North American Regional Reanalysis Data Research Articles

Modelling glacier mass balance and climate sensitivity in the context of sparse observations: application to Saskatchewan Glacier, western Canada

Abstract. Glacier mass balance models are needed at sites with scarce long-term observations to reconstruct past glacier mass balance and assess its sensitivity to future climate change. In this study, North American Regional Reanalysis (NARR) data were used to force a physically based, distributed glacier mass balance model of Saskatchewan Glacier for the historical period 1979–2016 and assess its sensitivity to climate change. A 2-year record (2014–2016) from an on-glacier automatic weather station (AWS) and historical precipitation records from nearby permanent weather stations were used to downscale air temperature, relative humidity, wind speed, incoming solar radiation and precipitation from the NARR to the station sites. The model was run with fixed (1979, 2010) and time-varying (dynamic) geometry using a multitemporal digital elevation model dataset. The model showed a good performance against recent (2012–2016) direct glaciological mass balance observations as well as with cumulative geodetic mass balance estimates. The simulated mass balance was not very sensitive to the NARR spatial interpolation method, as long as station data were used for bias correction. The simulated mass balance was however sensitive to the biases in NARR precipitation and air temperature, as well as to the prescribed precipitation lapse rate and ice aerodynamic roughness lengths, showing the importance of constraining these two parameters with ancillary data. The glacier-wide simulated energy balance regime showed a large contribution (57 %) of turbulent (sensible and latent) heat fluxes to melting in summer, higher than typical mid-latitude glaciers in continental climates, which reflects the local humid “icefield weather” of the Columbia Icefield. The static mass balance sensitivity to climate was assessed for prescribed changes in regional mean air temperature between 0 and 7 ∘C and precipitation between −20 % and +20 %, which comprise the spread of ensemble Representative Concentration Pathway (RCP) climate scenarios for the mid (2041–2070) and late (2071–2100) 21st century. The climate sensitivity experiments showed that future changes in precipitation would have a small impact on glacier mass balance, while the temperature sensitivity increases with warming, from −0.65 to −0.93 m w.e. a−1 ∘C−1. The mass balance response to warming was driven by a positive albedo feedback (44 %), followed by direct atmospheric warming impacts (24 %), a positive air humidity feedback (22 %) and a positive precipitation phase feedback (10 %). Our study underlines the key role of albedo and air humidity in modulating the response of winter-accumulation type mountain glaciers and upland icefield-outlet glacier settings to climate.

Accuracy of WRF for prediction of operational wind farm data and assessment of influence of upwind farms on power production

Numerical Weather Prediction models such as Weather Research and Forecasting (WRF) are increasingly used to inform planning of both on-shore and off-shore wind-farms. This study aims to establish the accuracy with which WRF can be employed to predict the operational performance of a wind farm located in complex terrain and to assess the impact of nearby wind farms on farm energy production. Analysis is presented for a wind farm operating in complex terrain located in Baja California, Mexico. The WRF model initialized with the North American Regional Reanalysis (NARR) data provides time-variation and annual occurrence of wind speed with root mean square error (RMSE) 12% and 1.4%, using either the Rapid Update Cycle (RUC) or Pleim-Xu (PX) land-surface models. Energy supply from a 10 MW wind farm is predicted to within 5.25% (RUC) and 2% (PX) of measured data. The wind resource at several sites in the region offers the potential for wind farms with capacity factors in the range 32–34% without wake interaction. This reduces to 29% when wind turbine momentum extraction within the farm is modelled. The wakes of these farms are found to extend over 12 km downwind, with greater extent for larger-diameter 5 MW turbines than 2 MW turbines. These farm wakes reduce power output from a downwind farm by over 20% for wind speeds below rated speed resulting in 1.3–1.7% reduction in annual energy. This analysis of WRF accuracy establishes confidence in use of numerical weather prediction tools for wind farm operational data and informing use for wind farm siting in relation to nearby farms.

