Atmospheric forcing validation for modeling the central Arctic

Author Posting. © American Geophysical Union, 2007. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 34 (2007): L20706, doi:10.1029/2007GL031378.We compare daily data from the National Center for Atmospheric Research and National Centers for Environmental Prediction “Reanalysis 1” project with observational data obtained from the North Pole drifting stations in order to validate the atmospheric forcing data used in coupled ice-ocean models. This analysis is conducted to assess the role of errors associated with model forcing before performing model verifications against observed ocean variables. Our analysis shows an excellent agreement between observed and reanalysis sea level pressures and a relatively good correlation between observed and reanalysis surface winds. The observed temperature is in good agreement with reanalysis data only in winter. Specific air humidity and cloudiness are not reproduced well by reanalysis and are not recommended for model forcing. An example sensitivity study demonstrates that the equilibrium ice thickness obtained using NP forcing is two times thicker than using reanalysis forcing.This research is supported by the National Science Foundation Office of Polar Programs (under Cooperative Agreements Nos. OPP-0002239 and OPP-0327664) with the International Arctic Research Center, University of Alaska Fairbanks, NSF grant OPP- 0424864 and by Russian Foundation for Basic Research, No. 07-05-13576

Sensitivity of L-band vegetation optical depth to carbon stocks in tropical forests: a comparison to higher frequencies and optical indices

Supplementary data to this article can be found online at https://doi.org/10.1016/j.rse.2019.111303.Monitoring vegetation carbon in tropical regions is essential to the global carbon assessment and to evaluate the actions oriented to the reduction of forest degradation. Mainly, satellite optical vegetation indices and LiDAR data have been used to this purpose. These two techniques are limited by cloud cover and are sensitive only to the top of vegetation. In addition, the vegetation attenuation to the soil microwave emission, represented by the vegetation optical depth (VOD), has been applied for biomass estimation using frequencies ranging from 4 to 30¿GHz (C- to K-bands). Atmosphere is transparent to microwaves and their sensitivity to canopy layers depends on the frequency, with lower frequencies having greater penetration depths. In this regard, L-band VOD (1.4¿GHz) is expected to enhance the ability to estimate carbon stocks. This study compares the sensitivity of different VOD products (from L, C, and X-bands) and an optical vegetation index (EVI) to the above-ground carbon density (ACD). It quantifies the contribution of ACD and forest cover proportion to the VOD/EVI signals. The study is conducted in Peru, southern Colombia and Panama, where ACD maps have been derived from airborne LiDAR. Results confirm the enhanced sensitivity of L-band VOD to ACD when compared to higher frequency bands, and show that the sensitivity of all VOD bands decreases in the densest forests. ACD explains 34% and forest cover 30% of L-band VOD variance, and these proportions gradually decrease for EVI, C-, and X-band VOD, respectively. Results are consistent through different categories of altitude and carbon density. This pattern is found in most of the studied regions and in flooded forests. Results also show that C-, X-band VOD and EVI provide complementary information to L-band VOD, especially in flooded forests and in mountains, indicating that synergistic approaches could lead to improved retrievals in these regions. Although the assessment of vegetation carbon in the densest forests requires further research, results from this study support the use of new L-band VOD estimates for mapping the carbon of tropical forests.Peer ReviewedPostprint (author's final draft

The Potential for Sea Level Rise: new estimates from Glacier and Ice Cap area and volume distributions

Projections of sea-level rise from mountain glaciers and ice caps for the next century and beyond should be based on an assessment of the ice available for melting. Projections to date are based on all regions except Greenland and Antarctica (the latter are considered separately by the IPCC), yet no sound estimates for the appropriate volume of ice and its potential for sea level rise are evident in the literature. An ice cap data set is compiled allowing the separate treatment of glacier area coverage data. Glacier inventory data are comprehensive enough in some regions to allow the estimation of glacier size distributions. The differences in the distributions are related to a metric of the regional topographic variability, allowing glacier size distributions to be estimated on a 1o latitude longitude grid of cells containing glaciers. Appropriate volume-area scaling for glaciers and for ice caps gives global estimates of glacier and ice cap volumes by size class. This leads to an estimate of the total ice volume of 0.087 ± 0.010 106 km3 and a sea level rise equivalent of 0.241 ± 0.026 m. The glaciers and ice caps contribute 41% and 59% to these estimates respectively. These values are based on data sets compiled during several decades, mainly in the second half of the 20th Century. We compare our results to published results that include the glaciers and icecaps at the margins of the Greenland and Antarctic ice sheet

Variability of Arctic and North Atlantic sea ice: A combined analysis of model results and observations from 1978 to 2001

Ice cover data simulated by a coupled sea ice-oceanmodel of the North Atlantic and the Arctic Ocean are compared withsatellite observations for the period 1978 to 2001. The capability ofthe model in reproducing the long-term mean state and the inter-seasonalvariability is demonstrated. The main modes of variability of thesatellite data and the simulation in the summer and winter half yearsare highly similar.Using NCEP/NCAR reanalysis data and the results from the sea ice-oceanmodel, we describe the relationship with atmospheric and oceanicvariables for the first two modes of sea-ice concentration variabilityin winter and in summer. The first winter mode shows a time delayedresponse to the Arctic Oscillation due to advection of heatanomalies in the ocean. The second winter mode is dominated by anevent in the late 1990s that is characterized by anomalously highpressure over the eastern Arctic. The first summer mode isstrongly influenced by the Arctic Oscillation of the previouswinter. The second summer mode is caused by anomalous air temperaturein the Arctic. This mode shows a distinctive trend and is related to anice extent reduction of about 4 10^5 km^2 over the 23 years ofanalysis