Sean Swenson

Bio: Sean Swenson is an academic researcher from National Center for Atmospheric Research. The author has contributed to research in topics: Water storage & Permafrost. The author has an hindex of 46, co-authored 85 publications receiving 14517 citations. Previous affiliations of Sean Swenson include Cooperative Institute for Research in Environmental Sciences & University of Colorado Boulder.

Post‐processing removal of correlated errors in GRACE data

Abstract: [1] Gravity fields produced by the Gravity Recovery and Climate Experiment (GRACE) satellite mission require smoothing to reduce the effects of errors present in short wavelength components. As the smoothing radius decreases, these errors manifest themselves in maps of surface mass variability as long, linear features generally oriented north to south (i.e., stripes). The presence of stripes implies correlations in the gravity field coefficients. Here we examine the spectral signature of these correlated errors, and present a method to remove them. Finally, we apply the filter to a model of surface-mass variability to show that the filter has relatively little degradation of the underlying geophysical signals we seek to recover.

Parameterization improvements and functional and structural advances in version 4 of the Community Land Model

Abstract: [1] The Community Land Model is the land component of the Community Climate System Model. Here, we describe a broad set of model improvements and additions that have been provided through the CLM development community to create CLM4. The model is extended with a carbon-nitrogen (CN) biogeochemical model that is prognostic with respect to vegetation, litter, and soil carbon and nitrogen states and vegetation phenology. An urban canyon model is added and a transient land cover and land use change (LCLUC) capability, including wood harvest, is introduced, enabling study of historic and future LCLUC on energy, water, momentum, carbon, and nitrogen fluxes. The hydrology scheme is modified with a revised numerical solution of the Richards equation and a revised ground evaporation parameterization that accounts for litter and within-canopy stability. The new snow model incorporates the SNow and Ice Aerosol Radiation model (SNICAR) - which includes aerosol deposition, grain-size dependent snow aging, and vertically-resolved snowpack heating – as well as new snow cover and snow burial fraction parameterizations. The thermal and hydrologic properties of organic soil are accounted for and the ground column is extended to ∼50-m depth. Several other minor modifications to the land surface types dataset, grass and crop optical properties, surface layer thickness, roughness length and displacement height, and the disposition of snow-capped runoff are also incorporated. The new model exhibits higher snow cover, cooler soil temperatures in organic-rich soils, greater global river discharge, and lower albedos over forests and grasslands, all of which are improvements compared to CLM3.5. When CLM4 is run with CN, the mean biogeophysical simulation is degraded because the vegetation structure is prognostic rather than prescribed, though running in this mode also allows more complex terrestrial interactions with climate and climate change.

Accuracy of scaled GRACE terrestrial water storage estimates

Abstract: [1] We assess the accuracy of global-gridded terrestrial water storage (TWS) estimates derived from temporal gravity field variations observed by the Gravity Recovery and Climate Experiment (GRACE) satellites. The TWS data set has been corrected for signal modification due to filtering and truncation. Simulations of terrestrial water storage variations from land-hydrology models are used to infer relationships between regional time series representing different spatial scales. These relationships, which are independent of the actual GRACE data, are used to extrapolate the GRACE TWS estimates from their effective spatial resolution (length scales of a few hundred kilometers) to finer spatial scales (∼100 km). Gridded, scaled data like these enable users who lack expertise in processing and filtering the standard GRACE spherical harmonic geopotential coefficients to estimate the time series of TWS over arbitrarily shaped regions. In addition, we provide gridded fields of leakage and GRACE measurement errors that allow users to rigorously estimate the associated regional TWS uncertainties. These fields are available for download from the GRACE project website (available at http://grace.jpl.nasa.gov). Three scaling relationships are examined: a single gain factor based on regionally averaged time series, spatially distributed (i.e., gridded) gain factors based on time series at each grid point, and gridded-gain factors estimated as a function of temporal frequency. While regional gain factors have typically been used in previously published studies, we find that comparable accuracies can be obtained from scaled time series based on gridded gain factors. In regions where different temporal modes of TWS variability have significantly different spatial scales, gain factors based on the first two methods may reduce the accuracy of the scaled time series. In these cases, gain factors estimated separately as a function of frequency may be necessary to achieve accurate results.