Flash drought identification from satellite-based land surface water index

Flash droughts can lead to significant agricultural and ecosystem impacts via rapid land surface desiccation. While gridded weather and climate datasets, land surface models, or widely spaced in situ observations are typically used to quantify flash drought development, coarse spatial data limits the ability to determine fine-scale spatial evolution of flash drought at landscape and ecosystem scales. In this study, a novel approach is introduced to objectively identify flash drought using the land surface water index (LSWI) derived from satellite observations. LSWI is a water-related vegetation index that represents the total water content in vegetation by using the near-infrared and shortwave infrared bands. LSWI was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance (MOD09A1) with a 500 m spatial resolution and an 8-day temporal resolution. When applied to two well-established case studies, LSWI anomalies were able to capture the temporal and spatial evolution of flash drought over Oklahoma during the years of 2011 and 2012. In addition, rapid changes of LSWI during flash drought were comparable across space and time to the reanalysis-based Standardized Evaporative Stress Ratio (SESR), while negative anomalies of LSWI following flash drought corresponded with drought impacts via the United States Drought Monitor. It was found that LSWI was able to identify flash drought with a finer spatial resolution (500 m) and revealed spatial propagation of flash drought events that would not otherwise be seen with coarser meteorological data (e.g., ∼32 km at the lowest latitude for the North American Regional Reanalysis data). Furthermore, LSWI greatly enhanced the ability to detect rapid changes in surface conditions driven by flash drought and was able to provide early warning for drought development when compared with the USDM across Oklahoma. As such, the temporal and spatial evolution of flash drought depicted by LSWI and the presented methodology improves our ability to identify flash drought at high spatial resolution using satellite remote sensing and detect rapid changes in surface conditions. In light of these results, the novel LSWI approach demonstrates that satellite remote sensing applications using an objective technique are advantageous for flash drought detection in near real-time and at fine spatial scales.

Climatology and Trends of Heat Index, Human Discomfort Index, and Energy Per Capita for CONUS and Meso-America

Abstract Air temperature and humidity affect human comfort and health in warm and humid climates, and consequently energy demands from buildings to maintain indoor comfort levels. An effective way to measure the overall effect of temperature and humidity on human comfort (or discomfort) is using the heat index (HI), a measure of how people “feel” when exposed to warm and humid environments. This research aims to investigate the spatial and temporal changes and trends of HI and associated energy per capita (EPC) to maintain human comfort in the Continental US (CONUS) and the Meso-America (the Caribbean, and Northern Regions of South America). Hourly air temperature and relative humidity datasets were collected from three sources: the National Center for Environmental Prediction, the North American Regional Reanalysis (NARR), and surface weather stations for a period of 30 years: 1990–2019. The algorithm used for the HI is similar to the one used by the US National Weather Service whereas the EPC is based on the ventilation requirements per person. Results for HI and EPC climatology for the summer season indicate the largest increasing values in Southeast US, followed by the Greater Antilles, and then the Lesser Antilles. Results of the analysis depict positive EPC rate of 2 kW h per year for the summer season for the Southern Greater Antilles. These trends using NARR data were found to be statistically significant and correlate very well with selected weather stations. The actual trends of electricity consumption per person per year for the CONUS suggest, in general, a correlation with increasing maximum HI.

Synoptic Climatology of Lake-Effect Snow Events off the Western Great Lakes

As the mesoscale dynamics of lake-effect snow (LES) are becoming better understood, recent and ongoing research is beginning to focus on the large-scale environments conducive to LES. Synoptic-scale composites are constructed for Lake Michigan and Lake Superior LES events by employing an LES case repository for these regions within the U.S. North American Regional Reanalysis (NARR) data for each LES event were used to construct synoptic maps of dominant LES patterns for each lake. These maps were formulated using a previously implemented composite technique that blends principal component analysis with a k-means cluster analysis. A sample case from each resulting cluster was also selected and simulated using the Advanced Weather Research and Forecast model to obtain an example mesoscale depiction of the LES environment. The study revealed four synoptic setups for Lake Michigan and three for Lake Superior whose primary differences were discrepancies in a surface pressure dipole structure previously linked with Great Lakes LES. These subtle synoptic-scale differences suggested that while overall LES impacts were driven more by the mesoscale conditions for these lakes, synoptic-scale conditions still provided important insight into the character of LES forcing mechanisms, primarily the steering flow and air–lake thermodynamics.

