Surface Energy Fluxes Research Articles

Urbanization-induced Earth's surface energy alteration and warming: A global spatiotemporal analysis

As both drivers and first responders, urban areas are rapidly increasing in importance of shaping global climate change. The global imprint of urbanization on surface energy balance (SEB) remains, however, largely unknown. Here, we undertake a global spatiotemporal analysis on urbanization-induced Earth's surface energy alteration using annually dynamic maps of impervious surface and satellite-derived surface energy fluxes and land surface temperature (LST), alongside space-for-time (spatial) and time-for-time (temporal) approaches. Relying on space-for-time substitution, we estimate that global urbanization-driven surface warming of annual mean temperature has reached 0.054 °C (95% CI, −0.009–0.214 °C) between 2003 and 2018 (with temporal stability), especially pronounced in summer daytime (0.122 °C, −0.038–0.495 °C), largely attributed to decline in local latent heat cooling, enlarged long-wave heat dissipation and anthropogenic heat. Temporal quantification of urbanization effects demonstrates consistence with spatial gradient approach, implying that space can substitute time in understanding and predicting future urbanization imprint on SEB and temperature (i.e., cities as harbingers of climate change). We further predict that during the first 35 years of this century annual warming magnitude will be nearly double that of the period 1985–2018, and particularly, urbanization perturbation to SEB will be more intense across countries or regions in the arid and warm temperate climates under global warming. This research provides science-based foundation that can help inform the IPCC special report on cities and climate change, and emphasize urgency to develop tailored mitigation and adaptation strategies against rapidly warming based on SEB attribution and climate background.

An analytical approximation of urban heat and dry islands and their impact on convection triggering

It is well known that cities increase air and surface temperatures compared to their rural surroundings, the so-called urban heat island (UHI) effect. However, the associated changes in atmospheric humidity (also known as urban dry island, UDI) and convection triggering remain largely unexplored and it is still unclear how urban modifications of the surface energy budget influence the diurnal evolution of temperature and humidity in the Atmospheric Boundary Layer (ABL) and ultimately control the initiation of convective clouds.Here we quantify the impact of different urban settings and free atmospheric conditions on UHI, UDI, and convection triggers by means of a zero-order model of the ABL. Specifically, we derive an approximate solution for urban-rural changes in surface energy fluxes and ABL potential temperature and humidity and we investigate the crossing between the ABL height and the lifting condensation level (LCL) which is a proxy for the triggering of convective clouds. We show that urban areas are generally warmer and drier, thus causing an increase in both ABL and LCL heights. However, the response of the ABL-LCL crossing to surface conditions is non-linear and there exists a range of free atmosphere conditions for which changes in imperviousness can impact convective clouds.

Evaluation and spatio-temporal analysis of surface energy flux in permafrost regions over the Qinghai-Tibet Plateau and Arctic using CMIP6 models

ABSTRACT The surface energy budget over the Qinghai-Tibet Plateau (QTP) and the Arctic significantly influences the climate system with global consequences. The performances of 30 selected Coupled Model Intercomparison Project Phase 6 (CMIP6) models were evaluated based on six sites in the QTP and Arctic. The simulation results for latent heat flux (LE) were more accurate in the QTP, where the correlation coefficient and root mean square error (RMSE) were 0.9 and 30 W m−2, respectively. The results for sensible heat flux (H) were more accurate in the Arctic, the correlation coefficient and RMSE were 0.8 and 24 W m−2, respectively. Furthermore, the multiple models mean results revealed that the surface energy flux had seasonal variation and regional differences over the QTP and the Arctic. In the QTP, H was the lowest in winter, increased in spring, and reached the maximum in summer. However, the transitional changes in spring and autumn were not apparent in the Arctic, mainly due to seasonal net radiation difference between the two places. LE was affected by precipitation and surface soil moisture content. This work is important for understanding land-atmosphere interactions and useful for improving the accuracy of land surface models simulations.