Recent contributions of glaciers and ice caps to sea level rise

Abstract: Satellite measurements of Earth’s gravity field show that the mass loss of glaciers and ice caps contributed to sea level rise by approximately 0.4 millimetres per year between 2003 and 2010. The extent to which mass loss from ice-covered land areas contributes to sea-level rise is not known accurately, in part because previous global estimates use different techniques for glaciers, ice caps and ice sheets. The Gravity Recovery and Climate Experiment (GRACE) satellite has provided monthly measurements of the global gravity field since 2002. Using GRACE data, Jacob et al. assess regional mass loss between 2003 and 2010, and conclude that mass loss from ice-covered land areas contributed almost 1.5 mm per year to the sea-level rise. Estimated mass loss from the high mountains of Asia was negligible, in contrast to some other reports. Glaciers and ice caps (GICs) are important contributors to present-day global mean sea level rise1,2,3,4. Most previous global mass balance estimates for GICs rely on extrapolation of sparse mass balance measurements1,2,4 representing only a small fraction of the GIC area, leaving their overall contribution to sea level rise unclear. Here we show that GICs, excluding the Greenland and Antarctic peripheral GICs, lost mass at a rate of 148 ± 30 Gt yr−1 from January 2003 to December 2010, contributing 0.41 ± 0.08 mm yr−1 to sea level rise. Our results are based on a global, simultaneous inversion of monthly GRACE-derived satellite gravity fields, from which we calculate the mass change over all ice-covered regions greater in area than 100 km2. The GIC rate for 2003–2010 is about 30 per cent smaller than the previous mass balance estimate that most closely matches our study period2. The high mountains of Asia, in particular, show a mass loss of only 4 ± 20 Gt yr−1 for 2003–2010, compared with 47–55 Gt yr−1 in previously published estimates2,5. For completeness, we also estimate that the Greenland and Antarctic ice sheets, including their peripheral GICs, contributed 1.06 ± 0.19 mm yr−1 to sea level rise over the same time period. The total contribution to sea level rise from all ice-covered regions is thus 1.48 ± 0.26 mm −1, which agrees well with independent estimates of sea level rise originating from land ice loss and other terrestrial sources6.

Climate Change 2007: The Physical Science Basis.

Abstract: Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

The Community Climate System Model Version 4

Abstract: The fourth version of the Community Climate System Model (CCSM4) was recently completed and released to the climate community. This paper describes developments to all CCSM components, and documents fully coupled preindustrial control runs compared to the previous version, CCSM3. Using the standard atmosphere and land resolution of 1° results in the sea surface temperature biases in the major upwelling regions being comparable to the 1.4°-resolution CCSM3. Two changes to the deep convection scheme in the atmosphere component result in CCSM4 producing El Nino–Southern Oscillation variability with a much more realistic frequency distribution than in CCSM3, although the amplitude is too large compared to observations. These changes also improve the Madden–Julian oscillation and the frequency distribution of tropical precipitation. A new overflow parameterization in the ocean component leads to an improved simulation of the Gulf Stream path and the North Atlantic Ocean meridional overturning circulati...

Climate change and the permafrost carbon feedback

Abstract: Large quantities of organic carbon are stored in frozen soils (permafrost) within Arctic and sub-Arctic regions. A warming climate can induce environmental changes that accelerate the microbial breakdown of organic carbon and the release of the greenhouse gases carbon dioxide and methane. This feedback can accelerate climate change, but the magnitude and timing of greenhouse gas emission from these regions and their impact on climate change remain uncertain. Here we find that current evidence suggests a gradual and prolonged release of greenhouse gas emissions in a warming climate and present a research strategy with which to target poorly understood aspects of permafrost carbon dynamics.

Chapter 12 - Long-term climate change: Projections, commitments and irreversibility

Abstract: This chapter assesses long-term projections of climate change for the end of the 21st century and beyond, where the forced signal depends on the scenario and is typically larger than the internal variability of the climate system. Changes are expressed with respect to a baseline period of 1986-2005, unless otherwise stated.