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Xubin Zeng
,
Michael Barlage
,
Robert E. Dickinson
,
Yongjiu Dai
,
Guiling Wang
, and
Keith Oleson

Abstract

In arid and semiarid regions most of the solar radiation penetrates through the canopy and reaches the ground, and hence the turbulent exchange coefficient under canopy Cs becomes important. The use of a constant Cs that is only appropriate for thick canopies is found to be primarily responsible for the excessive warm bias of around 10 K in monthly mean ground temperature over these regions in version 2 of the Community Climate System Model (CCSM2). New Cs formulations are developed for the consistent treatment of undercanopy turbulence for both thick and thin canopies in land models, and provide a preliminary solution of this problem.

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Keith M. Hines
,
David H. Bromwich
,
Le-Sheng Bai
,
Michael Barlage
, and
Andrew G. Slater

Abstract

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 that has 25-km resolution. This work serves as preparation for the high-resolution Arctic System Reanalysis of 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 temperature and moisture that have a much slower spinup than the atmosphere are cycled from 48-h output of earlier runs. Arctic conditions are simulated for a winter-to-summer seasonal cycle from 15 November 2006 to 1 August 2007. Simulation results are compared with a variety of observations from several Alaskan sites, with emphasis on the North Slope. Polar WRF simulation results show good agreement with most near-surface observations. Warm temperature biases are found for winter and summer. A sensitivity experiment with reduced soil heat conductivity, however, improves simulation of near-surface temperature, ground heat flux, and soil temperature during winter. There is a marked deficit in summer cloud cover over land with excessive incident shortwave radiation. The cloud deficit may result from anomalous vertical mixing of moisture by the turbulence parameterization. The new snow albedo parameterization for WRF 3.1.1 is successfully tested for snowmelt over the North Slope of Alaska.

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Andrew J. Monaghan
,
Michael Barlage
,
Jennifer Boehnert
,
Cody L. Phillips
, and
Olga V. Wilhelmi

Abstract

There is growing use of limited-area models (LAMs) for high-resolution (<10 km) applications, for which consistent mapping of input terrestrial and meteorological datasets is critical for accurate simulations. The geographic coordinate systems of most input datasets are based on spheroid-shaped (i.e., elliptical) Earth models, while LAMs generally assume a perfectly sphere-shaped Earth. This distinction is often neglected during preprocessing, when input data are remapped to LAM domains, leading to geolocation discrepancies that can exceed 20 km at midlatitudes.

A variety of terrestrial (topography and land use) input dataset configurations is employed to explore the impact of Earth model assumptions on a series of 1-km LAM simulations over Colorado. For the same terrestrial datasets, the ~20-km geolocation discrepancy between spheroidal-versus-spherical Earth models over the domain leads to simulated differences in near-surface and midtropospheric air temperature, humidity, and wind speed that are larger and more widespread than those due to using different topography and land use datasets altogether but not changing the Earth model. Simulated differences are caused by the shift of static fields with respect to boundary conditions, and altered Coriolis forcing and topographic gradients.

The sensitivity of high-resolution LAM simulations to Earth model assumptions emphasizes the importance for users to ensure terrestrial and meteorological input data are consistently mapped during preprocessing (i.e., datasets share a common geographic coordinate system before remapping to the LAM domain). Concurrently, the modeling community should update preprocessing systems to make sure input data are correctly mapped for all global and limited-area simulation domains.

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Lu Su
,
Qian Cao
,
Mu Xiao
,
David M. Mocko
,
Michael Barlage
,
Dongyue Li
,
Christa D. Peters-Lidard
, and
Dennis P. Lettenmaier

Abstract

We examine the drought variability over the conterminous United States (CONUS) for 1915–2018 using the Noah-MP land surface model. We examine different model options on drought reconstruction, including optional representation of groundwater and dynamic vegetation phenology. Over our 104-yr reconstruction period, we identify 12 great droughts that each covered at least 36% of CONUS and lasted for at least 5 months. The great droughts tend to have smaller areas when groundwater and/or dynamic vegetation are included in the model configuration. We detect a small decreasing trend in dry area coverage over CONUS in all configurations. We identify 45 major droughts in the baseline (with a dry area coverage greater than 23.6% of CONUS) that are, on average, somewhat less severe than great droughts. We find that representation of groundwater tends to increase drought duration for both great and major droughts, primarily by leading to earlier drought onset (some due to short-lived recovery from a previous drought) or later demise (groundwater anomalies lag precipitation anomalies). In contrast, representation of dynamic vegetation tends to shorten major droughts duration, primarily due to earlier drought demise (closed stoma or dead vegetation reduces ET loss during droughts). On a regional basis, the U.S. Southwest (Southeast) has the longest (shortest) major drought durations. Consistent with earlier work, dry area coverage in all subregions except the Southwest has decreased. The effects of groundwater and dynamic vegetation vary regionally due to differences in groundwater depths (hence connectivity with the surface) and vegetation types.

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Fei Chen
,
Guo Zhang
,
Michael Barlage
,
Ying Zhang
,
Jeffrey A. Hicke
,
Arjan Meddens
,
Guangsheng Zhou
,
William J. Massman
, and
John Frank

Abstract

Bark beetle outbreaks have killed billions of trees and affected millions of hectares of forest during recent decades. The objective of this study was to quantify responses of surface energy and hydrologic fluxes 2–3 yr following a spruce beetle outbreak using measurements and modeling. The authors used observations at the Rocky Mountains Glacier Lakes Ecosystem Experiments Site (GLEES), where beetles killed 85% of the basal area of spruce from 2005–07 (prebeetle) to 2009/10 (postbeetle). Observations showed increased albedo following tree mortality, more reflected solar radiation, and less net radiation, but these postoutbreak radiation changes are smaller than or comparable to their annual preoutbreak variability. The dominant signals from observations were a large reduction (27%) in summer daytime evaporation and a large increase (25%) in sensible heat fluxes. Numerous Noah LSM with multiparameterization options (Noah-MP) simulations incorporating beetle-caused tree mortality effects were conducted to assess their impact on the surface hydrological cycle components that were not directly observed. Model results revealed substantial seasonal variations: more spring snowmelt and runoff, less spring–summer transpiration, and drier soil in summer and fall. This modeled trend is similar to observed runoff changes in harvested forests where reduced forest density resulted in more spring snowmelt and annual water yields. Model results showed that snow albedo changes due to increased litter cover beneath killed trees altered the seasonal pattern of simulated snowmelt and snow water equivalent, but these changes are small compared to the effect of leaf loss. This study highlights the need to include the transient effects of forest disturbances in modeling land–atmosphere interactions and their potential impacts on regional weather and climate.

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Andrew J. Monaghan
,
Martyn P. Clark
,
Michael P. Barlage
,
Andrew J. Newman
,
Lulin Xue
,
Jeffrey R. Arnold
, and
Roy M. Rasmussen

Abstract

Weather and climate variability strongly influence the people, infrastructure, and economy of Alaska. However, the sparse observational network in Alaska limits our understanding of meteorological variability, particularly of precipitation processes that influence the hydrologic cycle. Here, a new 14-yr (September 2002–August 2016) dataset for Alaska with 4-km grid spacing is described and evaluated. The dataset, generated with the Weather Research and Forecasting (WRF) Model, is useful for gaining insight into meteorological and hydrologic processes, and provides a baseline against which to measure future environmental change. The WRF fields are evaluated at annual, seasonal, and daily time scales against observation-based gridded and station records of 2-m air temperature, precipitation, and snowfall. Pattern correlations between annual mean WRF and observation-based gridded fields are r = 0.89 for 2-m temperature, r = 0.75 for precipitation, r = 0.82 for snow-day fraction, r = 0.55 for first snow day of the season, and r = 0.71 for last snow day of the season. A shortcoming of the WRF dataset is that spring snowmelt occurs too early over a majority of the state, due partly to positive 2-m temperature biases in winter and spring. Strengths include an improved representation of the interannual variability of 2-m temperature and precipitation and accurately simulated (relative to regional station observations) winter and summer precipitation maxima. This initial evaluation suggests that the 4-km WRF climate dataset robustly simulates meteorological processes and recent climatic variability in Alaska. The dataset may be particularly useful for applications that require high-temporal-frequency weather fields, such as driving hydrologic or glacier models. Future studies will provide further insight on its ability to represent other aspects of Alaska’s climate.

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Christa D. Peters-Lidard
,
David M. Mocko
,
Lu Su
,
Dennis P. Lettenmaier
,
Pierre Gentine
, and
Michael Barlage

Abstract

Millions of people across the globe are affected by droughts every year, and recent droughts have highlighted the considerable agricultural impacts and economic costs of these events. Monitoring the state of droughts depends on integrating multiple indicators that each capture particular aspects of hydrologic impact and various types and phases of drought. As the capabilities of land surface models and remote sensing have improved, important physical processes such as dynamic, interactive vegetation phenology, groundwater, and snowpack evolution now support a range of drought indicators that better reflect coupled water, energy, and carbon cycle processes. In this work, we discuss these advances, including newer classes of indicators that can be applied to improve the characterization of drought onset, severity, and duration. We utilize a new model-based drought reconstruction to illustrate the role of dynamic phenology and groundwater in drought assessment. Further, through case studies on flash droughts, snow droughts, and drought recovery, we illustrate the potential advantages of advanced model physics and observational capabilities, especially from remote sensing, in characterizing droughts.

