Chul-Su Shin

The heated condensation framework as a convective trigger in the NCEP Climate Forecast System version 2

An updated version of the Heated Condensation Framework (HCF) is implemented as a convective triggering criterion into the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). The new trigger replaces the original criteria in both the deep (Simplified Arakawa-Schubert – SAS) and shallow (SAS based) convective schemes. The performance of the original and new triggering criteria is first compared against radiosonde observations. Then, a series of hindcasts are performed to evaluate the influence of the triggering criterion in the CFSv2 representation of summer precipitation, the diurnal cycle of precipitation, and hurricanes that made landfall. The observational analysis shows that the HCF trigger better captures the frequency of convection, where the original SAS trigger initiates convection too often. When implemented in CFSv2, the HCF trigger improves the seasonal forecast of the Indian summer monsoon rainfall, including the representation of the onset dates of the rainy season over India. On the other hand, the HCF trigger increases error in the seasonal forecast of precipitation over the eastern United States. The HCF trigger also improves the representation of the intensity of hurricanes. Moreover, the simulation of hurricanes provides insights on the mechanism whereby the HCF trigger impacts the representation of convection.

Slow and fast annual cycles of the Asian summer monsoon in the NCEP CFSv2

Abstract The climatological Asian summer monsoon (ASM) is decomposed into the slow and fast annual cycles (SAC and FAC). The FAC represents the abrupt onset and breaks phase-locked to the ASM seasonal progression. This study evaluates how well the NCEP Climate Forecast System version 2 (CFSv2) simulates the SAC and FAC over the Indian and East Asia monsoon regions (IMR and EAMR). The simulated SACs are in good agreement with observations in both regions. The FAC also represents the northward propagation in both observations and CFSv2. It is further demonstrated that the FAC is associated with a thermodynamic air–sea interaction. In particular, the different roles played by the wind-evaporation-SST (WES) feedback may account for the faster propagation in the IMR than the EAMR. However, compared with observations, the simulated FAC shows earlier monsoon onset and long-lasting stronger dry and wet phases in the IMR but delayed monsoon onset with weaker and less organized FAC in the EAMR. These reversed behaviors may originate from a warm (cold) SST bias in the IMR (EAMR) in boreal spring and enhanced by an overly sensitive surface evaporation to wind changes in the CFSv2. As a result, the warm spring SST bias in the IMR initiates a strong WES feedback and changes of solar insolation during boreal summer, which leads to a cold SST bias in early fall. On the other hand, the cold spring SST bias in the EAMR accounts for a weaker air–sea coupling, which in turn results in a warm SST bias after the withdrawal of the monsoon.

Verification of Land–Atmosphere Coupling in Forecast Models, Reanalyses, and Land Surface Models Using Flux Site Observations

We confront four model systems in three configurations (LSM, LSM+GCM, and reanalysis) with global flux tower observations to validate states, surface fluxes, and coupling indices between land and atmosphere. Models clearly under-represent the feedback of surface fluxes on boundary layer properties (the atmospheric leg of land-atmosphere coupling), and may over-represent the connection between soil moisture and surface fluxes (the terrestrial leg). Models generally under-represent spatial and temporal variability relative to observations, which is at least partially an artifact of the differences in spatial scale between model grid boxes and flux tower footprints. All models bias high in near-surface humidity and downward shortwave radiation, struggle to represent precipitation accurately, and show serious problems in reproducing surface albedos. These errors create challenges for models to partition surface energy properly and errors are traceable through the surface energy and water cycles. The spatial distribution of the amplitude and phase of annual cycles (first harmonic) are generally well reproduced, but the biases in means tend to reflect in these amplitudes. Interannual variability is also a challenge for models to reproduce. Our analysis illuminates targets for coupled land-atmosphere model development, as well as the value of long-term globally-distributed observational monitoring.

Reforecasting the ENSO Events in the Past 57 Years (1958–2014)

AbstractA set of ensemble seasonal reforecasts for 1958–2014 is conducted using the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2. In comparison with other current reforecasts, this dataset extends the seasonal reforecasts to the 1960s–70s. Direct comparison of the predictability of the ENSO events occurring during the 1960s–70s with the more widely studied ENSO events since then demonstrates the seasonal forecast system’s capability in different phases of multidecadal variability and degrees of global climate change. A major concern for a long reforecast is whether the seasonal reforecasts before 1979 provide useful skill when observations, particularly of the ocean, were sparser. This study demonstrates that, although the reforecasts have lower skill in predicting SST anomalies in the North Pacific and North Atlantic before 1979, the prediction skill of the onset and development of ENSO events in 1958–78 is comparable to that for 1979–2014. In particular, the EN...

A spurious warming trend in the NMME equatorial Pacific SST hindcasts

Using seasonal hindcasts of six different models participating in the North American Multimodel Ensemble project, the trend of the predicted sea surface temperature (SST) in the tropical Pacific for 1982–2014 at each lead month and its temporal evolution with respect to the lead month are investigated for all individual models. Since the coupled models are initialized with the observed ocean, atmosphere, land states from observation-based reanalysis, some of them using their own data assimilation process, one would expect that the observed SST trend is reasonably well captured in their seasonal predictions. However, although the observed SST features a weak-cooling trend for the 33-year period with La Niña-like spatial pattern in the tropical central-eastern Pacific all year round, it is demonstrated that all models having a time-dependent realistic concentration of greenhouse gases (GHG) display a warming trend in the equatorial Pacific that amplifies as the lead-time increases. In addition, these models’ behaviors are nearly independent of the starting month of the hindcasts although the growth rates of the trend vary with the lead month. This key characteristic of the forecasted SST trend in the equatorial Pacific is also identified in the NCAR CCSM3 hindcasts that have the GHG concentration for a fixed year. This suggests that a global warming forcing may not play a significant role in generating the spurious warming trend of the coupled models’ SST hindcasts in the tropical Pacific. This model SST trend in the tropical central-eastern Pacific, which is opposite to the observed one, causes a developing El Niño-like warming bias in the forecasted SST with its peak in boreal winter. Its implications for seasonal prediction are discussed.