Technical challenges and solutions in representing lakes when using WRF in downscaling applications

Mallard, Megan S.; Nolte, Christopher G.; Spero, Tanya L.; Bullock, O. Russell; Alapaty, Kiran; Herwehe, Jerold A.; Gula, Jonathan; Bowden, Jared H.

doi:10.5194/gmd-8-1085-2015

Cited by 39 publications

(42 citation statements)

References 40 publications

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“…As a result, water temperatures from the north coast are used, which are too cold and lead to nonphysical temperature discontinuities. The adverse effect of spatially interpolating water temperatures when lakes are absent in the large-scale driving data is documented in Mallard et al (2015). (4) Warm summer bias in heavily urbanised river valleys like the Rhine river valley between the Black Forest and Taunus, the Cologne Lowlands, the Ruhr region, and also around some cities like Munich, Stuttgart, Nuremberg, Dresden, Cottbus, as well as the foreign cities of Salzburg and Strasbourg (Figs.…”

Section: Discussionmentioning

confidence: 99%

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Field significance of performance measures in the context of regional climate model evaluation. Part 1: temperature

Ivanov

Warrach‐Sagi

Wulfmeyer

2017

Theor Appl Climatol

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A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as "field" or "global" significance. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 • grid resolution. Monthly temperature climatology for the 1990-2009 period is analysed for Germany for winter and summer in comparison with highresolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. In winter and in most regions in summer, the downscaled distributions are statistically indistinguishable from the observed ones. A systematic cold summer bias occurs in deep river valleys due to overestimated elevations, in coastal areas due probably to enhanced sea breeze circulation, and over large lakes due to the interpolation of water temperatures. Urban areas in concave topography forms have a warm summer bias due to the strong heat islands, not reflected in the observations. WRF-NOAH generates appropriate fine-scale features in the monthly temperature field over regions of complex topography, but over spatially homogeneous areas even small biases can lead to significant deteriorations relative to the driving reanalysis. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.

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Section: Discussionmentioning

confidence: 99%

“…They are derived from monthly means of surface air temperature at stations of the DWD network and have a spatial resolution of 1 km (Maier and Müller-Westermeier 2010).…”

Section: Datamentioning

confidence: 99%

Field significance of performance measures in the context of regional climate model evaluation. Part 1: temperature

Ivanov

Warrach‐Sagi

Wulfmeyer

2017

Theor Appl Climatol

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“…Most WRF implementations do not simulate time‐varying water body surface temperatures which affect evaporation, and hence the overlying atmospheric moisture profile. The CLM lake model, included in WRF version 3.6, requires multiyear spin‐up (Bullock et al, ; Mallard et al, , ), which is too computationally intensive for the high spatial resolutions we used. As a default, WRF initializes lakes to have surface temperatures that match nearby coastal sea surface temperature (SST), and in its absence, SST and lake temperatures are initialized with surface air temperature values.…”

Section: Methodsmentioning

confidence: 99%

Spatial Variability of Wet Troposphere Delays Over Inland Water Bodies

2017

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Satellite radar altimetry has enabled the study of water levels in large lakes and reservoirs at a global scale. The upcoming Surface Water and Ocean Topography (SWOT) satellite mission (scheduled launch 2020) will simultaneously measure water surface extent and elevation at an unprecedented accuracy and resolution. However, SWOT retrieval accuracy will be affected by a number of factors, including wet tropospheric delay—the delay in the signal's passage through the atmosphere due to atmospheric water content. In past applications, the wet tropospheric delay over large inland water bodies has been corrected using atmospheric moisture profiles based on atmospheric reanalysis data at relatively coarse (tens to hundreds of kilometers) spatial resolution. These products cannot resolve subgrid variations in wet tropospheric delays at the spatial resolutions (of 1 km and finer) that SWOT is intended to resolve. We calculate zenith wet tropospheric delays (ZWDs) and their spatial variability from Weather Research and Forecasting (WRF) numerical weather prediction model simulations at 2.33 km spatial resolution over the southwestern U.S., with attention in particular to Sam Rayburn, Ray Hubbard, and Elephant Butte Reservoirs which have width and length dimensions that are of order or larger than the WRF spatial resolution. We find that spatiotemporal variability of ZWD over the inland reservoirs depends on climatic conditions at the reservoir location, as well as distance from ocean, elevation, and surface area of the reservoir, but that the magnitude of subgrid variability (relative to analysis and reanalysis products) is generally less than 10 mm.

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“…Previous downscaling studies using much coarser horizontal resolution with fully parameterized convection revealed that using a lake model and accounting for lake depth can produce better precipitation for some regions (e.g., Small et al, ), while for others 2‐m temperatures and frozen fractions of lakes have improved at the cost of enhanced wet bias in precipitation (e.g., Mallard et al, ). Using the skin temperatures from the driver ESM may lead to erroneous lake temperatures depending on lake depth because the approach does not account for the warming and cooling time of the lakes (e.g., Mallard et al, ). Nevertheless, we are taking advantage of the much higher resolution and convection‐permitting nature of our WRF simulations and the fact that we are using monthly averaged skin temperatures from the driver data over lakes.…”

Section: Comparison Of Projected Future Changes Between Cesm and Wrf mentioning

confidence: 99%

High‐Resolution Climate Projections for the Northeastern United States Using Dynamical Downscaling at Convection‐Permitting Scales

Kömürcü

Emanuel

Huber

et al. 2018

Earth and Space Science

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To paraphrase former Speaker of the House Tip O'Neill, “All climate change is local”—that is, society reacts most immediately to changes in local weather such as regional heat waves and heavy rainstorms. Such phenomena are not well resolved by the current generation of coupled climate models. Here it is shown that dynamical downscaling of climate reanalyses using a high‐resolution regional model can reproduce both the means and extremes of temperature and precipitation as observed in the well‐measured northeastern United States. Given this result, the downscaling is applied to climate projections for the middle and end of the 21st century under Representative Concentration Pathway (RCP) 8.5 as well as for the historical time period to help assess regional climate impacts in the northeastern United States. The resulting high‐resolution projections are intended to support regional sustainability studies for the northeastern United States and are made publicly available.

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Technical challenges and solutions in representing lakes when using WRF in downscaling applications

Cited by 39 publications

References 40 publications

Field significance of performance measures in the context of regional climate model evaluation. Part 1: temperature

Field significance of performance measures in the context of regional climate model evaluation. Part 1: temperature

Spatial Variability of Wet Troposphere Delays Over Inland Water Bodies

High‐Resolution Climate Projections for the Northeastern United States Using Dynamical Downscaling at Convection‐Permitting Scales

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