Steven M. DiNapoli

Understanding the wet season variations over Florida

The wet season of Florida is well defined and is invariably centered in the boreal summer season of June–July–August. In this observational study we objectively define the Length of the Wet Season (LOWS) for Florida and examine its variations with respect to El Niño and the Southern Oscillation (ENSO) and the Atlantic Warm Pool (AWP). Our study reveals that ENSO variability has a profound influence on the LOWS especially over south Florida and parts of panhandle Florida prior to 1976. In the post-1976 era the influence of ENSO has significantly diminished. Our results show that in this pre-1976 era, warm (cold) ENSO events in the boreal winter are followed by long (short) LOWS over the region. This variation is consistent with warm (cold) ENSO events influencing early (late) onset of the wet season in the region. There is significant relationship of the LOWS in south and northeast Florida with the variation of the AWP. Unlike the teleconnection with ENSO the relationship of the demise of the wet season with AWP is stronger in the post-1976 period compared to the pre-1976 period. Furthermore the variability of the LOWS has increased in the post-1976 period.

The observed teleconnection between the equatorial Amazon and the Intra-Americas Seas

Using observations of rainfall and SST analysis it is shown that there is a robust relationship with two-season lag between the austral summer (December–January–February [DJF]) Equatorial Amazon (EA) rainfall and the following boreal summer season (June–July–August [JJA]) Intra-Americas Seas (IAS) Sea Surface Temperature Anomalies (SSTA). It is observed that in wetter than normal austral summer seasons over EA, the SSTA in the IAS are cooler than normal in the following JJA season. This teleconnection also manifests in the ocean heat content of the IAS region. Our analysis indicates that the net surface heat flux into the ocean (particularly the surface longwave and the shortwave radiative fluxes) dictates the strongest influence on the JJA Caribbean SSTA, the core region of the IAS where the observed teleconnection with EA rainfall is strongest. This study also finds that this teleconnection is in fact a manifestation of the remote ENSO forcing on the Caribbean SSTA through its modulation of the EA rainfall anomalies. In a wet DJF year over EA, the Atlantic Inter-Tropical Convergence Zone (ITCZ) moves further southward than climatology. This causes the dry limb of the associated overturning circulation of the Atlantic ITCZ to reside over the Caribbean Sea region in the subsequent March–April–May and JJA seasons. As a result of this large-scale descent in the wet DJF year over EA, there is a net decrease in the heat flux into the ocean from increased emission of surface longwave radiation in the presence of anomalously dry atmosphere. In a dry DJF year over EA the Atlantic ITCZ is nearly co-located in the core region of the IAS, which is northward than the climatological location, resulting in the descending limb of the overturning location to be located further south of the Caribbean Sea leading to warmer SSTA.

Uncertainty and Intercalibration Analysis of H*Wind

AbstractThe Hurricane Research Division (HRD) Real-time Hurricane Wind Analysis System (H*Wind) is a software application used by NOAA’s HRD to create a gridded tropical cyclone wind analysis based on a wide range of observations. These analyses are used in both forecasting and research applications. Although mean bias and RMS errors are listed, H*Wind lacks robust uncertainty information that considers the contributions of random observation errors, relative biases between observation types, temporal drift resulting from combining nonsimultaneous measurements into a single analysis, and smoothing and interpolation errors introduced by the H*Wind analysis. This investigation seeks to estimate the total contributions of these sources, and thereby provide an overall uncertainty estimate for the H*Wind product.A series of statistical analyses show that in general, the total uncertainty in the H*Wind product in hurricanes is approximately 6% near the storm center, increasing to nearly 13% near the tropical st...

Evaluating the fidelity of downscaled climate data on simulated wheat and maize production in the southeastern US

Crop models are one of the most commonly used tools to assess the impact of climate variability and change on crop production. However, before the impact of projected climate changes on crop production can be addressed, a necessary first step is the assessment of the inherent uncertainty and limitations of the forcing data used in these crop models. In this paper, we evaluate the simulated crop production using separate crop models for maize (summer crop) and wheat (winter crop) over six different locations in the Southeastern United States forced with multiple sources of actual and simulated weather data. The paper compares the crop production simulated by a crop model for maize and wheat during a historical period, using daily weather data from three sources: station observations, dynamically downscaled global reanalysis, and dynamically downscaled historical climate model simulations from two global circulation models (GCMs). The same regional climate model is used to downscale the global reanalysis and both global circulation models’ historical simulation. The average simulated yield derived from bias-corrected downscaled reanalysis or bias-corrected downscaled GCMs were, in most cases, not statistically different from observations. Statistical differences of the average yields, generated from observed or downscaled GCM weather, were found in some locations under rainfed and irrigated scenarios, and more frequently in winter (wheat) than in summer (maize). The inter-annual variance of simulated crop yield using GCM downscaled data was frequently overestimated, especially in summer. An analysis of the bias-corrected climate data showed that despite the agreement between the modeled and the observed means of temperatures, solar radiation, and precipitation, their intra-seasonal variances were often significantly different from observations. Therefore, due to this high intra-seasonal variability, a cautious approach is required when using climate model data for historical yield analysis and future climate change impact assessments.

The rendition of the Atlantic Warm Pool in the reanalyses

The Atlantic Warm Pool (AWP) region, which is comprised of the Gulf of Mexico, Caribbean Sea and parts of the northwestern tropical Atlantic Ocean, is one of the most poorly observed parts of the global oceans. This study compares three ocean reanalyses, namely the Global Ocean Data Assimilation System of National Centers for Environmental Prediction (NCEP), the Climate Forecast System Reanalysis (CFSR) of NCEP, and the Simple Ocean Data Assimilation (SODA) for its AWP variation. The surface temperature in these ocean reanalyses is also compared with that from the Extended Range SST version 3 and Optimally Interpolated SST version 2 SST analyses. In addition we also compare three atmospheric reanalyses: NCEP-NCAR (R1), NCEP-DOE (R2), and CFSR for the associated atmospheric variability with the AWP. The comparison shows that there are important differences in the climatology of the AWP and its interannual variations. There are considerable differences in the subsurface ocean manifestation of the AWP with SODA (CFSR) showing the least (largest) modulation of the subsurface ocean temperatures. The remote teleconnections with the tropical Indian Ocean are also different across the reanalyses. However, all three oceanic reanalyses consistently show the absence of any teleconnection with the eastern equatorial Pacific Ocean. The influence of the AWP on the tropospheric temperature anomalies last for up to a one season lead and it is found to be relatively weak in R1 reanalyses. A simplified SST anomaly equation initially derived for diagnosing El Niño Southern Oscillation variability is adapted for the AWP variations in this study. The analysis of this equation reveals that the main contribution of the SST variation in the AWP region is from the variability of the net heat flux. All three reanalyses consistently show that the role of the ocean advective terms, including that associated with upwelling in the AWP region, is comparatively much smaller. The covariance of the SST tendency in the AWP with the net heat flux is large, with significant contributions from the variations of the surface shortwave and longwave fluxes.