Record Arctic Cyclone of January 2022: Characteristics, Impacts, and Predictability

AbstractArctic cyclones are a fundamental component of Arctic climate, influencing atmospheric heat and moisture transport into the region and surface energy, moisture, and momentum fluxes. Arctic cyclones can also drive changes in sea ice and energize ocean waves. Here we investigate a record low sea level pressure (SLP) Arctic cyclone which formed in East Greenland and tracked NE over the Barents and Kara seas between 21 and 27 January 2022. At its peak intensity on 24 January, the cyclone reached an estimated depth of 932.2 mb at 79.5°N 20°E. North of 70°N, this is the lowest SLP in the ERA‐5 reanalysis over 1979 to present. The cyclone resulted in a record (over the period 1979–2022) weekly loss of regional sea ice area and surface wind speeds, and generated ocean waves exceeding 8 m that impinged on sea ice in the Barents sea, observed via satellite altimetry as large waves‐in‐sea ice up to 2 m in amplitude more than 100 km into the ice pack. Surface heat fluxes were strongly impacted by the cyclone, with record atmosphere‐to‐surface turbulent fluxes. However, the direct atmospheric thermodynamic impact on sea ice loss was modest, and the record sea ice changes were likely mainly driven by dynamical and/or ocean processes. While the storm was well predicted up to 8 days in advance, subsequent changes in sea ice cover were not, likely due to biases in the forecasts' sea ice initial conditions and missing physics in the forecast model such as wave‐sea ice interaction.

Partitioning of Sensible and Latent Heat Fluxes in Different Vegetation Types and Their Spatiotemporal Variations Based on 203 FLUXNET Sites

AbstractBowen ratio reflects the partitioning between sensible and latent heat fluxes and plays a crucial role in land–atmosphere interaction. In this study, the spatiotemporal variations of Bowen ratio among 12 vegetation types were analyzed using observations from 203 FLUXNET sites and compared against Community Land Model (CLM) simulations. Results showed that the annual mean Bowen ratio across all sites was 1.48 ± 1.20 (mean ± SD). Sites with Bowen ratios less than 1 were found across all continents, and the ones with a higher Bowen ratio appeared in dry and warm areas. Open shrublands showed the highest Bowen ratio (3.04 ± 0.58), whereas wetlands showed the lowest (0.74 ± 0.09). In terms of seasonality, Bowen ratio generally showed lower values in local summer and higher in spring and autumn in the northern hemisphere; the opposite occurred in the southern hemisphere. The spatiotemporal variations in Bowen ratio can be explained by climatic, geographical, and biological factors, with climate factors having the greatest impact. The spatial correlation analyses suggested Bowen ratio increased under higher VPD (R = 0.45, p < 0.001) and hotter (R = 0.14, p < 0.05) conditions and decreased with higher precipitation (R = −0.34, p < 0.001), albedo (R = −0.16, p > 0.05), and leaf area index (R = −0.25, p < 0.001). Compared to FLUXNET observations, CLM well reproduced the global annual mean Bowen ratio (1.48 for CLM vs. 1.56 for FLUXNET) but showed larger differences for certain vegetation types. Our results could enhance our understanding of biotic and environmental controls on land surface energy fluxes and help improve the land surface and climate models.

Vegetation type is an important predictor of the arctic summer land surface energy budget

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.

TIBETAN PLATEAU SNOW COVER VARYING WITH CLIMATE CHANGE: A REGIONAL CLIMATE PERSPECTIVE

Abstract. The Tibetan Plateau (TP) is experiencing dramatic climate changes, which increases the geographical hazard risks affecting human lives and properties. As TP holding the largest cryosphere extent outside the polar region, frequent and serious snow disasters become the crucial topic in the TP disaster reduction and management. The primary task to cope with TP snow disasters is to understand the formation and evolution of snow cover as the basis to assess and predict geographical hazards. Considering time variability and spatial heterogeneity, the geographical detector analysis has been adopted to investigate the coupling relationships between snow cover and climate change in the different periods (1989-2018) and different geographical regions (Qaidam areas, Qiangtang areas and Hengduan Mountains). The following results are noted: (i) Regionalization provides a better climate explanation for snow cover compared with the non-regionalized whole plateau model, which verifies again that the snow cover distribution and its driving mechanism both have strong spatial heterogeneity. (ii) Temperature has a dominant influence on the snow cover in all three regions, showing that net surface energy flux balance is the major limitation to the snow cover so that temperature becomes the key factor of snow-related risk management. (iii) The impact of precipitation on snow cover is only significant in the Qaidam areas according to the interaction detector approach, where the combination of temperature and precipitation can explain more than 65% of the snow cover distribution. Thus the Qaidam areas requires risk monitoring related to both hydrological and thermal aspects.

