Emission Of Longwave Radiation Research Articles

Field evaluation of the efficacy of passive radiative cooling infrastructure: A case study in Phoenix Arizona

Radiative properties of shade structures affect their surface temperatures, sensible heat fluxes and longwave and shortwave radiation exchange. In fact, structures with high solar reflectance and thermal emittance have the potential to remain below ambient air temperatures, convecting sensible heat from the air to the surface and then radiating that heat to space—a sort of radiant heat pump.We explore cooling benefits of urban surfaces with high solar reflectance and high thermal emittance radiative cooling films through a field measurement campaign in Phoenix Arizona, USA. The tested films have solar reflectance and selective thermal emittance (in wavelengths 8–13 μm) close to 95 %. We applied films in both before-after and control-test experimental designs on thin metal roofs of park shade structures. We measured surface temperatures, surface heat fluxes, upward- and downward-welling longwave and shortwave radiation, and local weather conditions.Results demonstrate the ability of radiant cooling films to reduce surface temperatures on hot days below ambient air temperatures. Test surfaces with cooling films were an average of 7 °C cooler than control shelter surfaces over the diurnal cycle, reducing sensible heat fluxes into the environment by up to 80 %, and lowering mean radiant temperatures for pedestrians using the shelters by more than 3 °C. It was also observed that the sum of the net reflected shortwave and emitted longwave radiation over the diurnal cycle can exceed the total incident longwave and shortwave radiation on the surface, demonstrating the ability of these materials to radiatively “pump” heat out of the city.

ISLAND: Interpolating Land Surface Temperature using land cover

Cloud occlusion is a common problem in the field of remote sensing, particularly for retrieving Land Surface Temperature (LST). Remote sensing thermal instruments onboard operational satellites are supposed to enable frequent and high-resolution observations over land; unfortunately, clouds adversely affect thermal signals by blocking outgoing longwave radiation emission from the Earth’s surface, interfering with the retrieved ground emission temperature. Such cloud contamination severely reduces the set of serviceable LST images for downstream applications, making it impractical to perform intricate time-series analysis of LST. In this paper, we introduce a novel method to remove cloud occlusions from Landsat 8 LST images. We call our method ISLAND, an acronym for Interpolating Land Surface Temperature using land cover. Our approach uses LST images from Landsat 8 (at 30m resolution with 16-day revisit cycles) and the NLCD land cover dataset. Inspired by Tobler’s first law of Geography, ISLAND predicts occluded LST through a set of spatio-temporal filters that perform distance-weighted spatio-temporal interpolation. A critical feature of ISLAND is that the filters are land cover-class aware, making it particularly advantageous in complex urban settings with heterogeneous land cover types and distributions. Through qualitative and quantitative analysis, we show that ISLAND achieves robust reconstruction performance across a variety of cloud occlusion and surface land cover conditions, and with a high spatio-temporal resolution. We provide a public dataset of 20 U.S. cities with pre-computed ISLAND LST outputs. Using several case studies, we demonstrate that ISLAND opens the door to a multitude of high-impact urban and environmental applications across the continental United States.

Global 1 km land surface parameters for kilometer-scale Earth system modeling

Abstract. Earth system models (ESMs) are progressively advancing towards the kilometer scale (“k-scale”). However, the surface parameters for land surface models (LSMs) within ESMs running at the k-scale are typically derived from coarse-resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1 km land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31 % to 54 % of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSM and ESM modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. The 1 km land surface parameters are publicly available at https://doi.org/10.5281/zenodo.10815170 (Li et al., 2024).

Local surface warming assessment in response to vegetation shifts over arid lands of Central Asia (2001−2020)

The Northern Eurasia Earth Science Partnership Initiative (NEESPI) was established to address the large-scale environmental change across this region. Regardless of the increasingly insightful literature addressing vegetation change across Central Asia, the biogeophysical warming effects of vegetation shifts still need to be clarified. To contribute, the utility of robust satellite observation is explored to evaluate the surface warming effects of vegetation shifts across Central Asia, which is among NEEPSI's hotspots. We estimated an average increase of +1.9 °C in daytime local surface temperature and + 1.5 °C in the nighttime due to vegetation shift (2001–2020). Meanwhile, the mean local latent heat increased by 4.65Wm−2, following the mild reduction of emitted longwave radiation (−0.8Wm−2). We found that vegetation shifts led to local surface warming with a bright surface, noting that the average air surface temperature was revealed to have increased significantly (2001–2020). This signal was driven mainly by agricultural expansion in western Kazakhstan stretching to Tajikistan and Xinjiang, then deforestation confined in Tajikistan, southeast Kazakhstan, and the northwestern edge of Xinjiang, and finally, grassland encroachment occurred massively in the west to central Kazakhstan. These findings address the latest information on Central Asia's vegetation shifts that may be substantial in landscape change mitigation plans.

