ALBERT

Description: We are developing a climate-data record (CDR of daily "clear-sky" ice-surface temperature (IST) of the Greenland Ice Sheet, from 1982 to the present using Advanced Very High Resolution Radiometer (AVHRR) (1982 - present) and Moderate-Resolution Imaging Spectroradiometer (MODIS) data (2000 - present) at a resolution of approximately 5 km. The CDR will be continued in the National Polar-orbiting Operational Environmental Satellite System Visible/Infrared Imager Radiometer Suite era. Two algorithms remain under consideration. One algorithm under consideration is based on the split-window technique used in the Polar Pathfinder dataset (Fowler et al., 2000 & 21007). Another algorithm under consideration, developed by Comiso (2006), uses a single channel of AVHRR data (channel 4) in conjunction with meteorological-station data to account for atmospheric effects and drift between AVHRR instruments. Known issues being addressed in the production of the CDR are: tune-series bias caused by cloud cover (surface temperatures can be different under clouds vs. clear areas) and cross-calibration in the overlap period between AVHRR instruments, and between AVHRR and MODIS instruments. Because of uncertainties, mainly due to clouds (Stroeve & Steffen, 1998; Wang and Key, 2005; Hall et al., 2008 and Koenig and Hall, submitted), time-series of satellite 1S'1" do not necessarily correspond to actual surface temperatures. The CDR will be validated by comparing results with automatic-,",eather station (AWS) data and with satellite-derived surface-temperature products. Regional "clear-sky" surface temperature increases in the Arctic, measured from AVHRR infrared data, range from 0.57+/-0.02 deg C (Wang and Key, 2005) to 0.72+/-0.10 deg C (Comiso, 2006) per decade since the early 1980s. Arctic warming has important implications for ice-sheet mass balance because much of the periphery of the Greenland Ice Sheet is already near 0 deg C during the melt season, and is thus vulnerable to rapid melting if temperatures continue to increase. References

Description: Ice nuclei (IN) significantly affect clouds via supercooled droplets, that in turn modulate atmospheric radiation and thus climate change. Since the IN effect is relatively strong in stratiform clouds but weak in convective ones, the overall effect depends on the ratio of stratiform to convective cloud amount. In this paper, 10 years of TRMM (Tropical Rainfall Measuring Mission) satellite data are analyzed to confirm that stratiform precipitation fraction increases with increasing latitude, which implies that the IN effect is stronger at higher latitudes. To quantitatively evaluate the IN effect versus latitude, large-scale forcing data from ten field campaigns are used to drive a CRM (cloud-resolving model) to generate longterm cloud simulations. As revealed in the simulations, the increase in the net downward radiative flux at the TOA (top of the atmosphere) from doubling the current IN concentrations is larger at higher latitude, which is attributed to the meridional tendency in the stratiform precipitation fraction. Surface warming from doubling the IN concentrations, based on the radiative balance of the globe, is compared with that from anthropogenic COZ . It is found that the former effect is stronger than the latter in middle and high latitudes but not in the Tropics. With regard to the impact of IN on global warming, there are two factors to consider: the radiative effect from increasing the IN concentration and the increase in IN concentration itself. The former relies on cloud ensembles and thus varies mainly with latitude. In contrast, the latter relies on IN sources (e.g., the land surface distribution) and thus varies not only with latitude but also longitude. Global desertification and industrialization provide clues on the geographic variation of the increase in IN concentration since pre-industrial times. Thus, their effect on global warming can be inferred and then be compared with observations. A general match in geographic and seasonal variations between the inferred and observed warming suggests that IN may have contributed positively to global warming over the past decades, especially in middle and high latitudes.

Description: Satellite-derived aerosol data sets, such as those provided by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, are greatly improving our understanding of global aerosol optical depth (AOD). Yet, there are sampling issues. MODIS specific orbital geometry, convolved with the need to avoid bright surfaces (glint, desert, clouds, etc.), means that AOD can be under- or over-sampled in places. When deriving downstream products, such as daily or monthly gridded AOD, one must consider the spatial and temporal density of the measurements relative to the gradients of the true AOD. Additionally, retrieval confidence criteria should be considered. Averaged products are highly dependent on choices made for data aggregation and weighting, and sampling errors can be further propagated when deriving regional or global mean AOD. Different choices for aggregation and weighting result in estimates of regional and global means varying by 30% or more. The impacts of a particular averaging algorithm vary by region and surface type and can be shown to represent different tolerance for clouds and retrieval confidence.

Description: The responses of the atmospheric water cycle and climate of West Africa and the Atlantic to radiative forcing of Saharan dust are studied using the NASA finite volume general circulation model (fvGCM), coupled to a mixed layer ocean. We find evidence of an "elevated heat pump" (EHP) mechanism that underlines the responses of the atmospheric water cycle to dust forcing as follow. During the boreal summerr, as a result of large-scale atmospheric feedback triggered by absorbing dust aerosols, rainfall and cloudiness are ehanIed over the West Africa/Eastern Atlantic ITCZ, and suppressed over the West Atlantic and Caribbean region. Shortwave radiation absorption by dust warms the atmosphere and cools the surface, while longwave has the opposite response. The elevated dust layer warms the air over West Africa and the eastern Atlantic. As the warm air rises, it spawns a large-scale onshore flow carrying the moist air from the eastern Atlantic and the Gulf of Guinea. The onshore flow in turn enhances the deep convection over West Africa land, and the eastern Atlantic. The condensation heating associated with the ensuing deep convection drives and maintains an anomalous large-scale east-west overturning circulation with rising motion over West Africa/eastern Atlantic, and sinking motion over the Caribbean region. The response also includes a strengthening of the West African monsoon, manifested in a northward shift of the West Africa precipitation over land, increased low-level westerlies flow over West Africa at the southern edge of the dust layer, and a near surface westerly jet underneath the dust layer overr the Sahara. The dust radiative forcing also leads to significant changes in surface energy fluxes, resulting in cooling of the West African land and the eastern Atlantic, and warming in the West Atlantic and Caribbean. The EHP effect is most effective for moderate to highly absorbing dusts, and becomes minimized for reflecting dust with single scattering albedo at0.95 or higher.

Description: Seasonal snow cover in extra-tropical areas of South America was examined in this study using passive microwave satellite data from the Scanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7 satellite and from the Special Sensor Microwave Imagers (SSM/I) on board the Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1979-2006, both snow cover extent and snow mass were estimated for the months of May-September. Most of the seasonal snow in South America occurs in the Patagonia region of Argentina. The average snow cover extent for July, the month with the greatest average extent during the 28-year period of record, is 321,674 sq km. The seasonal (May-September) 2 average snow cover extent was greatest in 1984 (464,250 sq km) and least in 1990 (69,875 sq km). In terms of snow mass, 1984 was also the biggest year (1.19 x 10(exp 13) kg) and 1990 was the smallest year (0.12 X 10(exp 13) kg). A strong relationship exists between the snow cover area and snow mass, correlated at 0.95, though no significant trend was found over the 28 year record for either snow cover extent or snow mass. For this long term climatology, snow mass and snow cover extent are shown to vary considerably from month to month and season to season. This analysis presents a consistent approach to mapping and measuring snow in South America utilizing an appropriate and readily available long term snow satellite dataset. This is the optimal dataset available, thus far, for deriving seasonal snow cover and snow mass in this region. Nonetheless, shallow snow, wet snow, snow beneath forests, as well as snow along coastal areas all may confound interpretation using passive microwave approaches. More work needs to be done to reduce the uncertainties in the data and hence, increase the confidence of the interpretation

Description: A general theory for retrieving the fraction of ground flashes in N lightning observed by a satellite-based lightning imager is provided. An "exponential model" is applied as a physically reasonable constraint to describe the measured optical parameter distributions, and population statistics (i.e., mean, variance) are invoked to add additional constraints to the retrieval process. The retrieval itself is expressed in terms of a Bayesian inference, and the Maximum A Posteriori (MAP) solution is obtained. The approach is tested by performing simulated retrievals, and retrieval error statistics are provided. The ability to retrieve ground flash fraction has important benefits to the atmospheric chemistry community. For example, using the method to partition the existing satellite global lightning climatology into separate ground and cloud flash climatologies will improve estimates of lightning nitrogen oxides (NOx) production; this in turn will improve both regional air quality and global chemistry/climate model predictions.

Description: The foremost challenge in parameterizing convective clouds and cloud systems in large-scale models are the many coupled dynamical and physical processes that interact over a wide range of scales, from microphysical scales to the synoptic and planetary scales. This makes the comprehension and representation of convective clouds and cloud systems one of the most complex scientific problems in Earth science. During the past decade, the Global Energy and Water Cycle Experiment (GEWEX) Cloud System Study (GCSS) has pioneered the use of single-column models (SCMs) and cloud-resolving models (CRMs) for the evaluation of the cloud and radiation parameterizations in general circulation models (GCMs; e.g., GEWEX Cloud System Science Team 1993). These activities have uncovered many systematic biases in the radiation, cloud and convection parameterizations of GCMs and have led to the development of new schemes (e.g., Zhang 2002; Pincus et al, 2003; Zhang and Wu 2003; Wu et al. 2003; Liang and Wu 2005; Wu and Liang 2005, and others). Comparisons between SCMs and CRMs using the same large-scale forcing derived from field campaigns have demonstrated that CRMs are superior to SCMs in the prediction of temperature and moisture tendencies (e.g., Das et al. 1999; Randall et al 2003b; Xie et al. 2005).

Description: Many state and local air quality agencies use the U.S. Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) modeling system to determine compliance with the National Ambient Air Quality Standards (NAAQS). Because emission reduction scenarios are tested using CMAQ with an aim of determining the most efficient and cost effective strategies for attaining the NAAQS, it is very important that trace gas concentrations derived by CMAQ are accurate. Overestimating concentrations can literally translate into billions of dollars lost by commercial and government industries forced to comply with the standards. Costly health, environmental and socioeconomic problems can result from concentration underestimates. Unfortunately, lightning modeling for CMAQ is highly oversimplified. This leads to very poor estimates of lightning-produced nitrogen oxides "NOx" (= NO + NO2) which directly reduces the accuracy of the concentrations of important CMAQ trace gases linked to NOx concentrations such as ozone and methane. Today it is known that lightning is the most important NOx source in the upper troposphere with a global production rate estimated to vary between 2-20 Tg(N)/yr. In addition, NOx indirectly influences our climate since it controls the concentration of ozone and hydroxyl radicals (OH) in the atmosphere. Ozone is an important greenhouse gas and OH controls the oxidation of various greenhouse gases. We describe a robust NASA lightning model, called the Lightning Nitrogen Oxides Model (LNOM) that combines state-of-the-art lightning measurements, empirical results from field studies, and beneficial laboratory results to arrive at a realistic representation of lightning NOx production for CMAQ. NASA satellite lightning data is used in conjunction with ground-based lightning detection systems to assure that the best representation of lightning frequency, geographic location, channel length, channel altitude, strength (i.e., channel peak current), and number of strokes per flash are accounted for. LNOM combines all of these factors in a straightforward approach that is easily implemented into CMAQ. We anticipate that future applications of LNOM will produce significant and important changes in CMAQ trace gas concentrations for various regions and times. We also anticipate that these changes will have a direct impact on decision makers responsible for NAAQS attainment.