Assessing trends in atmospheric circulation patterns across North America

AbstractCirculation patterns (CPs) are discrete categorizations of spatial fields of atmospheric conditions, often used to examine the interaction between the overlying atmosphere and surface weather. Examining changes in the frequency of these atmospheric CPs may therefore help us better understand the relationship between a changing climate and the occurrence of extreme weather events. In this study, we use self‐organized maps to classify CPs from 500 mb geopotential heights (500z) and mean sea‐level pressure (MSLP) for five regions across North America, using two different reanalysis datasets. Trends in CPs (from 1979 to 2018) from both the North American Regional Reanalysis (NARR) and the recently released ERA5 datasets are compared using a new method in which the CPs from the ERA5 dataset are based on the principal components of the NARR data. This results in a more direct comparison between the CPs of each dataset. z500 CP trends were generally larger than the MSLP CP trends and the NARR CP trends were generally larger than the ERA5 CP trends. Furthermore, the most extreme summer‐dominant CPs were found to have increased significantly over the 40‐year study period in all five regions for both the NARR and ERA5 CPs.

The Effect of Climatological Variables on Future UAS-Based Atmospheric Profiling in the Lower Atmosphere

Vertical profiles of wind, temperature, and moisture are essential to capture the kinematic and thermodynamic structure of the atmospheric boundary layer (ABL). Our goal is to use weather observing unmanned aircraft systems (WxUAS) to perform the vertical profiles by taking measurements while ascending through the ABL and subsequently descending to the Earth’s surface. Before establishing routine profiles using a network of WxUAS stations, the climatologies of the flight locations must be studied. This was done using data from the North American Regional Reanalysis (NARR) model. To begin, NARR data accuracy was verified against radiosondes. While the results showed variability in individual profiles, the detailed statistical analyses of the aggregated data suggested that the NARR model is a viable option for the study. Based on these findings, we used NARR data to determine fractions of successful hypothetical flights of vertical profiles across the state of Oklahoma given thresholds of visibility, cloud base level (CBL) height, and wind speed. CBL height is an important parameter because the WxUAS must stay below clouds for the flight restrictions being considered. For the purpose of this study, a hypothetical WxUAS flight is considered successful if the vehicle is able to reach an altitude corresponding to a pressure level of 600 hPa. Our analysis indicated the CBL height parameter hindered the fractions of successful hypothetical flights the most and the wind speed tolerance limited the fractions of successful hypothetical flights most strongly in the winter months. Northwest Oklahoma had the highest fractions of successful hypothetical flights, and the southeastern corner performs the worst in every season except spring, when the northeastern corner performed the worst. Future work will study the potential effect of topology and additional variables, such as amount of rainfall and temperature, on fractions of successful hypothetical flights by region of the state.

Assessment of climate change impact over California using dynamical downscaling with a bias correction technique: method validation and analyses of summertime results

This study explores climate-change influences on future air pollution-relevant meteorological variables (e.g., temperature, wind, humidity, boundary layer heights) and atmospheric phenomena (e.g., heat wave, marine air penetration, droughts) over California by the 2050s. The Community Earth System Model simulation results from Coupled Model Intercomparison Project Phase 5 under an emission scenario that most closely aligns with California’s climate change goals were bias-corrected with respect to North American Regional Reanalysis data to reduce biases in both the climatological mean and inter-annual variations. The bias-corrected ~ 1° × 1° meteorological fields were dynamically downscaled to a resolution of 4 km × 4 km over California using the Weather Research and Forecasting model. This study focuses on summertime results, while the analysis of wintertime results will be presented in a separate paper. Our downscaled results projected a future increase of approximately 1 K in summer mean surface temperature over California under this single future climate realization. The temperature increase is larger in the nighttime than in the daytime. Water vapor mixing ratio is also projected to increase over California and off the coast. There are discernable decreases in boundary layer heights over the mountain ranges surrounding the central valley of California, while increases in boundary layer heights are observed over other regions in California. The number and duration of heat wave events are projected to increase substantially over the most populated parts of the State. The occurrence of marine air penetration events over the northern California is also projected to increase in the future.

