RADIATIVE FLUXES AND ALBEDO FEEDBACK IN POLAR REGIONS

The Arctic is experiencing an unprecedented increase in surface temperature and decrease in sea ice extent. Discussion as to the causes that contribute to the Arctic warming is still ongoing. The ice-albedo feedback has been proposed as a possible mechanism for polar amplification of such warming. It states that more open water leads to more solar heat absorption, which results in more ice melting and more open water. In order to study this relationship there is a need for accurate information on the solar heat input into the Arctic Oceans. I have developed and improved inference schemes for shortwave radiative fluxes that respond to the needs of Polar Regions utilizing most recent information on atmospheric and surface states. A Moderate Resolution Imaging Spectroradiometer (MODIS) approach has been optimized for Polar Regions and implemented at 1° for 2002-2010 and at 5-km for 2007. A methodology was developed to derive solar fluxes from the Advanced Very High Resolution Radiometer (AVHRR) and implemented at 0.5° for 1983-2006. Evaluation against ground measurements over land and ocean at high latitudes shows that the MODIS shortwave flux estimates are in best agreement with ground observations as compared to other available satellite and model products, with a bias of -3.6 Wm-2 and standard deviation of 23 Wm-2 at a daily time scale. The AVHRR estimates agree with ground observations with a bias of -4.7 Wm-2 and a standard deviation of 41 Wm-2 at a daily time scale. The ice-albedo feedback was evaluated by computing the solar heating into the Arctic Ocean using the improved satellite flux estimates. A growth at a rate of 2 %/year in the trend of solar heating for 2003-09 was found at a 75 % confidence level; the trend for 1984-2002 was only 0.2 %/year at a 99 % confidence level. The ice retreat is correlated to the solar energy into the ocean at 0.7 at a 75 % confidence level. An increase in the open water fraction resulted in a maximum 300 % positive anomaly in solar heating in 2007 located where the maximum sea ice retreat is

Evaluation of Arctic land snow cover characteristics, surface albedo and temperature during the transition seasons from regional climate model simulations and satellite data

This paper evaluates the simulated Arctic land snow cover duration, snow water equivalent, snow cover fraction, surface albedo and land surface temperature in the regional climate model HIRHAM5 during 2008-2010, compared with various satellite and reanalysis data and one further regional climate model (COSMO-CLM). HIRHAM5 shows a general agreement in the spatial patterns and annual course of these variables, although distinct biases for specific regions and months are obvious. The most prominent biases occur for east Siberian deciduous forest albedo, which is overestimated in the simulation for snow covered conditions in spring. This may be caused by the simplified albedo parameterization (e.g. non-consideration of different forest types and neglecting the effect of fallen leaves and branches on snow for deciduous tree forest). The land surface temperature biases mirror the albedo biases in their spatial and temporal structures. The snow cover fraction and albedo biases can explain the simulated land surface temperature bias of ca. -3 °C over the Siberian forest area in spring

Snow observations from Arctic Ocean Soviet drifting stations: legacy and new directions

The Arctic Ocean is one of the most rapidly changing regions on the planet. Its warming climate has driven reductions in the region's sea ice cover which are likely unprecedented in recent history, with many of the environmental impacts being mediated by the overlying snow cover. As well as impacting energetic and material fluxes, the snow cover also obscures the underlying ice from direct satellite observation. While the radar waves emitted from satellite-mounted altimeters have some ability to penetrate snow cover, an understanding of snow geophysical properties remains critical to remote sensing of sea ice thickness. The paucity of Arctic Ocean snow observations was recently identified as a key knowledge gap and uncertainty by the Intergovernmental Panel on Climate Change's Special Report on Oceans and Cryosphere in a Changing Climate. This thesis aims to address that knowledge gap. Between 1937 and 1991 the Soviet Union operated a series of 31 crewed stations which drifted around the Arctic Ocean. During their operation, scientists took detailed observations of the atmospheric conditions, the physical oceanography, and the snow cover on the sea ice. This thesis contains four projects that feature these observations. The first two consider a well known snow depth and density climatology that was compiled from observations at the stations between 1954 & 1991. Specifically, Chapter two considers the role of seasonally evolving snow density in sea ice thickness retrievals, and Chapter three considers the impact of the climatological treatment itself on satellite estimates of sea ice thickness variability and trends. Chapter four presents a statistical model for the sub-kilometre distribution of snow depth on Arctic sea ice through analysis of snow depth transect data. Chapter five then compares the characteristics of snow melt onset at the stations with satellite observations and results from a recently developed model

Land Surface Climate in the Regional Arctic System Model

The article of record as published may be found at http://dx.doi.org/10.1175/JCLI-D-15-0415.1The 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 degrees C; however, in the cold seasons there are local biases that exceed 6 degrees 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.U.S. Department of Energy (DOE) [DE-FG02-07ER64460, DE-SC0006856, DE-SC0006178]; DO

A Global Investigation Of Cloud-Radiative Properties Through An Integrative Analysis Of Observations And Model Simulations

