Mesoscale convective complexes in regional climate modeling and increased extreme precipitation due to agricultural landuse change over the central U.S.

This study investigates the role of agricultural landuse change in the observed increase in extreme precipitation during the 20th century. Landuse input was constructed for the Community Land Model in the WRF-ARW using county-level planting data from the USDA for two periods: the 1940s and 2010. Crops were separated into small grains, winter wheat, soybean, and maize. When simulations are run using these two land datasets for the 1949-2010 period, the 2010 landuse has higher frequencies of extreme precipitation above 24-26 mm day-1 or 1 in day-1. This indicates that cropland shifts in the 20th century when society shifted from large oat cultivation to feed work animals on traditional farms to soybean and maize following the industrial revolution have contributed to increases in extreme precipitation across the central U.S. Additionally, this study makes simple changes to convective parameterizations to allow grid-scale microphysics to have a larger role in producing precipitation with the goal of improving MCC production. Using an objective MCC detection algorithm that uses only the precipitation field, no scheme is presented as the best performer, although modifications we made performed on par with unmodified schemes. We also examined MCC production and trends in the Can-RCM4, CRCM5, HIRHAM, and RCA4 models under the CORDEX framework. Although trends were found within each model, variation was large among models; this reinforces the importance of considering variability in regional climate modeling when analyzing or forecasting hydrologic trends

Mesoscale convective complexes in regional climate modeling and increased extreme precipitation due to agricultural landuse change over the central U.S.

This study investigates the role of agricultural landuse change in the observed increase in extreme precipitation during the 20th century. Landuse input was constructed for the Community Land Model in the WRF-ARW using county-level planting data from the USDA for two periods: the 1940s and 2010. Crops were separated into small grains, winter wheat, soybean, and maize. When simulations are run using these two land datasets for the 1949-2010 period, the 2010 landuse has higher frequencies of extreme precipitation above 24-26 mm day-1 or 1 in day-1. This indicates that cropland shifts in the 20th century when society shifted from large oat cultivation to feed work animals on traditional farms to soybean and maize following the industrial revolution have contributed to increases in extreme precipitation across the central U.S. Additionally, this study makes simple changes to convective parameterizations to allow grid-scale microphysics to have a larger role in producing precipitation with the goal of improving MCC production. Using an objective MCC detection algorithm that uses only the precipitation field, no scheme is presented as the best performer, although modifications we made performed on par with unmodified schemes. We also examined MCC production and trends in the Can-RCM4, CRCM5, HIRHAM, and RCA4 models under the CORDEX framework. Although trends were found within each model, variation was large among models; this reinforces the importance of considering variability in regional climate modeling when analyzing or forecasting hydrologic trends.</p

Coupling Fast All-season Soil Strength Land Surface Model with Weather Research and Forecasting Model to Assess Low-level Icing in Complex Terrain.

