal cycle of precipitation improves with increasing resolution, though not always monotonically. Nevertheless, large errors in both the phase and amplitude of the diurnal cycle in precipitation remain even at the highest resolution considered here. These errors tend to be associated with unrealistically strong coupling of the convection to the surface heating and suggest that improved simulations of the diurnal cycle of precipitation require further improvements in the parameterizations of moist convection processes.

Description: A hybrid dynamical–statistical model is developed for predicting Atlantic seasonal hurricane activity. The model is built upon the empirical relationship between the observed interannual variability of hurricanes and the variability of sea surface temperatures (SSTs) and vertical wind shear in 26-yr (1981–2006) hindcasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). The number of Atlantic hurricanes exhibits large year-to-year fluctuations and an upward trend over the 26 yr. The latter is characterized by an inactive period prior to 1995 and an active period afterward. The interannual variability of the Atlantic hurricanes significantly correlates with the CFS hindcasts for August–October (ASO) SSTs and vertical wind shear in the tropical Pacific and tropical North Atlantic where CFS also displays skillful forecasts for the two variables. In contrast, the hurricane trend shows less of a correlation to the CFS-predicted SSTs and vertical wind shear in the two tropical regions. Instead, it strongly correlates with observed preseason SSTs in the far North Atlantic. Based on these results, three potential predictors for the interannual variation of seasonal hurricane activity are constructed by averaging SSTs over the tropical Pacific (TPCF; 5°S–5°N, 170°E–130°W) and the Atlantic hurricane main development region (MDR; 10°–20°N, 20°–80°W), respectively, and vertical wind shear over the MDR, all of which are from the CFS dynamical forecasts for the ASO season. In addition, two methodologies are proposed to better represent the long-term trend in the number of hurricanes. One is the use of observed preseason SSTs in the North Atlantic (NATL; 55°–65°N, 30°–60°W) as a predictor for the hurricane trend, and the other is the use of a step function that breaks up the hurricane climatology into a generally inactive period (1981–94) and a very active period (1995–2006). The combination of the three predictors for the interannual variation, along with the two methodologies for the trend, is explored in developing an empirical forecast system for Atlantic hurricanes. A cross validation of the hindcasts for the 1981–2006 hurricane seasons suggests that the seasonal hurricane forecast with the TPCF SST as the only CFS predictor is more skillful in inactive hurricane seasons, while the forecast with only the MDR SST is more skillful in active seasons. The forecast using both predictors gives better results. The most skillful forecast uses the MDR vertical wind shear as the only CFS predictor. A comparison with forecasts made by other statistical models over the 2002–07 seasons indicates that this hybrid dynamical–statistical forecast model is competitive with the current statistical forecast models.

Description: The variability of Atlantic tropical cyclones (TCs) associated with El Niño–Southern Oscillation (ENSO) in model simulations is assessed and compared with observations. The model experiments are 28-yr simulations forced with the observed sea surface temperature from 1982 to 2009. The simulations were coordinated by the U.S. Climate Variability and Predictability Research Program (CLIVAR) Hurricane Working Group and conducted with five global climate models (GCMs) with a total of 16 ensemble members. The model performance is evaluated based on both individual model ensemble means and multimodel ensemble mean. The latter has the highest anomaly correlation (0.86) for the interannual variability of TCs. Previous observational studies show a strong association between ENSO and Atlantic TC activity, as well as distinctions during eastern Pacific (EP) and central Pacific (CP) El Niño events. The analysis of track density and TC origin indicates that each model has different mean biases. Overall, the GCMs simulate the variability of Atlantic TCs well with weaker activity during EP El Niño and stronger activity during La Niña. For CP El Niño, there is a slight increase in the number of TCs as compared with EP El Niño. However, the spatial distribution of track density and TC origin is less consistent among the models. Particularly, there is no indication of increasing TC activity over the U.S. southeast coastal region during CP El Niño as in observations. The difference between the models and observations is likely due to the bias of the models in response to the shift of tropical heating associated with CP El Niño, as well as the model bias in the mean circulation.