Can the Multiscale Modeling Framework (MMF) Simulate the MCS‐Associated Precipitation Over the Central United States?

AbstractMesoscale convective systems (MCSs) are a major source of precipitation in many regions of the world. Traditional global climate models (GCMs) do not have adequate parameterizations to represent MCSs. In contrast, the Multiscalex Modeling Framework (MMF), which explicitly resolves convection within the cloud‐resolving model embedded in each GCM column, has been shown to be a promising tool for simulating MCSs, particularly over the Tropics. In this work, we use ground‐based radar‐observed precipitation, North American Regional Reanalysis data, and a high‐resolution Weather Research and Forecasting simulation to evaluate in detail the MCS‐associated precipitation over the central United States predicted by a prototype MMF simulation that has a 2° host‐GCM grid. We show that the prototype MMF with nudged winds fails to capture the convective initiation in three out of four major MCS events during May 201x1 and underpredicts the precipitation rates for the remaining event, because the model cannot resolve the mesoscale drylines/fronts that are important drivers for initiating convection over the Southern Great Plains region. By reducing the host‐GCM grid spacing to 0.25° in the MMF and nudging the winds, the simulation is able to better capture the mesoscale dynamics, which drastically improves the model performance. We also show that the MMF model performs better than the traditional GCM in capturing the precipitation intensity. Our results suggest that increasing resolution plays a dominant role in improving the simulation of precipitation in the MMF, and the cloud‐resolving model embedded in each GCM column further helps to boost precipitation rate.

Climatological Changes: Meteorological Parameters Affecting the Spatial Redistribution of U.S. Tornadoes

Climatological changes in the environments of key meteorological parameters that affect Significant Tornado Days (SigTorDs) have been determined for two active tornado regions defined as Box α and Box β, centered, respectively, over Oklahoma and Alabama and their respective environs. The North American Regional Reanalysis data was selected for 1980–2013, providing two successive 17-year periods corresponding to the last 34 years of previous research findings that focused on the aforementioned regions. This data record also corresponds to an increasing surface air temperature trend for the continental United States. Period I (1980–1996) and Period II (1997–2013) defined the years of changing environments for the two regions studied. Environmental parameters investigated and compared to SigTorDs were surface-based convective available potential energy (CAPE), storm relative helicity (0–3 km), bulk wind difference (1,000 mb to 500 mb), and lifted condensation level (LCL). Environments have changed to fewer SigTorDs (130 to 74) in Box α from Period I to Period II, dominated by decreasing frequency of storm relative helicity and slightly increasing CAPE. Box β is characterized by more SigTorDs (94 to 119) in Period II with an environment dominated by increasing frequency of storm relative helicity and minimal change in CAPE. These results support the importance of the changes in storm relative helicity (as opposed to CAPE) in explaining the eastward shift in U.S. tornado activity.

Diagnosing Moisture Sources for Flash Floods in the United States. Part II: Terrestrial and Oceanic Sources of Moisture

Abstract Backward trajectories were derived from North American Regional Reanalysis data for 19 253 flash flood reports published by the National Weather Service to determine the along-path contribution of the land surface to the moisture budget for flash flood events in the conterminous United States. The impact of land surface interactions was evaluated seasonally and for six regions: the West Coast, Arizona, the Front Range, Flash Flood Alley, the Missouri Valley, and the Appalachians. Parcels were released from locations that were impacted by flash floods and traced backward in time for 120 h. The boundary layer height was used to determine whether moisture increases occurred within the boundary layer or above it. Moisture increases occurring within the boundary layer were attributed to evapotranspiration from the land surface, and surface properties were recorded from an offline run of the Noah land surface model. In general, moisture increases attributed to the land surface were associated with anomalously high surface latent heat fluxes and anomalously low sensible heat fluxes (resulting in a positive anomaly of evaporative fraction) as well as positive anomalies in top-layer soil moisture. Over the ocean, uptakes were associated with positive anomalies in sea surface temperatures, the magnitude of which varies both regionally and seasonally. Major oceanic surface-based source regions of moisture for flash floods in the United States include the Gulf of Mexico and the Gulf of California, while boundary layer moisture increases in the southern plains are attributable in part to interactions between the land surface and the atmosphere.

Diagnosing Moisture Sources for Flash Floods in the United States. Part I: Kinematic Trajectories

Abstract This study uses backward trajectories derived from North American Regional Reanalysis data for 19 253 flash flood reports during the period 2007–13 published by the National Weather Service to assess the origins of air parcels for flash floods in the conterminous United States. The preferred flow paths for parcels were evaluated seasonally and for six regions of interest: the West Coast, Arizona, the Front Range of the Rocky Mountains, Flash Flood Alley in south-central Texas, the Missouri Valley, and the Appalachians. Parcels were released from vertical columns in the atmosphere at times and locations where there were reported flash floods; these were traced backward in time for 5 days. The temporal and seasonal cycles of flood events in these regions are also explored. The results show the importance of trajectories residing for long periods over oceanic regions such as the Gulf of Mexico and the Caribbean Sea. The flow is generally unidirectional with height in the lower layers of the atmosphere. The trajectory paths from oceanic genesis regions to inland hotspots and their orientation with height provide clues that can assist in the diagnosis of impending flash floods. Part II of this manuscript details the land–atmosphere interactions along the trajectory paths.

Characteristics and Variations of Low-Level Jets and Environmental Factors Associated with Summer Precipitation Extremes over the Great Plains

Abstract Low-level jets (LLJs) are associated with 10%–45% of the summer precipitation in the U.S. Great Plains region (GPR). This study uses the NCEP North American Regional Reanalysis data product (1979–2017) to characterize the association between LLJs and precipitation extremes (anomalously wet versus dry) during the summer months (June–August) over the GPR. It is found that the number, distribution, and direction of LLJs are not clearly associated with the precipitation anomalies. The characteristics and structural variations of the LLJs and their large-scale and mesoscale environment are then examined to identify the links between LLJs and precipitation extremes. Results show that dry and wet summers vary by synoptic anomaly patterns. During dry summers the anomalous ridging results in a warmer and drier environment, primarily through subsidence, which inhibits precipitation near LLJs. In contrast, during wet summers, a reduction in subsidence occurs, resulting in stronger lift and a cooler and moister environment, which leads to enhanced precipitation near LLJs. The LLJ speed, orientation, and spatial properties vary according to the synoptic anomaly patterns. LLJs do not drive precipitation extremes, but instead, they respond to them. Specifically, the LLJ exit region is characterized by stronger baroclinity and higher moisture content during the wet years. The higher moisture content allows for ascending air parcels to reach saturation more quickly, while the stronger baroclinity increases the warm advection associated with the LLJ. This, in turn, leads to faster rising motion and is therefore closely associated with the location and intensity of the LLJ associated precipitation.

The Energy Side of Budyko: Surface‐Energy Partitioning From Hydrological Observations

AbstractLand‐surface partitioning of net radiation into sensible and latent heat fluxes is critical for hydroclimatic processes but remains highly uncertain due to limited observations. We show that a suitable extension of the Budyko's curve, a well‐known framework in hydrology for water balance estimation, can be utilized effectively to partition the surface energy fluxes by expressing the long‐term evaporative fraction (EF) as a function of the dryness index only. The combination of this energy partitioning method with hydrological observations allows us to estimate the surface energy components at watershed and continental scales. Using this new framework, we show that North American Regional Reanalysis data overestimate surface evaporation, likely influencing the modeling of atmospheric convection. The obtained hydrologic constrains on energy partitioning can be used to provide more accurate estimations of surface energy fluxes for hydroclimatic predictions.