The cloud and radiative properties simulated in an assortment of global climate models (GCMs) and reanalyses are examined to identify and assess systematic biases based upon comparisons with multiple satellites observations and retrievals. The global mean total column cloud fraction (CF) simulated by the 33-member multimodel mean is 7% and 17% lower than the results from passive (Moderate Resolution Infrared Spectroradiometer, MODIS) and active (CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation, CALIPSO) satellite remote sensing platforms. The simulated cloud water path (CWP), which is used as a proxy for optical depth, on global average, has a negative bias of ~17 g mâ2. Despite these errors in simulated cloud properties, the simulated top-of-atmosphere (TOA) radiation budgets match relatively well with Clouds and the Earth Radiant Energy System (CERES) measurements. The biases in multimodel mean global TOA reflected shortwave (SW) and outgoing longwave (LW) fluxes and cloud radiative effects (CREs) are less than 2.5 W mâ2. Nevertheless, when assessing models individually, some physically inconsistent results are evident. For example, in the ACCESS1.0 model, the simulated TOA SW and LW fluxes are within 2 W mâ2 of the observed global means, however, the global mean CF and CWP are underpredicted by ~10% and ~25 g mâ2, respectively. These unphysical model biases suggest tuning of the modeled radiation budgets. Two dynamically-driven regimes, based on the atmospheric vertical motion at 500 hPa (Ï500), are identified to provide a more quantitative measure of error in the radiation fields determined separately by biases in CF and CWP. These error types include the regime-averaged biases, biases in the sensitivity of TOA CREs to CF/CWP, and their co-variations. Overall, the biases in simulated CF and CWP are larger in the descent regime (Ï500 \u3e 25 hPa dayâ1) than in the ascent regime (Ï500 \u3c â25 hPa dayâ1), but are better correlated with observations. According to CERES observations, the sensitivity of LW CRE to CF is stronger in the ascent regime than in the decent regime (0.82 vs. 0.23 W mâ2 %â1) and the multimodel mean overestimates this value by ~40%. The difference in sensitivity of SW CRE to CF between the two regimes is less drastic (â1.34 vs. â1.12 W mâ2 %â1). TOA CREs rely independently on CWP in regions of large scale ascent and decent, as their sensitivities are similar between these two regimes (e.g., SW CRE/CWP = â0.28 W gâ1 for both regimes). In general, the total TOA CRE errors are heavily weighted by their biases in simulated sensitivity and biases in the simulated CF. A new observationally-constrained, data product is generated that can be used as a process-oriented diagnostic tool to further identify errors in simulated cloud and radiation fields. Based on the CloudSat and CALIPSO Ice Cloud Property Product (2C-ICE), and through one-dimensional radiative transfer modeling, a global database of radiative heating rate profiles is produced for non-precipitating single-layered ice clouds. Non-precipitating single-layered ice clouds have a global occurrence frequency of ~18% with considerable frequency in the tropical upper troposphere (13â16 km). A variety of ice cloud types exist in the sample of single-layered ice clouds developed here, which is determined by the distribution on cloud-top temperatures (CTT). For example, a peak in the distribution near 190 K (260 K) suggests the existence of cirrus (glaciated ice) clouds. The ice cloud microphysical properties responsible for having the largest impact on radiation (e.g., ice water content [IWC] and effective radius [Re]) are largest in the tropics and mid-latitudes according to 2C-ICE. Accordingly, this is where the strongest TOA SW absorption, and subsequently, the strongest upper tropospheric net radiative heating (\u3e 1.5 K dayâ1) occurs. This newly generated product will provide the data for which new ice cloud parameterizations can be developed in global models

Influence of Arctic sea-ice variability on Pacific trade winds.

A conceptual model connecting seasonal loss of Arctic sea ice to midlatitude extreme weather events is applied to the 21st-century intensification of Central Pacific trade winds, emergence of Central Pacific El Nino events, and weakening of the North Pacific Aleutian Low Circulation. According to the model, Arctic Ocean warming following the summer sea-ice melt drives vertical convection that perturbs the upper troposphere. Static stability calculations show that upward convection occurs in annual 40- to 45-d episodes over the seasonally ice-free areas of the Beaufort-to-Kara Sea arc. The episodes generate planetary waves and higher-frequency wave trains that transport momentum and heat southward in the upper troposphere. Regression of upper tropospheric circulation data on September sea-ice area indicates that convection episodes produce wave-mediated teleconnections between the maximum ice-loss region north of the Siberian Arctic coast and the Intertropical Convergence Zone (ITCZ). These teleconnections generate oppositely directed trade-wind anomalies in the Central and Eastern Pacific during boreal winter. The interaction of upper troposphere waves with the ITCZ air-sea column may also trigger Central Pacific El Nino events. Finally, waves reflected northward from the ITCZ air column and/or generated by triggered El Nino events may be responsible for the late winter weakening of the Aleutian Low Circulation in recent years

Satellite and in situ observations for advancing global Earth surface modelling: a review

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort