Michael Barlage

The community Noah land surface model with multiparameterization options (Noah‐MP): 1. Model description and evaluation with local‐scale measurements

[1] This first paper of the two‐part series describes the objectives of the community efforts in improving the Noah land surface model (LSM), documents, through mathematical formulations, the augmented conceptual realism in biophysical and hydrological processes, and introduces a framework for multiple options to parameterize selected processes (Noah‐MP). The Noah‐MP’s performance is evaluated at various local sites using high temporal frequency data sets, and results show the advantages of using multiple optional schemes to interpret the differences in modeling simulations. The second paper focuses on ensemble evaluations with long‐term regional (basin) and global scale data sets. The enhanced conceptual realism includes (1) the vegetation canopy energy balance, (2) the layered snowpack, (3) frozen soil and infiltration, (4) soil moisture‐groundwater interaction and related runoff production, and (5) vegetation phenology. Sample local‐scale validations are conducted over the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site, the W3 catchment of Sleepers River, Vermont, and a French snow observation site. Noah‐MP shows apparent improvements in reproducing surface fluxes, skin temperature over dry periods, snow water equivalent (SWE), snow depth, and runoff over Noah LSM version 3.0. Noah‐MP improves the SWE simulations due to more accurate simulations of the diurnal variations of the snow skin temperature, which is critical for computing available energy for melting. Noah‐MP also improves the simulation of runoff peaks and timing by introducing a more permeable frozen soil and more accurate simulation of snowmelt. We also demonstrate that Noah‐MP is an effective research tool by which modeling results for a given process can be interpreted through multiple optional parameterization schemes in the same model framework.

High resolution coupled climate-runoff simulations of seasonal snowfall over Colorado: A process study of current and warmer climate

AbstractClimate change is expected to accelerate the hydrologic cycle, increase the fraction of precipitation that is rain, and enhance snowpack melting. The enhanced hydrological cycle is also expected to increase snowfall amounts due to increased moisture availability. These processes are examined in this paper in the Colorado Headwaters region through the use of a coupled high-resolution climate–runoff model. Four high-resolution simulations of annual snowfall over Colorado are conducted. The simulations are verified using Snowpack Telemetry (SNOTEL) data. Results are then presented regarding the grid spacing needed for appropriate simulation of snowfall. Finally, climate sensitivity is explored using a pseudo–global warming approach. The results show that the proper spatial and temporal depiction of snowfall adequate for water resource and climate change purposes can be achieved with the appropriate choice of model grid spacing and parameterizations. The pseudo–global warming simulations indicate enha...

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

Using MODIS BRDF and albedo data to evaluate global model land surface albedo

Abstract The land surface albedo in the NCAR Community Climate System Model (CCSM2) is calculated based on a two-stream approximation, which does not include the effect of three-dimensional vegetation structure on radiative transfer. The model albedo (including monthly averaged albedo, direct albedo at local noon, and the solar zenith angle dependence of albedo) is evaluated using the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) and albedo data acquired during July 2001–July 2002. The model monthly averaged albedos in February and July are close to the MODIS white-sky albedos (within 0.02 or statistically insignificant) over about 40% of the global land between 60°S and 70°N. However, CCSM2 significantly underestimates albedo by 0.05 or more over deserts (e.g., the Sahara Desert) and some semiarid regions (e.g., parts of Australia). The difference between the model direct albedo at local noon and the MODIS black-sky albedo for the near-infrar...

Are we unnecessarily constraining the agility of complex process-based models?

In this commentary we suggest that hydrologists and land-surface modelers may be unnecessarily constraining the behavioral agility of very complex physics-based models. We argue that the relatively poor performance of such models can occur due to restrictions on their ability to refine their portrayal of physical processes, in part because of strong a priori constraints in: (i) the representation of spatial variability and hydrologic connectivity, (ii) the choice of model parameterizations, and (iii) the choice of model parameter values. We provide a specific example of problems associated with strong a priori constraints on parameters in a land surface model. Moving forward, we assert that improving hydrological models requires integrating the strengths of the “physics-based” modeling philosophy (which relies on prior knowledge of hydrologic processes) with the strengths of the “conceptual” modeling philosophy (which relies on data driven inference). Such integration will accelerate progress on methods to define and discriminate among competing modeling options, which should be ideally incorporated in agile modeling frameworks and tested through a diagnostic evaluation approach.

Climate Change Impacts on the Water Balance of the Colorado Headwaters: High-Resolution Regional Climate Model Simulations

AbstractA high-resolution climate model (4-km horizontal grid spacing) is used to examine the following question: How will long-term changes in climate impact the partitioning of annual precipitation between evapotranspiration and runoff in the Colorado Headwaters?This question is examined using a climate sensitivity approach in which eight years of current climate is compared to a future climate created by modifying the current climate signal with perturbation from the NCAR Community Climate System Model, version 3 (CCSM3), model forced by the A1B scenario for greenhouse gases out to 2050. The current climate period is shown to agree well with Snowpack Telemetry (SNOTEL) surface observations of precipitation (P) and snowpack, as well as streamflow and AmeriFlux evapotranspiration (ET) observations. The results show that the annual evaporative fraction (ET/P) for the Colorado Headwaters is 0.81 for the current climate and 0.83 for the future climate, indicating increasing aridity in the future despite a p...

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.

Comparison of land-precipitation coupling strength using observations and models.

Abstract Numerous studies have attempted to address the land–precipitation coupling, but scientists’ understanding remains limited and discrepancies still exist from different studies. A new parameter Γ is proposed here to estimate the land–precipitation coupling strength based on the ratio of the covariance between monthly or seasonal precipitation and evaporation anomalies (from their climatological means) over the variance of precipitation anomalies. The Γ value is easy to compute and insensitive to the horizontal scales used; however, it does not provide causality. A relatively high Γ is a necessary—but not sufficient—condition for a relatively strong land–precipitation coupling. A computation of Γ values using two global reanalyses (ECMWF and NCEP), one regional reanalysis [North American Regional Reanalysis (NARR)], and observed precipitation along with Variable Infiltration Capacity (VIC)-derived evaporation data indicates that the land–precipitation coupling is stronger in summer and weaker in win...

Impacts of Climate Change and Land Use Change on Runoff from a Great Lakes Watershed

Abstract Daily VEMAP output from the Hadley Coupled Climate Model (HadCM2) and land use projections from the Southeastern Michigan Council of Governments are used to examine the impacts of climate change and land use change on a regional watershed in southeastern lower Michigan. The precipitation, temperature, moisture, and solar radiation output from HadCM2 are processed before they are used as input to a modified version of the Biosphere-Atmosphere Transfer Scheme (BATS). The modified BATS model (BATS/HYDRO) includes the original 18 BATS land use types along with six new urban land classes as well as an improved surface runoff model, which accounts for impervious surfaces and depression storage. The daily VEMAP output is verified against observations and shown to be appropriate for use as input to the BATS/HYDRO model. The BATS/HYDRO model is then tested with observed NCEP/NCAR Reanalysis Data and shown to reproduce observed runoff for the period 1990 to 1992 with minimal tuning of initial soil moisture content and daily rainfall distribution. The BATS/HYDRO model is then run using VEMAP output as input for two time periods, 1994 to 2003 and 2090 to 2099 and two land use scenarios, current and future. Model results show that changing climate and changing land use will increase the percentage of precipitation that results in surface runoff from 17.1% to 21.4%. This 4.3% increase is partitioned into a 2.5% increase due to climate change and a 1.6% increase due to land use change.

Sensitivity of the NCEP/Noah land surface model to the MODIS green vegetation fraction data set

[1] Land surface processes are strongly controlled by vegetation cover. Current land surface models represent vegetation as a combination of leaf area index (LAI) and green vegetation fraction (GVF) parameters. The purpose of the study is to examine the impact of a spatially and temporally detailed Moderate Resolution Imaging Spectroradiometer (MODIS)-based GVF on surface processes in the NCEP Noah land surface model. The largest differences between the GVF data set currently used by the Noah model and the new MODIS GVF data set occur in winter and for tree-dominated vegetation classes. The greatest impact of the new GVF data on the surface energy and water balance is seen during the summer, when the transpiration is increased by more than 10 W/m 2 on average for most vegetation types and the July averaged daily transpiration rate is increased by up to 50 W/m 2 for evergreen needleleaf sites.