Andrew G. Slater

The large‐scale freshwater cycle of the Arctic

This paper synthesizes our understanding of the Arctics large-scale freshwater cycle. It combines terrestrial and oceanic observations with insights gained from the ERA-40 reanalysis and land surface and ice-ocean models. Annual mean freshwater input to the Arctic Ocean is dominated by river discharge (38%), inflow through Bering Strait (30%), and net precipitation (24%). Total freshwater export from the Arctic Ocean to the North Atlantic is dominated by transports through the Canadian Arctic Archipelago (35%) and via Fram Strait as liquid (26%) and sea ice (25%). All terms are computed relative to a reference salinity of 34.8. Compared to earlier estimates, our budget features larger import of freshwater through Bering Strait and larger liquid phase export through Fram Strait. While there is no reason to expect a steady state, error analysis indicates that the difference between annual mean oceanic inflows and outflows (∼8% of the total inflow) is indistinguishable from zero. Freshwater in the Arctic Ocean has a mean residence time of about a decade. This is understood in that annual freshwater input, while large (∼8500 km3), is an order of magnitude smaller than oceanic freshwater storage of ∼84,000 km3. Freshwater in the atmosphere, as water vapor, has a residence time of about a week. Seasonality in Arctic Ocean freshwater storage is nevertheless highly uncertain, reflecting both sparse hydrographic data and insufficient information on sea ice volume. Uncertainties mask seasonal storage changes forced by freshwater fluxes. Of flux terms with sufficient data for analysis, Fram Strait ice outflow shows the largest interannual variability.

Snow Data Assimilation via an Ensemble Kalman Filter

Abstract A snow data assimilation study was undertaken in which real data were used to update a conceptual model, SNOW-17. The aim of this study is to improve the model’s estimate of snow water equivalent (SWE) by merging the uncertainties associated with meteorological forcing data and SWE observations within the model. This is done with a view to aiding the estimation of snowpack initial conditions for the ultimate objective of streamflow forecasting via a distributed hydrologic model. To provide a test of this methodology, the authors performed experiments at 53 stations in Colorado. In each case the situation of an unobserved location is mimicked, using the data at any given station only for validation; essentially, these are withholding experiments. Both ensembles of model forcing data and assimilated data were derived via interpolation and stochastic modeling of data from surrounding sources. Through a process of cross validation the error for the ensemble of model forcing data and assimilated obser...

Simulations of a Boreal Grassland Hydrology at Valdai, Russia: PILPS Phase 2(d)

The Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS) aims to improve understanding and modeling of land surface processes. PILPS phase 2(d) uses a set of meteorological and hydrological data spanning 18 yr (1966‐83) from a grassland catchment at the Valdai water-balance research site in Russia. A suite of stand-alone simulations is performed by 21 land surface schemes (LSSs) to explore the LSSs’ sensitivity to downward longwave radiative forcing, timescales of simulated hydrologic variability, and biases resulting from single-year simulations that use recursive spinup. These simulations are the first in PILPS to investigate the performance of LSSs at a site with a well-defined seasonal snow cover and frozen soil. Considerable model scatter for the control simulations exists. However, nearly all the LSS scatter in simulated root-zone soil moisture is contained within the spatial variability observed inside the catchment. In addition, all models show a considerable sensitivity to longwave forcing for the simulation of the snowpack, which during the spring melt affects runoff, meltwater infiltration, and subsequent evapotranspiration. A greater sensitivity of the ablation, compared to the accumulation, of the winter snowpack to the choice of snow parameterization is found. Sensitivity simulations starting at prescribed conditions with no spinup demonstrate that the treatment of frozen soil (moisture) processes can affect the long-term variability of the models. The single-year recursive runs show large biases, compared to the corresponding year of the control run, that can persist through the entire year and underscore the importance of performing multiyear simulations.