Review of the Observed Energy Flow in the Earth System

The energy budget imbalance at the top of the atmosphere (TOA) and the energy flow in the Earth’s system plays an essential role in climate change over the global and regional scales. Under the constraint of observations, the radiative fluxes at TOA have been reconstructed prior to CERES (Clouds and the Earth’s Radiant Energy System) between 1985 and 2000. The total atmospheric energy divergence has been mass corrected based on ERA5 (the fifth generation ECMWF ReAnalysis) atmospheric reanalysis by a newly developed method considering the enthalpy removing of the atmospheric water vapor, which avoids inconsistencies due to the residual lateral total mass flux divergence in the atmosphere, ensuring the balances of the freshwater fluxes at the surface. The net surface energy flux (Fs) has been estimated using the residual method based on energy conservation, which is the difference between the net TOA radiative flux and the atmospheric energy tendency and divergence. The Fs is then verified directly and indirectly with observations, and results show that the estimated Fs in North Atlantic is superior to those from model simulations. This paper gives a brief review of the progress in the estimation of the observed energy flow in the Earth system, discusses some caveats of the existing method, and provides some suggestions for the improvements of the aforementioned data sets.

Unprecedented Arctic sea ice thickness loss and multiyear-ice volume export through Fram Strait during 2010–2011

The satellite-observed sea ice thickness (SIT) records from 2003 to 2020 identify an extreme SIT loss during 2010–2011. Ice thickness budget analysis demonstrates that the thickness loss was associated with an extraordinarily large multiyear ice (MYI) volume export through the Fram Strait during the season of sea ice advance. High cloudiness led to positive anomalies of net longwave radiation, and positive net surface energy flux anomalies supported enhanced sea ice melt from June to August. Due to the MYI loss, the Arctic sea ice became more sensitive to subsequent atmospheric anomalies. The reduced surface albedo triggering a positive ice-albedo amplifying feedback and contributed to the accelerating loss of ice thickness. These tightly coupled events highlight that the increasingly younger and thinner Arctic sea ice is becoming more vulnerable to external forcing and created the precondition for the rapid reduction in sea ice extent in 2012.

Energy fluxes, mass balance, and climate sensitivity of the Sutri Dhaka Glacier in the western Himalaya

Various regional climatic factors influence glacier mass balance and thus control the water budget of the Himalayan rivers. However, the scarcity of observational data hinders a detailed understanding of the processes governing glacier mass balances in the Himalaya. Here we analyze the mass balance of the Sutri Dhaka Glacier, a debris-free glacier in the Chandra basin (western Himalaya) combining field observations and a physically based model to understand the drivers of mass balance variability. The modeled energy flux showed that net shortwave radiation contributed 56% to the total surface energy fluxes, followed by net longwave radiation (27%), sensible heat (8%), latent heat (5%), and ground heat flux (4%). However, over the ablation zone, inward fluxes account for most of the total heat flux, resulting in strong summertime melting. The model estimated glacier mass balance was −1.09 ± 0.31 and −0.62 ± 0.19 m w.e. during 2015/16 and 2016/17, which matches well with the in situ glaciological mass balance of −1.16 ± 0.33 and −0.67 ± 0.33 m w.e., respectively. A sensitivity analysis demonstrates that the mass balance of the glacier is affected by both air temperature (−0.21 m w.e. a−1 °C−1) and precipitation (0.19 m w.e. a−1 (10%)−1) changes. Our study suggests that, the mass balance of the Sutri Dhaka Glacier is less sensitive to changes in the partitioning of precipitation into snow and rain because the majority of precipitation falls as snow during the winter when the temperature is well below 0°C.