Icing poses as a severe hazard to aircraft safety with financial resources and even human lives hanging in the balance when the decision to ground a flight must be made. When analyzing the effects of ice on aviation, a chief cause for danger is the disruption of smooth airflow, which increases the drag force on the aircraft therefore decreasing its ability to create lift. The Weather Research and Forecast (WRF) model Advanced Research WRF (WRF-ARW) is a collaboratively created, flexible model designed to run on distributed computing systems for a variety of applications including forecasting research, parameterization research, and real-time numerical weather prediction. Land-surface models, one of the physics options available in the WRF-ARW, output surface heat and moisture flux given radiation, precipitation, and surface properties such as soil type. They are a critical component of forecast models because they serve as a link between water sources and the atmosphere through surface and groundwater. The Fast All-Season Soil STrength (FASST) land-surface model was developed by the U.S. Army ERDC-CRREL in Hanover, New Hampshire. Originally, FASST was intended for the military purpose of providing information to mobility and sensor performance algorithms, but has since been utilized in civilian applications and research. Designed to use both meteorological and terrain data, the model calculates heat and moisture within the surface layer as well as the exchange of these parameters between the soil, surface elements (such as snow and vegetation), and atmosphere. Focusing on the Presidential Mountain Range of New Hampshire under the NASA Experimental Program to Stimulate Competitive Research (EPSCoR) Icing Assessments in Cold and Alpine Environments project, one of the main goals is to create a customized, high resolution model to predict and assess ice accretion in complex terrain. The purpose of this research is to couple the FASST land-surface model with the WRF to improve icing forecasts in complex terrain. Coupling FASST with the WRF-ARW may improve icing forecasts because of its sophisticated approach to handling processes such as meltwater, freezing, thawing, and others that would affect the water and energy budget and in turn affect icing forecasts. Several transformations had to take place in order for the FASST land-surface model and WRF-ARW to work together as fully coupled models. Changes had to be made to the WRF-ARW build mechanisms (Chapter 1, section a) so that FASST would be recognized as a new option that could be chosen through the namelist and compiled with other modules. Similarly, FASST had to be altered to no longer read meteorological data from a file, but accept input from WRF-ARW at each time step in a way that did not alter the integrity or run-time processes of the model. Several icing events were available to test the newly coupled model as well as the performance of other available land-surface models from the WRF-ARW. A variation of event intensities and durations from these events were chosen to give a broader view of the land-surface models‟ abilities to accurately predict icing in complex terrain. Non-icing events were also used in testing to ensure the land-surface models were not predicting ice in the events where none occurred. Four locations were chosen across the modeled domain: Mount Washington (NH), Plymouth (NH), Waterville (ME), and Bennington (VT) to evaluate the 2m temperature results that the WRF-ARW produced using the FASST, Noah, RUC(Rapid Update Cycle), and Thermal Diffusion land-surface models. When compared to the other land-surface models and observations FASST showed a warm bias in several regions. As the forecasts progressed, FASST appeared to attempt to correct this bias and performed similarly to the other land-surface models and at times better than these land-surface models in areas of the domain not affected by this bias. To correct this warm bias, future investigation should be conducted into the reasoning behind this warm bias, including but not limited to: FASST operation and elevation modeling, WRF-ARW variables and forecasting methods, as well as allowing for spin-up prior to forecast times. Following the correction to the warm bias, FASST can be parallelized to allow for operational forecast performance and included in the WRF-ARW forecasting suite for future software releases

Assessing mean climate change signals in the global CORDEX-CORE ensemble

The new Coordinated Output for Regional Evaluations (CORDEX-CORE) ensemble provides high-resolution, consistent regional climate change projections for the major inhabited areas of the world. It serves as a solid scientific basis for further research related to vulnerability, impact, adaptation and climate services in addition to existing CORDEX simulations. The aim of this study is to investigate and document the climate change information provided by the CORDEX-CORE simulation ensemble, as a part of the World Climate Research Programme (WCRP) CORDEX community. An overview of the annual and monthly mean climate change information in selected regions in different CORDEX domains is presented for temperature and precipitation, providing the foundation for detailed follow-up studies and applications. Initially, two regional climate models (RCMs), REMO and RegCM were used to downscale global climate model output. The driving simulations by AR5 global climate models (AR5-GCMs) were selected to cover the spread of high, medium, and low equilibrium climate sensitivity at a global scale. The CORDEX-CORE ensemble has doubled the spatial resolution compared to the previously existing CORDEX simulations in most of the regions (25[Formula: see text] (0.22[Formula: see text]) versus 50[Formula: see text] (0.44[Formula: see text])) leading to a potentially improved representation of, e.g., physical processes in the RCMs. The analysis focuses on changes in the IPCC physical climate reference regions. The results show a general reasonable representation of the spread of the temperature and precipitation climate change signals of the AR5-GCMs by the CORDEX-CORE simulations in the investigated regions in all CORDEX domains by mostly covering the AR5 interquartile range of climate change signals. The simulated CORDEX-CORE monthly climate change signals mostly follow the AR5-GCMs, although for specific regions they show a different change in the course of the year compared to the AR5-GCMs, especially for RCP8.5, which needs to be investigated further in region specific process studies