Probabilistic Quantitative Precipitation Estimation in Complex Terrain

Abstract This paper describes a flexible method to generate ensemble gridded fields of precipitation in complex terrain. The method is based on locally weighted regression, in which spatial attributes from station locations are used as explanatory variables to predict spatial variability in precipitation. For each time step, regression models are used to estimate the conditional cumulative distribution function (cdf) of precipitation at each grid cell (conditional on daily precipitation totals from a sparse station network), and ensembles are generated by using realizations from correlated random fields to extract values from the gridded precipitation cdfs. Daily high-resolution precipitation ensembles are generated for a 300 km × 300 km section of western Colorado (dx = 2 km) for the period 1980–2003. The ensemble precipitation grids reproduce the climatological precipitation gradients and observed spatial correlation structure. Probabilistic verification shows that the precipitation estimates are reliab...

Effects of frozen soil on soil temperature, spring infiltration, and runoff: results from the PILPS 2(d) experiment at Valdai, Russia

The Project for Intercomparison of Land-Surface Parameterization Schemes phase 2(d) experiment at Valdai, Russia, offers a unique opportunity to evaluate land surface schemes, especially snow and frozen soil parameterizations. Here, the ability of the 21 schemes that participated in the experiment to correctly simulate the thermal and hydrological properties of the soil on several different timescales was examined. Using observed vertical profiles of soil temperature and soil moisture, the impact of frozen soil schemes in the land surface models on the soil temperature and soil moisture simulations was evaluated. It was found that when soil-water freezing is explicitly included in a model, it improves the simulation of soil temperature and its variability at seasonal and interannual scales. Although change of thermal conductivity of the soil also affects soil temperature simulation, this effect is rather weak. The impact of frozen soil on soil moisture is inconclusive in this experiment due to the particular climate at Valdai, where the top 1mo fsoil is very close to saturation during winter and the range for soil moisture changes at the time of snowmelt is very limited. The results also imply that inclusion of explicit snow processes in the models would contribute to substantially improved simulations. More sophisticated snow models based on snow physics tend to produce better snow simulations, especially of snow ablation. Hysteresis of snowcover fraction as a function of snow depth is observed at the catchment but not in any of the models.

Diagnosing Present and Future Permafrost from Climate Models

AbstractPermafrost is a characteristic aspect of the terrestrial Arctic and the fate of near-surface permafrost over the next century is likely to exert strong controls on Arctic hydrology and biogeochemistry. Using output from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), the authors assess its ability to simulate present-day and future permafrost. Permafrost extent diagnosed directly from each climate models soil temperature is a function of the modeled surface climate as well as the ability of the land surface model to represent permafrost physics. For each CMIP5 model these two effects are separated by using indirect estimators of permafrost driven by climatic indices and compared to permafrost extent directly diagnosed via soil temperatures. Several robust conclusions can be drawn from this analysis. Significant air temperature and snow depth biases exist in some models climates, which degrade both directly and indirectly diagnosed permafrost conditions. The range of directl...

Simulation of Present-Day and Future Permafrost and Seasonally Frozen Ground Conditions in CCSM4

The representation of permafrost and seasonally frozen ground and their projected twenty-first century trends is assessed in the Community Climate System Model, version 4 (CCSM4) and the Community Land Model version 4 (CLM4). The combined impact of advances in CLM and a better Arctic climate simulation, especially for air temperature, improve the permafrost simulation in CCSM4 compared to CCSM3. Present-day continuousplus discontinuous permafrost extent is comparable to that observed [12.5 3 10 6 versus (11.8‐14.6) 3 10 6 km 2 ],but active-layer thickness (ALT) is generallytoo thick and deep ground (.15 m) temperatures are too warm in CCSM4. Present-day seasonally frozen ground area is well simulated (47.5 3 10 6 versus 48.1 3 10 6 km 2 ). ALT and deep ground temperatures are much better simulated in offline CLM4 (i.e., forced with observed climate), which indicates that the remaining climate biases, particularly excessive high-latitude snowfall biases, degrade the CCSM4 permafrost simulation. Near-surface permafrost (NSP) and seasonally frozen ground (SFG) area are projected to decline substantially during the twenty-first century [representative concentration projections (RCPs); RCP8.5: NSP by 9.0 3 10 6 km 2 , 72%, SFG by 7.1 3 10 6 , 15%; RCP2.6: NSP by 4.1 3 10 6 , 33%, SFG by 2.1 3 10 6 , 4%]. The permafrost degradation rate is slower (2000‐50) than in CCSM3 by ;35% because of the improved soil physics. Under the low RCP2.6 emissions pathway, permafrost state stabilizes by 2100, suggesting that permafrost related feedbacks could be minimized if greenhouse emissions could be reduced. The trajectory of permafrost degradation is affected by CCSM4 climate biases. In simulations with this climate bias ameliorated, permafrost degradation in RCP8.5 is lower by ;29%. Further reductions of Arctic climate biases will increase the reliability of permafrost projections and feedback studies in earth system models.