Spatial heterogeneity effects on land surface modeling of water and energy partitioning

Abstract. Understanding the influence of land surface heterogeneity on surface water and energy fluxes is crucial for modeling earth system variability and change. This study investigates the effects of four dominant heterogeneity sources on land surface modeling, including atmospheric forcing (ATM), soil properties (SOIL), land use and land cover (LULC), and topography (TOPO). Our analysis focused on their impacts on the partitioning of precipitation (P) into evapotranspiration (ET) and runoff (R), partitioning of net radiation into sensible heat and latent heat, and corresponding water and energy fluxes. An initial set of 16 experiments were performed over the continental US (CONUS) using the E3SM land model (ELMv1) with different combinations of heterogeneous and homogeneous datasets. The Sobol' total and first-order sensitivity indices were utilized to quantify the relative importance of the four heterogeneity sources. Sobol' total sensitivity index measures the total heterogeneity effects induced by a given heterogeneity source, consisting of the contribution from its own heterogeneity (i.e., the first-order index) and its interactions with other heterogeneity sources. ATM and LULC are the most dominant heterogeneity sources in determining spatial variability of water and energy partitioning, mainly contributed by their own heterogeneity and slightly contributed by their interactions with other heterogeneity sources. Their heterogeneity effects are complementary, both spatially and temporally. The overall impacts of SOIL and TOPO are negligible, except TOPO dominates the spatial variability of R/P across the transitional climate zone between the arid western and humid eastern CONUS. Accounting for more heterogeneity sources improves the simulated spatial variability of water and energy fluxes when compared with ERA5-Land reanalysis dataset. An additional set of 13 experiments identified the most critical components within each heterogeneity source, which are precipitation, temperature, and longwave radiation for ATM, soil texture, and soil color for SOIL and maximum fractional saturated area parameter for TOPO.

Comparison of Surface Energy Fluxes from Global to Local Scale

Abstract This study uses the ECMWF ERA5 reanalysis and observationally constrained top-of-the-atmosphere radiative fluxes to infer net surface energy fluxes covering 1985–2018, which can be further adjusted to match the observed mean land heat uptake. Various diagnostics are applied to provide error estimates of inferred fluxes on different spatial scales. For this purpose, adjusted as well as unadjusted inferred surface fluxes are compared with other commonly used flux products. On a regional scale, the oceanic energy budget of the North Atlantic between the RAPID array at 26.5°N and moorings located farther north (e.g., at the Greenland–Scotland Ridge) is evaluated. On the station scale, a comprehensive comparison of inferred and buoy-based fluxes is presented. Results indicate that global land and ocean averages of unadjusted inferred surface fluxes agree with the observed heat uptake to within 1 W m−2, while satellite-derived and model-based fluxes show large global mean biases. Furthermore, the oceanic energy budget of the North Atlantic is closed to within 2.7 (−0.2) W m−2 for the period 2005–09 when unadjusted (adjusted) inferred surface fluxes are employed. Indirect estimates of the 2004–16 mean oceanic heat transport at 26.5°N are 1.09 PW (1.17 PW with adjusted fluxes), which agrees well with observed RAPID transports. On the station scale, inferred fluxes exhibit a mean bias of −20.1 W m−2 when using buoy-based fluxes as reference, which confirms expectations that biases increase from global to local scales. However, buoy-based fluxes as reference are debatable, and are likely positively biased, suggesting that the station-scale bias of inferred fluxes is more likely on the order of −10 W m−2.

Effect of aggregation and disaggregation of land surface temperature imagery on evapotranspiration estimation