Description: The USCLI VAR working group on drought recently initiated a series of global climate model simulations forced with idealized SST anomaly patterns, designed to address a number of uncertainties regarding the impact of SST forcing and the role of land-atmosphere feedbacks on regional drought. Specific questions that the runs are designed to address include: What are the mechanisms that maintain drought across the seasonal cycle and from one year to the next? What is the role of the leading patterns of SST variability, and what are the physical mechanisms linking the remote SST forcing to regional drought, including the role of land-atmosphere coupling? The runs were carried out with five different atmospheric general circulation models (AGCM5), and one coupled atmosphere-ocean model in which the model was continuously nudged to the imposed SST forcing. This paper provides an overview of the experiments and some initial results focusing on the responses to the leading patterns of annual mean SST variability consisting of a Pacific El Nino/Southern Oscillation (ENSO)-like pattern, a pattern that resembles the Atlantic Multi-decadal Oscillation (AMO), and a global trend pattern. One of the key findings is that all the AGCMs produce broadly similar (though different in detail) precipitation responses to the Pacific forcing pattern, with a cold Pacific leading to reduced precipitation and a warm Pacific leading to enhanced precipitation over most of the United States. While the response to the Atlantic pattern is less robust, there is general agreement among the models that the largest precipitation response over the U.S. tends to occur when the two oceans have anomalies of opposite sign. That is, a cold Pacific and warm Atlantic tend to produce the largest precipitation reductions, whereas a warm Pacific and cold Atlantic tend to produce the greatest precipitation enhancements. Further analysis of the response over the U.S. to the Pacific forcing highlights a number of noteworthy and to some extent unexpected results. These include a seasonal dependence of the precipitation response that is characterized by signal-to-noise ratios that peak in spring, and surface temperature signal-to-noise ratios that are both lower and show less agreement among the models than those found for the precipitation response. Another interesting result concerns what appears to be a substantially different character in the surface temperature response over the U.S. to the Pacific forcing by the only model examined here that was developed for use in numerical weather prediction. The response to the positive SST trend forcing pattern is an overall surface warming over the world's land areas with substantial regional variations that are in part reproduced in runs forced with a globally uniform SST trend forcing. The precipitation response to the trend forcing is weak in all the models.

Description: The USCLIVAR working group on drought recently initiated a series of global climate model simulations forced with idealized SST anomaly patterns, designed to address a number of uncertainties regarding the impact of SST forcing and the role of land-atmosphere feedbacks on regional drought. Specific questions that the runs are designed to address include, What are the mechanisms that maintain drought across the seasonal cycle and from one year to the next? What is the role of the leading patterns of SST variability, and what are the physical mechanisms linking the remote SST forcing to regional drought, including the role of land-atmosphere coupling? The runs were carried out with five different atmospheric general circulation models (AGCMs), and one coupled atmosphere-ocean model in which the model was continuously nudged to the imposed SST forcing. This talk provides an overview of the experiments and some initial results focusing on the responses to the leading patterns of annual mean SST variability consisting of a Pacific El Nino/Southern Oscillation (ENSO)-like pattern, a pattern that resembles the Atlantic Multi-decadal Oscillation (AMO), and a global trend pattern. One of the key findings is that all the AGCMs produce broadly similar (though different in detail) precipitation responses to the Pacific forcing pattern, with a cold Pacific leading to reduced precipitation and a warm Pacific leading to enhanced precipitation over most of the United States. While the response to the Atlantic pattern is less robust, there is general agreement among the models that the largest precipitation response over the U.S. tends to occur when the two oceans have anomalies of opposite sign. That is, a cold Pacific and warm Atlantic tend to produce the largest precipitation reductions, whereas a warm Pacific and cold Atlantic tend to produce the greatest precipitation enhancements. Further analysis of the response over the U.S. to the Pacific forcing highlights a number of noteworthy and to some extent unexpected results. These include a seasonal dependence of the precipitation response that is characterized by signal-to-noise ratios that peak in spring, and surface temperature signal-to-noise ratios that are both lower and show less agreement among the models than those found for the precipitation response. Another interesting result concerns what appears to be a substantially different character in the surface temperature response over the U.S. to the Pacific forcing by the only model examined here that was developed for use in numerical weather prediction. The response to the positive SST trend forcing pattern is an overall surface warming over the world's land areas with substantial regional variations that are in part reproduced in runs forced with a globally uniform SST trend forcing. The precipitation response to the trend forcing is weak in all the models. It is hoped that these early results will serve to stimulate further analysis of these simulations, as well as suggest new research on the physical mechanisms contributing to hydroclimatic variability and change throughout the world.