Evaluating Intraseasonal Variability in Climate Forecasts: The Annular Mode Time Scales in the IPCC AR4 Models
The Northern and Southern Annular modes are the dominant signals in the extratropical atmosphere on intraseasonal timescales, and are closely linked with regional climate impacts and the occurrence of extreme events on shorter time scales (e.g. Thompson and Wallace 2001). The fidelity of their simulation in climate models, then, provides an important metric to assess the validity of trends in intraseasonal variability in their forecast. Here the ability of climate models in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) to capture the temporal structure of the annular modes is evaluated. Models capture the key qualitative features of the Northern and Southern Annular Modes: Northern Hemisphere time scales are shorter than those of the Southern Hemisphere and peak in boreal winter, while Southern Hemisphere time scales peak in austral spring and summer. Models, however, systematically overestimate the time scales, particularly in the Southern Hemisphere summer, where the multimodel ensemble average is approximately twice that of reanalyses. These biases in the intraseasonal time scales of the annular modes could impact estimates of extreme events based on the AR4 model forecasts.
The role of Great Lakes in simulating winter temperature of the Midwest: a large-scale modeling perspective
Owing to its huge heat capacity, adequate moisture supply, and albedo change, Great Lakes play an important role in Midwest winter temperature variations. Hence, how Great Lakes are represented in a large-scale model directly affect its capability of simulating such regional temperature properties. In this study, we analyze three NOAA GFDL GCMs, in which the treatments of lakes are significantly different while other aspects of the model are identical or similar. The standard GFDL AM2 model simply treats Great Lakes in the same manner as adjacent lands. In a modified version of AM2, Great Lakes are treated as one-layer water with huge heat capacity. In the newly-developed AM3, Great Lakes are resolved horizontally and vertically. Climatologically, the standard AM2 has about 2K cold bias compared to the ECMWF ERA40 reanalysis. It has consistently excessive cold events but insufficient warm event, which is consistent with its treatment of Great Lakes. The modified AM2 shows a more realistic simulation. AM3, though with a more realistic treatment of lakes, bears similar deficiencies as the standard AM2. Diagnostics of regional thermodynamics further sheds light on the linkages between Great Lakes physical processes and winter temperature variations.
Subseasonal Variability of the North American Coastal Cyclonic Activity: Implications for the Winter Precipitation and Wind Extremes
Subseasonal variability of the cyclonic activity along the Pacific coast of North America was diagnosed based on the NCEP/NCAR Reanalysis in 29 winters (DJF, 1979/80-2007/08). A significant portion of the variance associated with such variability can be attributed to the zonal distribution of convective heating in the tropical Pacific. In particular, eastward propagation of heating anomalies associated with Madden-Julian Oscillation (MJO) effectively increased the number and enhanced the average intensity of cyclones that approach the coastal line. The impact of the anomalies of the cyclonic activity on the likelihood of the occurrence of extreme precipitation and wind events in the coastal region was examined through composite and regression analysis. Further discussions include the implications of such variability for the intraseasonal prediction of the west coast cyclones and the potential impact on the disaster prewarning and planning in the coastal region.
Sensitivity of microwave rainfall estimation to the 'a priori' database: Extreme Events
The new version of Goddard profiling algorithm (GPROF2008) is used in this study. Different to the previous one, new algorithm has a new database using Tropical Rainfall Measuring Mission (TRMM) radar and cloud resolving models. Rather than focusing an global mean condition used in climate studies, this database focuses an extreme precipitation system. Retrieval result from new GPROF will be compared to that from previous algorithm and PR observation. The sensitivity of rainfall estimation to the different 'a priori' database will be also discussed.