Understanding the spatial distribution of evapotranspiration (ET) and other surface energy fluxes in tree crops is vital for optimizing precision irrigation management practices. The land surface temperature (LST) is the primary input to estimate ET at field scale, but currently, available satellite data have a coarse resolution, which is not suitable for estimating ET at the required spatiotemporal scale at the field level. The development of unmanned aerial vehicles (UAVs) or airborne platforms during the last two decades has enabled the acquisition of LST and subsequent derivation of ET at the finer spatial resolution of up to a few centimeters due to the advances in low-weight thermal cameras onboard these platforms. In this study, the disaggregation methodology was applied to Landsat imagery based on aerial imagery collected at 0.5 m resolution over almond orchards in the Sacramento Valley of California, USA, to estimate ET at the orchard scale. The TsHARP algorithm was applied to sharpen the Landsat LST images (30 m spatial resolution) to target resolutions of 0.5, 10, 20 m. Also, the simple average aggregation technique was used to aggregate the aerial imagery (0.5 resolution) to 10, 20, and 30 m scales. Two Source Energy Balance (TSEB) model was used to estimate spatially distributed ET fluxes by combining the ground-based meteorological data for aggregated and disaggregated LST images. The estimated ET fluxes were compared to the measured eddy covariance fluxes, and then the aggregation and disaggregation processes were evaluated at 0.5, 10, 20, and 30 m spatial scales. At four spatial scales (0.5, 10, 20, and 30 m) aggregation and disaggregation derived ET values were highly correlated with the measured eddy covariance fluxes within the source footprint area. Furthermore, both aggregation and disaggregation approaches demonstrated similar ET spatial patterns within the study area, with slight variations observed under the Landsat 30 m resolution. The results indicated that TsHARP downscaled high-resolution imagery provides optimal spatial resolution for mapping and analyzing ET at the plant to orchard scale, with a 95% confidence interval and an RMSE of 0.43 mm/day. Overall, the results suggest that high spatial resolution imagery disaggregated from Landsat was the best alternative for estimating ET fluxes from the plant to the orchard scale, which is beneficial for precision irrigation management in orchards, but the framework can also be applied to other precision agriculture applications.

Diagnosing Discrepancies between Observations and Models of Surface Energy Fluxes in a Midlatitude Lake

Abstract The physical processes of heat exchange between lakes and the surrounding atmosphere are important in simulating and predicting terrestrial surface energy balance. Latent and sensible heat fluxes are the dominant physical process controlling ice growth and decay on the lake surface, as well as having influence on regional climate. While one-dimensional lake models have been used in simulating environmental changes in ice dynamics and water temperature, understanding the seasonal to daily cycles of lake surface energy balance and its relationship to lake thermal properties, atmospheric conditions, and how those are represented in models is still an open area of research. We evaluated a pair of one-dimensional lake models, Freshwater Lake (FLake) and the General Lake Model (GLM), to compare modeled latent and sensible heat fluxes against observed data collected by an eddy covariance tower during a 1-yr period in 2017, using Lake Mendota in Madison, Wisconsin, as our study site. We hypothesized transitional periods of ice cover as a leading source of model uncertainty, and we instead found that the models failed to simulate accurate values for large positive heat fluxes that occurred from late August into late December. Our results ultimately showed that one-dimensional models are effective in simulating sensible heat fluxes but are considerably less sensitive to latent heat fluxes than the observed relationships of latent heat flux to environmental drivers. These results can be used to focus future improvement of these lake models especially if they are to be used for surface boundary conditions in regional numerical weather models. Significance Statement While lakes consist of a small amount of Earth’s surface, they have a large impact on local climate and weather. A large amount of energy is stored in lakes during the spring and summer, and then removed from lakes before winter. The effect is particularly noticeable in high latitudes, when the seasonal temperature difference is larger. Modeling this lake energy exchange is important for weather models and measuring this energy exchange is challenging. Here we compare modeled and observed energy exchange, and we show there are large amounts of energy exchange happening in the fall, which models struggle to capture well. During periods of partial ice coverage in early winter, lake behavior can change rapidly.

Influence of water table dynamics on spatial and temporal patterns of hydroclimate extremes over Lake Victoria Basin, East Africa: Comparison of wet and dry years