A Methodology for Flash Drought Identification: Application of Flash Drought Frequency across the United States

Abstract With the increasing use of the term “flash drought” within the scientific community, Otkin et al. provide a general definition that identifies flash droughts based on their unusually rapid rate of intensification. This study presents an objective percentile-based methodology that builds upon that work by identifying flash droughts using standardized evaporative stress ratio (SESR) values and changes in SESR over some period of time. Four criteria are specified to identify flash droughts: two that emphasize the vegetative impacts of flash drought and two that focus on the rapid rate of intensification. The methodology was applied to the North American Regional Reanalysis (NARR) to develop a 38-yr flash drought climatology (1979–2016) across the United States. It was found that SESR derived from NARR data compared well with the satellite-based evaporative stress index for four previously identified flash drought events. Furthermore, four additional flash drought cases were compared with the U.S. Drought Monitor (USDM), and SESR rapidly declined 1–2 weeks before a response was evident with the USDM. From the climatological analysis, a hot spot of flash drought occurrence was revealed over the Great Plains, the Corn Belt, and the western Great Lakes region. Relatively few flash drought events occurred over mountainous and arid regions. Flash droughts were categorized based on their rate of intensification, and it was found that the most intense flash droughts occurred over the central Great Plains, Corn Belt, and western Great Lakes region.

The Influence of Atmospheric Circulation on Abnormal Snowpack Melt‐Out Events and Drought in Wyoming

AbstractDeclines in high‐elevation snowpack and its effect on water availability in the western United States is of great interest given the projected changes in temperatures that may alter snowpack melt‐out timing. Because water from snowmelt is crucial to the West's water supply, this study examines the influence of continental‐scale atmospheric variables on snowpack melt out from 1980 to 2016 in Wyoming. Using snow telemetry station and North American Regional Reanalysis data, this research finds abnormally early snowpack melt out in 1987, 1992, and 2012 was associated with persistent above‐normal high pressure during spring, leading to sustained warmer‐than‐normal and drier‐than‐normal conditions. Conversely, abnormally late snowpack melt out in 2011, 1995, 1983, 1982, 2008, and 1999 was associated with lower‐than‐normal 500 mb geopotential heights, corresponding to very cold temperatures for April, May, and June, which set the stage for late snowpack melt out. Snowpack melt‐out departure values are also correlated with late‐season agricultural drought as indicated by 10 Drought Severity indices. Using snowpack melt‐out timing, in association with atmospheric variables in a predictive capacity may assist regulatory agencies, such as the Bureau of Reclamation, to make better informed decisions about when, or when not, to store or release water to mitigate late‐season agricultural drought impacts.

A Practical Approach to Landsat 8 TIRS Stray Light Correction Using Multi-Sensor Measurements

It has been noticed that the Landsat 8 Thermal Infrared Sensor (TIRS) had an issue with stray light since its launch in 2013. This artifact is due to out-of-field radiance that scatters onto the TIRS focal plane. Much effort has been taken to develop an algorithm to remove this artifact. One proposed approach involves using TIRS data itself (referred to as TIRS-on-TIRS) to retrieve the true sensor-reaching radiance. This approach has been proven to be operational and supports the TIRS Collection-1 product. A methodology of calibrating the TIRS sensor with information from the Geostationary Operational Environmental Satellite (GOES) instrument may optimally reduce the stray light effect for special cases where there is a large temperature contrast between the edge of the TIRS image and out-of-field radiance (referred to as GOES-on-TIRS). This paper illustrates a GOES to TIRS conversion (GTTC) algorithm with the North American Regional Reanalysis (NARR) data to support the GOES-on-TIRS method. Results show this GOES_TIRS correction method performs similarly to the TIRS Collection-1 product. Additionally, a simplified methodology is proposed to improve the GOES data processing which can operationalize the GOES-on-TIRS algorithm. Results also show that, using the proposed algorithm with these special cases, the maximum difference between the Collection-1 product and the GOES-on-TIRS correction results in a temperature difference from 0.5% to 0.7%.