Fog in the coastal region of southern Brazil: seasonal variations
Heating from Below: Impacts on Weather and Climate Prediction
Upward geothermal heat flow has been proved by observations and applications to be exist and influence weather and climate at regional scales. However, it is not considered in the existing weather and climate models due to the lack of comprehensive observations and understandings. Soil temperature represents the soil energy state that is influenced by both downward and upward energy transfers. It is a major variable in land surface models, representing soil energy status, storage, and transfer. It serves as an important factor indicating the underlying surface heating condition for weather and climate forecasts. However, soil temperature observations have not been utilized in present weather and climate models to provide soil energy state, although long-term observations have been in existence for more than half a century. Therefore, the objectives of this study is: 1) to consider heating from below in numerical modeling; and 2) to utilized observed soil temperature data in initializing numerical model to conduct impact study. As the first step in climate impact study, the Weather Research and Forecasting (WRF) model is used to study the impacts of changes to the surface heating condition, derived from soil temperature observations, on regional weather simulations. Large biases are found, as compared to observations, in the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-year reanalysis project (ERA-40) soil temperatures and in the lower boundary assumption adopted by the Noah land surface model. In six heavy rain cases studied herein, observed soil temperatures are used to initialize the land surface model and to provide a lower boundary condition at the bottom of the model soil layer. By analyzing the impacts from the incorporation of observed soil temperatures, the following major conclusions are drawn: 1) A consistent increase in the ground heat flux is found during the day, when the observed soil temperatures are used to correct the cold bias present in ERA-40. Soil temperature changes introduced at the initial time maintain positive values but gradually decrease in magnitude with time. Sensible and latent heat fluxes and the moisture flux experience an increase during the first six hours. 2) An increase in soil temperature impacts the air temperature through surface exchange, and near-surface moisture through evaporation. During the first two days, an increase in air temperature is seen across the region from the surface up to about 800 hPa (~1450 m). The maximum near-surface air temperature increase is found to be, averaged over all cases, 0.5 K on the first day and 0.3 K on the second day. 3) The strength of the low-level jet is affected by the changes described above and also by the consequent changes in horizontal gradients of pressure and thermal fields. Thus, the three-dimensional circulation is affected, in addition to changes seen in the humidity and thermal fields and the locations and intensities of precipitating systems. 4) Overall results indicate that the incorporation of observed soil temperatures introduces a persistent soil heating condition that is favorable to convective development and, consequently, improves the simulation of precipitation. The significant impacts on weather modeling imply that the heating from below may also have significant impact on regional climate. Future study of soil heating impacts on regional climate has been planned.
Statistical Tests of Taylor's Hypothesis: An Application to Precipitation Fields
The Taylor Hypothesis (TH) as applied to rainfall is a proposition about the space-time covariance structure of the rainfall field. Specifically, it supposes that if a spatio-temporal precipitation field with a stationary covariance Cov(r, τ) in both space r and time τ, moves with a constant velocity v, then the temporal covariance at time lag τ is equal to the spatial covariance at space lag v τ, that is, Cov(0, τ) = Cov(v τ, 0). Qualitatively this means that the field evolves slowly in time relative to the advective time scale, which is often referred to as the 'frozen field' hypothesis. Of specific interest is whether there is a cut-off or decorrelation time scale for which the TH holds for a given mean flow velocity v. In this study the validity of the TH is tested for precipitation fields using high-resolution gridded NEXRAD radar reflectivity data produced by the WSI Corporation by employing two different statistical approaches. The first method is based upon rigorous hypothesis testing while the second is based on a simple correlation analysis, which neglects possible dependencies in the correlation estimates. We use radar reflectivity values from the southeastern United States with an approximate horizontal resolution of 4 km x 4 km and a temporal resolution of 15 minutes. During the 4-day period from 2 to 5 May 2002, substantial precipitation occurs in the region of interest, and the motion of the precipitation systems is approximately uniform. The results of both statistical methods suggest that the TH might hold for the shortest space and time scales resolved by the data (4 km and 15 minutes), but that it does not hold for longer periods or larger spatial scales. Also, the simple correlation analysis tends to overestimate the statistical significance through failing to account for correlations between the covariance estimates.
The Pacific-Atlantic Dipole: A Climate Index with Effects on Trade Wind Strength and Central American Precipitation
The interaction of moist easterly trade winds and high Central American topography creates tropical rainforests in the mountains of Central America. Understanding changes in precipitation in this region is important because these rain forests host a wealth of biodiversity and are sensitive to changes in precipitation. This study focuses on causes of interannual variability in Central American precipitation. Specifically, we find that the relative phases of the Atlantic Nino and Pacific El Nino Southern Oscillation are related to the strength of the trade winds and precipitation over Central America. These relative phases are quantified through the creation of the Pacific Atlantic Dipole index, which is the difference between the Atlantic Nino and the El Nino Southern Oscillation indices. An index of moisture transport over Central America is also created. We find that the Pacific- Atlantic dipole is positively correlated to both the moisture transport index and to a new Central American precipitation station data set, comprised of 43 stations and spanning the period 1961-2000. Composites of years of high and low Pacific-Atlantic dipole index reveal the spatial pattern associated with its variability. These showed that when the PAD is low (high), there is a strengthening (weakening) of the subtropical high and a decrease (increase) in pressure over the eastern equatorial Pacific. This causes an increase (decrease) in the trade wind strength and subsequently a decrease (increase) in Central American precipitation. We conclude that the PAD index represents a dipole in sea surface temperature and sea level pressure between the Atlantic and Pacific Oceans. In the future, this could be used to predict interannual changes in trade wind strength and precipitation in Central America. With current changes in climate, a further understanding of this relationship may be important for the sensitive ecology in the region.