AbstractWRF model coupled to water table dynamics has been adapted to investigate the spatial and temporal evolution of wet and dry conditions over Lake Victoria Basin. Two 2‐year long simulations were conducted using coupled model with water table and the uncoupled (without water table) for wet and dry periods. Influence of water table on land–atmosphere coupling and interconnections among precipitation, soil moisture, evapotranspiration, and surface energy fluxes were examined. Overall, the coupled model simulated significantly higher monthly rainfall amounts during both the short (March‐May) and long (October‐December) rains of the wet year, which was more consistent with observations particularly over the lake surface and immediate hinterlands. Simulated monthly rainfall differences between coupled and uncoupled were pronounced during the peak of long rains, exceeding 100mm over the lake surface. Toward the end of the rainfall season (May) the difference was minimal. During the short rains significant differences occurred mostly in November, especially over the eastern shores. But during the relatively dry year minimal differences were generally witnessed throughout the year except in May, east of Lake Victoria. The coupled model simulated stronger matching among rainfall, soil moisture, and evapotranspiration over areas with shallow water table, for example in Kisumu to the east and Bukoba to the west. In these areas coupled model simulated higher soil moisture corresponding to higher evapotranspiration and precipitation. These interconnections were more pronounced during short and long rains in 1997 (wet) compared to 2010 (dry). Wet conditions over the gulf of Kisumu corresponded with rise in water table especially during October–December 1997 consistent with ENSO‐related flooding over the area. Hence, our study demonstrated that incorporating water table resulted in realistic interconnections between precipitation, soil moisture, ET, and surface energy fluxes, and could improve simulation and prediction of spatial‐temporal evolution of wet and dry conditions over Lake Victoria Basin.

UAS-based high resolution mapping of evapotranspiration in a Mediterranean tree-grass ecosystem

Understanding the impact of land use and land cover change on surface energy and water budgets is increasingly important in the context of climate change research. Eddy covariance (EC) methods are the gold standard for high temporal resolution measurements of water and energy fluxes, but cannot resolve spatial heterogeneity and are limited in scope to the tower footprint (few hundred meter range). Satellite remote sensing methods have excellent coverage, but lack spatial and temporal resolution. Long-range unmanned aerial systems (UAS) can complement these other methods with high spatial resolution over larger areas. Here we use UAS thermography and multispectral data as inputs to two variants of the Two Source Energy Balance Model to accurately map surface energy and water fluxes over a nutrient manipulation experiment in a managed semi-natural oak savanna from peak growing season to senescence. We use energy flux measurements from 6 EC stations to evaluate the performance of our method and achieve good accuracy (RMSD ≈ 60 W m−2 for latent heat flux). We use the best performing latent heat estimates to produce very high-resolution evapotranspiration (ET) maps, and investigate the drivers of ET change over the transition to the senescence period. We find that nitrogen and nitrogen plus phosphorus treatments lead to significant increases in ET (P < 0.001) for both trees (4 and 6%, respectively) and grass (12 and 9%, respectively) compared to the control. These results highlight that the high sensitivity and spatial and temporal resolution of a UAS system allows the precise estimation of relative water and energy fluxes over heterogeneous vegetation cover.

The Effects of Soil Representation in WRF–CLM on the Atmospheric Moisture Budget

Abstract Soil hydrophysical properties are necessary components in weather and climate simulation, yet the parameter inaccuracies may introduce considerable uncertainty in the representation of surface water and energy fluxes. This study uses seasonal coupled simulations to examine the uncertainties in the North American atmospheric water cycle that result from the use of different soil datasets. Two soil datasets are considered: the State Soil Geographic dataset (STATSGO) from the U.S. Department of Agriculture and the Global Soil Dataset for Earth System Modeling (GSDE) from Beijing Normal University. Two simulations are conducted from 1 June to 31 August 2016–18 using the Weather Research and Forecasting (WRF) Model coupled with the Community Land Model (CLM) version 4 and applying each soil dataset. It is found that changes in soil texture lead to statistically significant differences in daily mean surface water and energy fluxes. The boundary layer thermodynamic structure responds to these changes in surface fluxes resulting in differences in mean CAPE and CIN, leading to conditions that are less conducive for precipitation. The soil-texture-related surface fluxes instigate dynamic responses as well. Low-level wind fields are altered, resulting in differences in the associated vertically integrated moisture fluxes and in vertically integrated moisture flux convergence in the same regions. Through land–atmosphere interactions, it is shown that soil parameters can affect each component of the atmospheric water budget.