Thermodynamic characteristics of extreme heat waves over the middle and lower reaches of the Yangtze River Basin

In August 2022, an exceptionally long-lasting heat wave (HW) affected the middle and lower reaches of the Yangtze River basin. This study uses the JRA55 daily reanalysis datasets to elucidate the thermodynamic characteristics of the daily evolution of historical extreme HWs in this region via the heat budget equation. HWs are generally characterized by the occurrence of anticyclonic circulation anomaly throughout the troposphere and positive air temperature anomaly with the maximum amplitude in the boundary layer. The anticyclonic anomaly can induce compression heating in the entire troposphere and warm zonal advection in the boundary layer. Meanwhile, due to the reduced cloud cover, more shortwave radiation reaches the ground surface, and the sensible heat flux becomes an important source of diabatic heating before the onset of HWs. The accumulated excessive heat in the HWs is primarily damped through the emission of longwave radiation and meridional thermal advection. For the HW in August 2022, its extreme persistence is mainly caused by prolonged adiabatic heating, enhanced diabatic heating during the developing stage and weakened diabatic cooling during the decay stage. The upper-level portion of the anticyclonic circulation anomalies is linked to the strengthened South Asia High. After applying the state-of-the-art dynamic metric, i.e., local finite wave activity, we reveal that the formation of the anomalous South Asia High in August 2022 is associated with the Stokes drift flux rather than the dispersion of Rossby wave energy. This characteristic sets it apart from other extreme HWs.

Comparative study of energy balance in urban and forest areas in Central Amazonia

Data from two experimental sites in central Amazonia were used, one located in a forested region and the other in an urban region. The values of the radiation and energy balance components were measured at both sites. The observed components of the radiation balance in the forest, and urban areas were quite different. The city, the radiative (albedo and emissivity) and thermal (absorptivity) parameters of the surface produced greater reflection of solar radiation and emission of longwave radiation. Urban pollution reduced the incident solar radiation and increased the longwave radiation emitted by the atmosphere. The energy balance presented marked differences in the partition between sensible and latent heat flux between forest and city. In the forest, much of the available energy is converted into latent heat flux, due to the process of evapotranspiration. Whereas, in the city, energy is equally divided into sensible and latent heat fluxes.

Quantifying the cooling effect of tropical cyclone clouds on the climate system

The net effect on the upwelling radiation caused by tropical cyclone clouds is calculated over a 20-year global data set, and the corresponding contribution to the earth energy balance is analyzed. Tropical cyclone clouds are shown on average to increase the upwelling radiation at the top of the atmosphere compared with the background non-tropical-cyclone-cloud climatology. This increase in upwelling radiation provides an overall cooling effect on the climate system because the increased reflected shortwave radiation (cooling) outweighs the decreased emitted longwave radiation (warming). While the effect neglects the (likely considerable) contribution due to tropical cyclone drying, the amount of cooling by clouds alone represents a considerable fraction of the excess warming energy in the climate system. Thus, any future change in tropical cyclone activity has the potential to impact the overall energy balance if it substantially alters this total. The seasonal and geographic distribution of warming and cooling effects, and the diurnal dynamics that impact whether any particular cyclone is net cooling or net warming are discussed in this study.

All-sky longwave radiation modelling based on infrared images and machine learning

Sky longwave radiation is an important input parameter for heat transfer and radiant energy balance calculations of the building envelope, and it has a significant effect on the accuracy of predictions of the energy consumption of buildings. The ability of the sky to emit longwave radiation needs to be accurately quantified under different weather conditions. In this study, a low-cost sky cloudiness observation platform based on an infrared imager was established, and an algorithm for calculating the cloudiness of the sky dome by stitching and processing infrared images was proposed. A machine learning model was developed by collecting local meteorological data; these data were then input into the synthetic minority oversampling technique algorithm. This model can estimate sky cloudiness based on conventional meteorological parameters. Nine different machine learning algorithms were tested, and a cloudiness prediction model based on the XGBoost algorithm was finally established, which had a prediction accuracy of 88.81%. Furthermore, an all-sky longwave radiation model was developed by introducing cloudiness parameters. The applicability of different longwave radiation models for clear and cloudy skies in subtropical climates was examined, and the coefficient values of the different models were modified. Finally, the formula applicable to subtropical climates was determined via a fitting method.