Description: This work examines the performance of Madden–Julian oscillation (MJO) forecasts from NCEP’s coupled and uncoupled general circulation models (GCMs) and statistical models. The forecast skill from these methods is evaluated in near–real time. Using a projection of El Niño–Southern Oscillation (ENSO)-removed variables onto the principal patterns of MJO convection and upper- and lower-level circulations, MJO-related signals in the dynamical model forecasts are extracted. The operational NCEP atmosphere–ocean fully coupled Climate Forecast System (CFS) model has useful skill (〉0.5 correlation) out to ∼15 days when the initial MJO convection is located over the Indian Ocean. The skill of the CFS hindcast dataset for the period from 1995 to 2004 is nearly comparable to that from a lagged multiple linear regression model, which uses information from the previous five pentads of the leading two principal components (PCs). In contrast, the real-time analysis for the MJO forecast skill for the period from January 2005 to February 2006 using the lagged multiple linear regression model is reduced to ∼10–12 days. However, the operational CFS forecast for this period is skillful out to ∼17 days for the winter season, implying that the coupled dynamical forecast has some usefulness in predicting the MJO compared to the statistical model. It is shown that the coupled CFS model consistently, but only slightly, outperforms the uncoupled atmospheric model (by one to two days), indicating that only limited improvement is gained from the inclusion of the coupled air–sea interaction in the MJO forecast in this model. This slight improvement may be the result of the existence of a propagation barrier around the Maritime Continent and the far western Pacific in the NCEP Global Forecast System (GFS) and CFS models, as shown in several previous studies. This work also suggests that the higher horizontal resolution and finer initial data might contribute to improving the forecast skill, presumably as a result of an enhanced representation of the Maritime Continent region.

Description: Summer seasonal simulations for selected years were performed using the NCEP Global Forecast System (GFS) at high (T126L28) and low (T62L28) resolutions, and the NCEP 80-km regional spectral model (RSM) nested in the T62 model outputs (RSM80/T62). All models have 28 levels in the vertical. All experiments were performed with prescribed observed sea surface temperatures to ensure that simulation errors came from model deficiencies. While the T126L28 model does not have a statistically significant advantage in simulating 500-hPa height anomalies over the Pacific–North American domain, it yields better monsoon precipitation forecasts and interannual variability. The T62L28 model simulations are too dry over the Southwest and northwestern Mexico when compared to observations and do not properly capture interannual variations of monsoon rainfall. The RSM80/T62 nesting improves the overall rainfall simulations somewhat but is not able to overcome deficiencies of the T62L28 global model to capture interannual variations in monsoon precipitation. Results indicate that a high-resolution version of the global model is needed for seasonal forecasts of monsoon precipitation. Both models capture the low-level jet from the Great Plains (GPLLJ) and rainfall anomalies associated with the 1993 summer floods and the 1988 summer drought, although the simulated rainfall maxima are often weaker and shifted spatially when compared to observations. The impact of horizontal resolution is largely local and is limited to areas over the western region of North America. The T126 model is able to capture the low-level jet from the Gulf of California (GCLLJ), while the T62 model is too coarse to resolve the Gulf of California (GOC). Moisture surges along the GOC are not properly simulated by the T62 model. Overall, the T62 model simulates a very dry Southwest and a weaker monsoon.

Description: Global climate models are challenged to represent the North American monsoon, in terms of its climatology and interannual variability. To investigate whether a regional atmospheric model can improve warm season forecasts in North America, a retrospective Climate Forecast System (CFS) model reforecast (1982–2000) and the corresponding NCEP–NCAR reanalysis are dynamically downscaled with the Weather Research and Forecasting model (WRF), with similar parameterization options as used for high-resolution numerical weather prediction and a new spectral nudging capability. The regional model improves the climatological representation of monsoon precipitation because of its more realistic representation of the diurnal cycle of convection. However, it is challenged to capture organized, propagating convection at a distance from terrain, regardless of the boundary forcing data used. Dynamical downscaling of CFS generally yields modest improvement in surface temperature and precipitation anomaly correlations in those regions where it is already positive in the global model. For the North American monsoon region, WRF adds value to the seasonally forecast temperature only in early summer and does not add value to the seasonally forecast precipitation. CFS has a greater ability to represent the large-scale atmospheric circulation in early summer because of the influence of Pacific SST forcing. The temperature and precipitation anomaly correlations in both the global and regional model are thus relatively higher in early summer than late summer. As the dominant modes of early warm season precipitation are better represented in the regional model, given reasonable large-scale atmospheric forcing, dynamical downscaling will add value to warm season seasonal forecasts. CFS performance appears to be inconsistent in this regard.