Search

Sea ice in the Arctic is one of the most rapidly changing components of the global climate system. Over the past few decades, summer areal extent has declined over 30, and all months show statistically significant declining trends. New satellite missions and techniques have greatly expanded information on sea ice thickness, but many uncertainties remain in the satellite data and long-term records are sparse. However, thickness observations and other satellite-derived data indicate a 40 decline in thickness, due in large part to the loss of thicker, older ice cover. The changes in sea ice are happening faster than models have projected. With continued increasing temperatures, summer ice-free conditions are likely sometime in the coming decades, though there are substantial uncertainties in the exact timing and high interannual variability will remain as sea ice decreases. The changes in Arctic sea ice are already having an impact on flora and fauna in the Arctic. Some species will face increasing challenges in the future, while new habitat will open up for other species. The changes are also affecting peoples living and working in the Arctic. Native communities are facing challenges to their traditional ways of life, while new opportunities open for shipping, fishing, and natural resource extraction.

We have developed a climate-quality data record of the clear-sky surface temperature of the Greenland Ice Sheet using the Moderate-Resolution Imaging Spectroradiometer (MODIS) ice-surface temperature (1ST) algorithm. A climate-data record (CDR) is a time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change. We present daily and monthly MODIS ISTs of the Greenland Ice Sheet beginning on 1 March 2000 and continuing through 31 December 2010 at 6.25-km spatial resolution on a polar stereographic grid. This record will be elevated in status to a CDR when at least nine more years of data become available either from MODIS Terra or Aqua, or from the Visible Infrared Imager Radiometer Suite (VIIRS) to be launched in October 2011. Our ultimate goal is to develop a CDR that starts in 1981 with the Advanced Very High Resolution (AVHRR) Polar Pathfinder (APP) dataset and continues with MODIS data from 2000 to the present, and into the VIIRS era. Differences in the APP and MODIS cloud masks have so far precluded the current 1ST records from spanning both the APP and MODIS time series in a seamless manner though this will be revisited when the APP dataset has been reprocessed. The complete MODIS 1ST daily and monthly data record is available online.

We have developed a climate-quality data record of the clear-sky surface temperature of the Greenland Ice Sheet using the Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra ice-surface temperature (1ST) algorithm. A climate-data record (CDR) is a time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change. We present daily and monthly Terra MODIS ISTs of the Greenland Ice Sheet beginning on 1 March 2000 and continuing through 31 December 2010 at 6.25-km spatial resolution on a polar stereographic grid within +/-3 hours of 17:00Z or 2:00 PM Local Solar Time. Preliminary validation of the ISTs at Summit Camp, Greenland, during the 2008-09 winter, shows that there is a cold bias using the MODIS IST which underestimates the measured surface temperature by approximately 3 C when temperatures range from approximately -50 C to approximately -35 C. The ultimate goal is to develop a CDR that starts in 1981 with the Advanced Very High Resolution (AVHRR) Polar Pathfinder (APP) dataset and continues with MODIS data from 2000 to the present. Differences in the APP and MODIS cloud masks have so far precluded the current IST records from spanning both the APP and MODIS IST time series in a seamless manner though this will be revisited when the APP dataset has been reprocessed. The Greenland IST climate-quality data record is suitable for continuation using future Visible Infrared Imager Radiometer Suite (VIIRS) data and will be elevated in status to a CDR when at least 9 more years of climate-quality data become available either from MODIS Terra or Aqua, or from the VIIRS. The complete MODIS IST data record will be available online in the summer of 2011.

This paper summarizes the NASA Visible Infrared Imaging Radiometer Suite (VIIRS) Land Science team's findings to date with respect to the utility of the VIIRS Land and Cryosphere EDRs to meet NASA's science requirements. Based on previous assessments and results from a recent 51-day global test performed by the Land Product Evaluation and Analysis Tool Element (Land PEATE), the NASA VIIRS Land Science team has determined that, if all the Land and Cryosphere EDRs are to serve the needs of the science community, a number of changes to several products and the Interface Data Processing Segment (IDPS) algorithm processing chain will be needed. In addition, other products will also need to be added to the VIIRS Land product suite to provide continuity for all of the MODIS land data record. As the NASA research program explores new global change research areas, the VIIRS instrument should also provide the polar-orbiting imager data from which new algorithms could be developed, produced, and validated.