Hydrological modelling using proxies for gauged precipitation and temperature

AbstractPrecipitation and temperature time series suffer from many problems, such as short time, inadequate spatial coverage, missing data, and biases from various causes, which are particularly critical in remote areas such as Northern Canada. The development of alternative datasets for using as proxies for inadequate/missing weather data represents a key research area. In this paper, the performance of 6 alternative datasets is evaluated for hydrological modelling over 12 watersheds located across Canada and the contiguous United States. The datasets can be classified into 3 distinct categories: (a) interpolated gridded data, (b) reanalysis data, and (c) climate model outputs. Hydrological simulations were carried out using a lumped conceptual hydrological model calibrated using standard weather data and compared against results using a calibration specific to each alternative dataset. Prior to the hydrological simulations, the alternative datasets were all evaluated with respect to their ability to reproduce gridded daily precipitation and temperature characteristics over North America. The results show that both the reanalysis data and climate model data adequately represent the spatial pattern of daily precipitation and temperature over North America. The North American Regional Reanalysis (NARR) dataset consistently shows the best performance. With respect to hydrological modelling, the observed discharges are accurately represented by both the gridded and NARR datasets, and more so for the NARR data. The National Centers for Environmental Prediction dataset consistently performs worst as it is unable to even capture the seasonal pattern of observed streamflow for 3 out of the 12 watersheds. These results indicate that the NARR dataset could be used as a proxy for gauged precipitation and temperature for hydrological modelling over watersheds where observational datasets are deficient. The results also illustrate the ability of climate model data to be used for performing hydrological modelling when driven by reanalysis data at their boundaries, and especially so for high‐resolution models.

Seasonal and interannual variability of land–atmosphere coupling across the Southern Great Plains of North America using the North American regional reanalysis

ABSTRACTThe purpose of this study was to investigate the seasonal to interannual variability of the temporal and spatial distributions of land–atmosphere coupling (LAC) at the mesoscale within the Southern Great Plains (SGP) of the United States. The North American Regional Reanalysis data set from 1979 to 2014 was used to complete this study. To further expand the relationship between soil moisture and precipitation, LAC was examined for the effects of soil moisture variability on latent heat flux (SM‐E) and the impact of latent heat flux variability on precipitation (E‐P). Results revealed that within the SGP there is a temporal and spatial seasonal evolution of the SM‐E relationship and dry boreal summer month (June, July and August, JJA) periods exhibit a stronger E‐P relationship relative to pluvial boreal summer month periods. Further, the variability of coupling was large both within‐season (i.e. JJA) as well as at the interannual scale while the interannual spatial and temporal coherence was such that no specific locations showed consistent coupling within the domain. Thus, the results indicate that while the SGP domain is sensitive to coupling, the location of preferred coupling is likely due to non‐local factors at the mesoscale embedded within synoptic conditions as well as the regional climate.

A calibration‐free formulation of the complementary relationship of evaporation for continental‐scale hydrology

AbstractAn important scaling consideration is introduced into the formulation of the complementary relationship (CR) of land surface evapotranspiration (ET) by specifying the maximum possible evaporation rate (Epmax) of a small water body (or wet patch) as a result of adiabatic drying from the prevailing near‐neutral atmospheric conditions. In dimensionless form the CR therefore becomes yB = f( xB) = f(X) = 2X2 − X3, where yB = ET/Ep, xB = Ew/Ep. Ew is the wet‐environment evaporation rate as given by the Priestley‐Taylor equation, Ep is the evaporation rate of the same small wet surface for which Epmax is specified and estimated by the Penman equation. With the help of North American Regional Reanalysis data, the CR this way yields better continental‐scale performance than earlier, calibrated versions of it and is on par with current land surface model results, the latter requiring vegetation, soil information and soil moisture bookkeeping. Validation has been performed by Parameter‐Elevation Regressions on Independent Slopes Model precipitation and United States Geological Survey runoff data. A novel approach is also introduced to calculate the value of the Priestley‐Taylor parameter to be used with continental‐scale data, making the new formulation of the CR completely calibration free.