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Daniel F. Steinhoff
,
Andrew J. Monaghan
,
Lars Eisen
,
Michael J. Barlage
,
Thomas M. Hopson
,
Isaac Tarakidzwa
,
Karielys Ortiz-Rosario
,
Saul Lozano-Fuentes
,
Mary H. Hayden
,
Paul E. Bieringer
, and
Carlos M. Welsh Rodríguez

Abstract

The mosquito virus vector Aedes (Ae.) aegypti exploits a wide range of containers as sites for egg laying and development of the immature life stages, yet the approaches for modeling meteorologically sensitive container water dynamics have been limited. This study introduces the Water Height and Temperature in Container Habitats Energy Model (WHATCH’EM), a state-of-the-science, physically based energy balance model of water height and temperature in containers that may serve as development sites for mosquitoes. The authors employ WHATCH’EM to model container water dynamics in three cities along a climatic gradient in México ranging from sea level, where Ae. aegypti is highly abundant, to ~2100 m, where Ae. aegypti is rarely found. When compared with measurements from a 1-month field experiment in two of these cities during summer 2013, WHATCH’EM realistically simulates the daily mean and range of water temperature for a variety of containers. To examine container dynamics for an entire season, WHATCH’EM is also driven with field-derived meteorological data from May to September 2011 and evaluated for three commonly encountered container types. WHATCH’EM simulates the highly nonlinear manner in which air temperature, humidity, rainfall, clouds, and container characteristics (shape, size, and color) determine water temperature and height. Sunlight exposure, modulated by clouds and shading from nearby objects, plays a first-order role. In general, simulated water temperatures are higher for containers that are larger, darker, and receive more sunlight. WHATCH’EM simulations will be helpful in understanding the limiting meteorological and container-related factors for proliferation of Ae. aegypti and may be useful for informing weather-driven early warning systems for viruses transmitted by Ae. aegypti.

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Pablo A. Mendoza
,
Martyn P. Clark
,
Naoki Mizukami
,
Andrew J. Newman
,
Michael Barlage
,
Ethan D. Gutmann
,
Roy M. Rasmussen
,
Balaji Rajagopalan
,
Levi D. Brekke
, and
Jeffrey R. Arnold

Abstract

The assessment of climate change impacts on water resources involves several methodological decisions, including choices of global climate models (GCMs), emission scenarios, downscaling techniques, and hydrologic modeling approaches. Among these, hydrologic model structure selection and parameter calibration are particularly relevant and usually have a strong subjective component. The goal of this research is to improve understanding of the role of these decisions on the assessment of the effects of climate change on hydrologic processes. The study is conducted in three basins located in the Colorado headwaters region, using four different hydrologic model structures [PRMS, VIC, Noah LSM, and Noah LSM with multiparameterization options (Noah-MP)]. To better understand the role of parameter estimation, model performance and projected hydrologic changes (i.e., changes in the hydrology obtained from hydrologic models due to climate change) are compared before and after calibration with the University of Arizona shuffled complex evolution (SCE-UA) algorithm. Hydrologic changes are examined via a climate change scenario where the Community Climate System Model (CCSM) change signal is used to perturb the boundary conditions of the Weather Research and Forecasting (WRF) Model configured at 4-km resolution. Substantial intermodel differences (i.e., discrepancies between hydrologic models) in the portrayal of climate change impacts on water resources are demonstrated. Specifically, intermodel differences are larger than the mean signal from the CCSM–WRF climate scenario examined, even after the calibration process. Importantly, traditional single-objective calibration techniques aimed to reduce errors in runoff simulations do not necessarily improve intermodel agreement (i.e., same outputs from different hydrologic models) in projected changes of some hydrological processes such as evapotranspiration or snowpack.

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Roy Rasmussen
,
Changhai Liu
,
Kyoko Ikeda
,
David Gochis
,
David Yates
,
Fei Chen
,
Mukul Tewari
,
Michael Barlage
,
Jimy Dudhia
,
Wei Yu
,
Kathleen Miller
,
Kristi Arsenault
,
Vanda Grubišić
,
Greg Thompson
, and
Ethan Gutmann

Abstract

Climate 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 enhanced snowfall on the order of 10%–25% over the Colorado Headwaters region, with the enhancement being less in the core headwaters region due to the topographic reduction of precipitation upstream of the region (rain-shadow effect). The main climate change impacts are in the enhanced melting at the lower-elevation bound of the snowpack and the increased snowfall at higher elevations. The changes in peak snow mass are generally near zero due to these two compensating effects, and simulated wintertime total runoff is above current levels. The 1 April snow water equivalent (SWE) is reduced by 25% in the warmer climate, and the date of maximum SWE occurs 2–17 days prior to current climate results, consistent with previous studies.

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