Development and testing of polar WRF. Part III: Arctic land

A version of the state-of-the-art Weather Research and Forecasting model (WRF) has been developed for use in polar climates. The model known as ‘‘Polar WRF’’ is tested for land areas with a western Arctic grid thathas25-kmresolution.Thisworkservesaspreparationforthehigh-resolutionArcticSystemReanalysisof the years 2000‐10. The model is based upon WRF version 3.0.1.1, with improvements to the Noah land surface model and snow/ice treatment. Simulations consist of a series of 48-h integrations initialized daily at 0000 UTC, with the initial 24 h taken as spinup for atmospheric hydrology and boundary layer processes. Soil

Permafrost thaw and resulting soil moisture changes regulate projected high-latitude CO2 and CH4 emissions

The fate of currently frozen permafrost carbon as high-latitude climate warms remains highly uncertain and existing models give widely varying estimates of the permafrost carbon-climate feedback. This uncertainty is due to many factors, including the role that permafrost thaw-induced transitions in soil hydrologic conditions will have on organic matter decomposition rates and the proportion of aerobic to anaerobic respiration. Large-scale permafrost thaw, as predicted by the Community Land Model (CLM) under an unmitigated greenhouse gas emissions scenario, results in significant soil drying due to increased drainage following permafrost thaw, even though permafrost domain water inputs are projected to rise (net precipitation minus evaporation >0). CLM predicts that drier soil conditions will accelerate organic matter decomposition, with concomitant increases in carbon dioxide (CO2) emissions. Soil drying, however, strongly suppresses growth in methane (CH4) emissions. Considering the global warming potential (GWP) of CO2 and CH4 emissions together, soil drying weakens the CLM projected GWP associated with carbon fluxes from the permafrost zone by more than 50% compared to a non-drying case. This high sensitivity to hydrologic change highlights the need for better understanding and modeling of landscape-scale changes in soil moisture conditions in response to permafrost thaw in order to more accurately assess the potential magnitude of the permafrost carbon-climate feedback.

Arctic System Reanalysis: Call for Community Involvement

Arctic climate encompasses multiple feedbacks, the most important of which is the ice-albedo feedback. Enhanced Arctic changes, first recognized in the nineteenth century, increasingly are being observed across terrestrial, oceanic, atmospheric, and human systems, inspiring interdisciplinary research efforts, including the Study of Environmental Arctic Change (SEARCH) program, to understand the nature and future development of the Arctic system. In response to the need for enhanced understanding outlined in the 2005 SEARCH Implementation Plan [Arctic Research Consortium of the United States, 2005], an ongoing Arctic System Reanalysis (ASR) project builds on previous programs to observe the Arctic climate. The ASR is a multi-institutional, interdisciplinary collaboration that optimally merges measurements and modeling to provide a high-resolution description of the regions atmosphere/sea ice/land system by assimilating a diverse suite of observations into a regional model. The project builds upon lessons learned from past reanalyses by optimizing model physics parameterizations and methods of data assimilation for Arctic conditions. The ASR, which is a partnership with the broader Arctic research community, represents a synthesis tool for assessing and monitoring variability and change in the Arctic system.