Evaluating long‐term One‐Way Atmosphere‐Hydrology simulations and the impacts of Two‐Way coupling in four mountain watersheds

AbstractJoint hydrologic‐atmospheric model frameworks offer novel insights into the terrestrial hydrologic cycle and the potential for improved predictive capabilities for stream discharge and other hydrologic fluxes. In this study, we examine both one‐ and two‐way coupled integrations of the Weather Research and Forecasting (WRF v3.8.1) atmospheric and WRF‐Hydro (v5.0) hydrologic models for four 1000–2000 km2 snow‐dominated mountain watersheds (1500–2100 m mean elevation) in Idaho's Rocky Mountains. In watersheds where anthropogenic withdrawals are minimal (3 of 4 watersheds), we simulate stream discharge with high confidence (KGE &gt; 0.63) for a 20 year period in the uncoupled scenarios, and find that WRF winter precipitation accumulations have less than 15% average error for all but two of the fourteen comparison NRCS Snotel sites. However, annual streamflow biases are highly correlated (r2 &gt; 0.8 in some cases) with the annual errors in WRF cold‐season precipitation, suggesting that process representation of winter orographic precipitation limits hydrologic predictability. In the second part of the study, we evaluate the potential for ‘two‐way’ model coupling to influence hydrologic predictability by examining a 2 month case‐study period with active spring season convective precipitation. We quantify the impacts of resolving hillslope‐scale soil water redistribution on the ABL, and find that while resolving overland and saturated subsurface soil moisture flow influences soil moisture distributions and surface energy fluxes, the impact on precipitation is non‐systematic, as precipitation is generally atmospherically controlled during the study period. Consequently, future efforts should focus on improving winter orographic process representation, as streamflow is highly sensitive to errors in these processes.

Accounting for Almond Crop Water Use under Different Irrigation Regimes with a Two-Source Energy Balance Model and Copernicus-Based Inputs

Accounting for water use in agricultural fields is of vital importance for the future prospects for enhancing water use efficiency. Remote sensing techniques, based on modelling surface energy fluxes, such as the two-source energy balance (TSEB), were used to estimate actual evapotranspiration (ETa) on the basis of shortwave and thermal data. The lack of high temporal and spatial resolution of satellite thermal infrared (TIR) missions has led to new approaches to obtain higher spatial resolution images with a high revisit time. These new approaches take advantage of the high spatial resolution of Sentinel-2 (10–20 m), and the high revisit time of Sentinel-3 (daily). The use of the TSEB model with sharpened temperature (TSEBS2+S3) has recently been applied and validated in several study sites. However, none of these studies has applied it in heterogeneous row crops under different water status conditions within the same orchard. This study assessed the TSEBS2+S3 modelling approach to account for almond crop water use under four different irrigation regimes and over four consecutive growing seasons (2017–2020). The energy fluxes were validated with an eddy covariance system and also compared with a soil water balance model. The former reported errors of 90 W/m2 and 87 W/m2 for the sensible (H) and latent heat flux (LE), respectively. The comparison of ETa with the soil water balance model showed a root-mean-square deviation (RMSD) ranging from 0.6 to 2.5 mm/day. Differences in cumulative ETa between the irrigation treatments were estimated, with maximum differences obtained in 2019 of 20% to 13% less in the most water-limited treatment compared to the most well-watered one. Therefore, this study demonstrates the feasibility of using the TSEBS2+S3 for monitoring ETa in almond trees under different water regimes.

Weakened amplitude and delayed phase of the future temperature seasonal cycle over China during the twenty‐first century

AbstractThe surface air temperature seasonal cycle in China has clearly changed in response to global warming, but its future projection has yet to be studied. Here, we investigate the performance and projection of the temperature seasonal cycle in China in 32 global climate models participating in the Coupled Model Intercomparison Project 6 (CMIP6). The characteristics of the temperature seasonal cycle, as represented by the amplitude and phase, are measured by the Fourier transform. The results show that the CMIP6 models can reasonably capture the observed typical temperature seasonal cycle feature with clear north–south gradients both for amplitude and phase in China. In the twenty‐first century, the models robustly project a delay in the phase across the whole country and a reduction in the amplitude in most of China relative to the reference period. Those changes are similar in the two shared socioeconomic pathway (SSP) scenarios, with overall larger magnitudes in the SSP5‐8.5 scenario than in the SSP2‐4.5 scenario. Projected amplitude changes are mainly linked to surface energy flux changes, especially latent heat flux changes. In response to future global warming, larger increases in the latent heat flux and thus stronger surface evaporative cooling lead to weaker surface warming in summer than in winter, contributing to a decrease in the amplitude of the temperature seasonal cycle in most regions of China.