Global sea ice products are produced from the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) on board both the Terra and Aqua satellites. Daily sea ice extent and ice-surface temperature (IST) products are available at 1- and 4-km resolution. Validation activities have been undertaken to assess the accuracy of the MODIS IST product at the South Pole station in Antarctica and in the Arctic Ocean using near-surface air-temperature data from a meteorological station and drifting buoys. Results from the study areas show that under clear skies, the MODIS ISTs are very close to those of the near-surface air temperatures with a bias of -1.1 and -1.2 K, and an uncertainty of 1.6 and 1.7 K, respectively. It is shown that the uncertainties would be reduced if the actual temperature of the ice surface were reported instead of the near-surface air temperature. It is not possible to get an accurate IST from MODIS in the presence of even very thin clouds or fog, however using both the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and the MODIS on the Aqua satellite, it may be possible to develop a relationship between MODIS-derived IST and ice temperature derived from the AMSR-E. Since the AMSR-E measurements are generally unaffected by cloud cover, they may be used to complement the MODIS IST measurements.

This document serves as a final report for NASA grant NAG5-8625 to the University of Wisconsin-Madison (UW). The report covers work done at Boston University for the first two years of the project (as NAG5-4903) and the third and final year of the project at the University of Wisconsin (NAG5-8625). In 1999 the third year's funding and tasks were transferred to UW where the Principal Investigator (J. Key) is now stationed. A no-cost extension period of the grant ended on June 15, 2001. This is a group project with the University of Colorado (CU) as the lead institution, where Judith Curry is the Principal Investigator. The overall project is jointly funded by NASA and NSF. The progress reported herein is specific to Boston University and the University of Wisconsin.

This document serves as a final report for NASA grant NAG5-4903 at Boston University. The report covers the first two years of the three-year project. The third year's funding and tasks have been transferred to the University of Wisconsin-Madison (UW) where the Principal Investigator (J. Key) is now stationed. The new UW grant number is NAG5-8625. Plans for the final project year at UW were detailed in the Request to Transfer document, submitted to NASA in July 1999. This is a group project with the University of Colorado (CU) as the lead institution, where Judith Curry is the Principal Investigator. The overall project is jointly funded by NASA and NSF. The progress reported herein is that specific to Boston University, but no attempt has been made to separate NASA and NSF activities.

This is the final report for the AVHRR Polar Pathfinder (APP) project activities at Boston University (BU). The grant to J. Key is part of a group proposal with the University of Colorado (CU) as the lead institution (J. Masianik, PI). The report is a summary of accomplishments over the three-year BU project, although the NASA grant to BU was only for the third year. For the first two years funding at BU was obtained via a subcontract from CU. The overall project report will be provided by the investigators at the University of Colorado this fall, when their no-cost extension period is completed. That report will also contain the information given here. As per NASA guidelines, this report is limited to three pages.

Two tools for the solution of radiative transfer problems are presented. Streamer is a highly flexible medium spectral resolution radiative transfer model based on the plane-parallel theory of radiative transfer. Capable of computing either fluxes or radiances, it is suitable for studying radiative processes at the surface or within the atmosphere and for the development of remote-sensing algorithms. FluxNet is a fast neural network-based implementation of Streamer for computing surface fluxes. It allows for a sophisticated treatment of radiative processes in the analysis of large data sets and potential integration into geophysical models where computational efficiency is an issue. Documentation and tools for the development of alternative versions of Fluxnet are available. Collectively, Streamer and FluxNet solve a wide variety of problems related to radiative transfer: Streamer provides the detail and sophistication needed to perform basic research on most aspects of complex radiative processes while the efficiency and simplicity of FluxNet make it ideal for operational use.

