ORCID Profile
0000-0002-4554-9289
Current Organisation
University of Colorado at Boulder
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Publisher: International Glaciological Society
Date: 2015
Abstract: This work evaluates the fidelity of the polar marine Ekman layer in the Regional Arctic System Model (RASM) and Community Earth System Model (CESM) using sea-ice inertial oscillations as a proxy for ice-ocean Ekman transport. A case study is presented that demonstrates that RASM replicates inertial oscillations in close agreement with motion derived using the GPS. This result is obtained from a year-long case study pre-dating the recent decline in perennial Arctic sea ice, using RASM with sub-hourly coupling between the atmosphere, sea-ice and ocean components. To place this work in context, the RASM coupling method is applied to CESM, increasing the frequency of oceanic flux exchange from once per day in the standard CESM configuration, to every 30 min. For a single year simulation, this change causes a considerable increase in the median inertial ice speed across large areas of the Southern Ocean and parts of the Arctic sea-ice zone. The result suggests that processes associated with the passage of storms over sea ice (e.g. oceanic mixing, sea-ice deformation and surface energy exchange) are underestimated in Earth System Models that do not resolve inertial frequencies in their marine coupling cycle.
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-13137
Abstract: & & Our goal here is to answer the question, & #8220 what drives the demand side of drought?& #8221 We achieve this by decomposing atmospheric evaporative demand (Eo) anomalies during periods of drought into contributions from all of its drivers, using the US Midwest as a study region. In drought, anomalies in Eo are driven by anomalies in moisture availability, but Eo reacts quickly and so is a robust drought indicator. Thus, asking to what extent each meteorological driver determines evaporative demand in drought conditions is of value both academically and operationally.& & & & We define drought as a sustained imbalance between the supply of moisture from the atmosphere to the surface (Precipitation) and the demand in the atmosphere for moisture from the surface, in favor of the demand. The demand arm is atmospheric evaporative demand (Eo (sometimes referred to as & #8220 otential evaporation& #8221 ) evapotranspiration (ET), the actual return flux to the atmosphere, is determined as the extent to which this demand can be met by the moisture available at the surface.& & & & In this context, Eo can be thought of as the & #8220 thirst of the atmosphere.& #8221 It is a function of meteorological and radiative drivers at the surface: specifically temperature, solar radiation, wind speed, and humidity (and to a lesser degree, surface pressure). For Eo we use daily reference ET (ETo) from the Penman-Monteith equation, which provides a fully physical estimate that incorporates the effects of both advective and radiative forcing. We drive ETo by inputs from the North American Land Data Assimilation System phase-2 (NLDAS-2), which are distributed across CONUS at a spatial resolution of 0.125 degrees, and available from 1979 to the present.& & & & Drought periods are determined using various spatially distributed drought-monitoring tools: specifically, the US Drought Monitor (USDM) the Evaporative Demand Drought Index (EDDI) the Standardized Precipitation Index (SPI) and soil moisture percentiles from the NLDAS-driven Noah land surface model.& & & & We conduct a first-order analysis of the anomalies in Eo that exist during drought conditions. This technique assumes that the contributions from anomalies in all drivers sum to the anomaly in Eo each driver& #8217 s contribution is the product of the sensitivity of Eo to, and the anomaly in, the driver. As our expression for Eo (i.e., Penman-Monteith ETo) is differentiable, the sensitivity to each driver can be derived explicitly by partial differentiation. Drivers& #8217 anomalies are observed by querying the reanalysis during drought periods and deriving deviations from the drivers& #8217 long-term means for the same periods across the entire reanalysis period.& & & & Here we present the (i) general methodology for both the development of Eo and its decomposition and (ii) the results of the decomposition of drought-period Eo anomalies into the relative contributions from each driver across the Midwest Drought Early Warning System (DEWS) region.& &
Publisher: American Meteorological Society
Date: 26-08-2016
Abstract: The Regional Arctic System Model (RASM) is a fully coupled, regional Earth system model applied over the pan-Arctic domain. This paper discusses the implementation of the Variable Infiltration Capacity land surface model (VIC) in RASM and evaluates the ability of RASM, version 1.0, to capture key features of the land surface climate and hydrologic cycle for the period 1979–2014 in comparison with uncoupled VIC simulations, reanalysis datasets, satellite measurements, and in situ observations. RASM reproduces the dominant features of the land surface climatology in the Arctic, such as the amount and regional distribution of precipitation, the partitioning of precipitation between runoff and evapotranspiration, the effects of snow on the water and energy balance, and the differences in turbulent fluxes between the tundra and taiga biomes. Surface air temperature biases in RASM, compared to reanalysis datasets ERA-Interim and MERRA, are generally less than 2°C however, in the cold seasons there are local biases that exceed 6°C. Compared to satellite observations, RASM captures the annual cycle of snow-covered area well, although melt progresses about two weeks faster than observations in the late spring at high latitudes. With respect to derived fluxes, such as latent heat or runoff, RASM is shown to have similar performance statistics as ERA-Interim while differing substantially from MERRA, which consistently overestimates the evaporative flux across the Arctic region.
No related grants have been discovered for MIMI HUGHES.