In many remote sensing studies of geophysical fields such as clouds, land cover, or sea ice characteristics, the fractional area coverage of the field in an image is estimated as the proportion of pixels that have the characteristic of interest (i.e., are part of the field) as determined by some thresholding operation. The effect of sensor field-of-view on this estimate is examined by modeling the unknown distribution of subpixel area fraction with the beta distribution, whose two parameters depend upon the true fractional area coverage, the pixel size, and the spatial structure of the geophysical field. Since it is often not possible to relate digital number, reflectance, or temperature to subpixel area fraction, the statistical models described are used to determine the effect of pixel size and thresholding operations on the estimate of area fraction for hypothetical geophysical fields. Examples are given for simulated cumuliform clouds and linear openings in sea ice, whose spatial structures are described by an exponential autocovariance function. It is shown that the rate and direction of change in total area fraction with changing pixel size depends on the true area fraction, the spatial structure, and the thresholding operation used.

Sunphotometer observations made from an aircraft several months after the June 1991 eruptions of Mount Pinatubo are used to quantify the spectral opacity of the Arctic stratosphere. Ancillary surface-based measurements are presented in support of the aircraft data that show large increases in stratospheric optical depth attributed to the presence of volcanic aerosols. Visible optical depths greater than 0.2 were observed during flight segments flown above the tropopause. An inversion algorithm and the optical depth data are used to infer effective aerosol size distributions. The distributions tend to be bimodal, having a large-partical mode radius of about 0.50 micron and a small-particle mode of higher concentration with radii less than 0.18 micron. Surface measurements made during spring 1992 and 1993 are also used to estimate a time constant (e-folding time) of about 13.5 months assuming that the Arctic stratosphere's opacity decays exponentially; this estimate is larger than decay times observed following other major volcanic eruptions. Our results suggest that any climate perturbations in the Arctic caused by eruptions of Pinatubo may be significant and will very likely persist longer than any volcanically-induced changes observed there during the past century.

The way in which thermal contrast between leads of varying widths and thickness can be distinguished from the background multiyear ice surface under varying atmospheric conditions is examined. The normalized brightness temperature difference between image pixels that include lead fractions and of the background ice is employed to determine thresholds of detection accounting for sensor FOV and various atmospheric phenomena that influence the Arctic radiation balance during winter. Surface temperatures are prescribed as a function of ice thickness, and the effects of the intervening atmosphere are simulated by varying the optical depths of hypothetic cloud or haze layers varying in microphysical characteristics. It is found that the limits of lead detection may be determined as a function of pixel lead fraction and atmospheric optical depth if suitable values of normalized contrast are used as detection criteria.

Spatial patterns of mean monthly surface albedo for May, June, and July, derived from DMSP Operational Line Scan (OLS) satellite imagery are compared with surface albedos derived from the International Satellite Cloud Climatology Program (ISCCP) monthly data set. Spatial patterns obtained by the two techniques are in general agreement, especially for June and July. Nevertheless, systematic differences in albedo of 0.05 - 0.10 are noted which are most likely related to uncertainties in the simple parameterizations used in the DMSP analyses, problems in the ISCCP cloud-clearing algorithm and other modeling simplifications. However, with respect to the eventual goal of developing a reliable automated retrieval algorithm for compiling a long-term albedo data base, these initial comparisons are very encouraging.

One surface-based and two satellite arctic cloud climatologies are compared in terms of the annual cycle and spatial patterns of total monthly cloud amounts. Additionally, amounts and spatial patterns of low, middle, and high cloud type are compared. The surface-based dataset is for the years 1951-81, while the satellite-based data are for 1979-85 and 1983-86. The satellite cloud amounts are generally 5-35 percent less than the surface observations over the entire Arctic. However, regional differences may be as high as 45 percent. During July the surface-based cloud amounts for the central Arctic are about 40 percent greater than the satellite-based, but only 10 percent greater in the Norwegian Sea area. Surprisingly, (ISCCP) cloud climatology and surface observations agree better during winter than during summer. Possible reasons for these differences are discussed.