Agrivoltaics: Modeling the relative importance of longwave radiation from solar panels.

Agrivoltaics, which integrate photovoltaic power production with agriculture in the same plot of land, have the potential to reduce land competition, reduce crop irrigation, and increase solar panel efficiency. To optimize agrivoltaic systems for crop growth, energy pathways must be characterized. While the solar panels shade the crops, they also emit longwave radiation and partially block the ground from downwelling longwave radiation. A deeper understanding of the spatial variation in incoming energy would enable controlled allocation of energy in the design of agrivoltaic systems. The model also demonstrates that longwave energy should not be neglected when considering a full energy balance on the soil under solar panels.

The impact of different playing surfaces on physiological parameters in collegiate DI American football athletes

Heat conditioning aids in acclimatization to support health and performance, yet heat safety is an important factor. This quasi-experimental pilot study investigated differences in micro-environmental conditions and physiological outcomes in n = 5 Division I collegiate American football players over different playing surfaces during summertime in the southwest U.S. Participants performed three practice sessions on hot days (∼33°C): outdoors on artificial turf (AT) and natural grass (NG); and an indoor dome (ID). Microclimate parameters, including net radiative fluxes, and physiological markers (core temperature (Tc), skin temperature (Tsk), heart rate, and hydration) were continuously and simultaneously monitored. Microclimate conditions varied across the three environments. Outdoors, the largest differences were observed in surface temperatures between AT and NG (67.0°C and 32.8°C, respectively), resulting in higher emitted longwave radiation (infrared heat), slightly increasing air temperatures. Indoors, the lack of radiation lessened the overall heat load, yet higher humidity and lower airflow were observed. Physiologically, similar baseline Tc and Tsk and self-reported heat stress levels were recorded. During exercise, a significantly higher Tsk value was found on the AT (higher heat load), followed by the NG (moderate) and ID (lowest). The same pattern was reflected in Tc, RPE, and self-reported heat stress, even with lower solar radiation on AT. No differences between environments were reported for estimated energy expenditure, pre/post-body weight, bodyweight loss, fluid intake, or sweat rate. Small changes in microclimates affect overall heat loads and measured and perceived heat stress, which coaches can use in decision-making for session type, heat safety, and/or acclimatization goals.

Large‐diameter trees affect snow duration in post‐fire old‐growth forests

AbstractSnow duration in post‐fire forests is influenced by neighbourhoods of trees, snags, and deadwood. We used annually resolved, spatially explicit tree and tree mortality data collected in an old‐growth, mixed‐conifer forest in the Sierra Nevada, California, that burned at low to moderate severity to calculate 10 tree neighbourhood metrics for neighbourhoods up to 40 m from snow depth and snow disappearance sampling points. We developed two linear mixed models, predicting snow disappearance timing as a function of tree neighbourhood, litter density, and simulated incoming solar radiation, and two multiple regression models explaining variation in snow depth as a function of tree neighbourhood. Higher densities of post‐fire large‐diameter snags within 10 m of a sampling point were related to higher snow depth (indicating reduced snow interception). Higher densities of large‐diameter trees within 5 m and larger amounts of litter were associated with shorter snow duration (indicating increased longwave radiation emittance and accelerated snow albedo decay). However, live trees with diameters >60 cm within 10 m of a snow disappearance sampling point were associated with a longer‐lasting spring snowpack. This suggests that, despite the local effects of canopy interception and emitted longwave radiation from boles of large trees, shading from their canopies may prolong snow duration over a larger area. Therefore, conservation of widely spaced, large‐diameter trees is important in old‐growth forests because they are resistant to fire and can enhance the seasonal duration of snowmelt.

Revisiting Topographic Horizons in the Era of Big Data and Parallel Computing

Widely used to calculate illumination geometry for estimates of solar and emitted longwave radiation, and for correcting remotely sensed data for topographic effects, digital elevation models are now extensive globally at 10 to 30 m spatial resolution and locally at spatial resolutions down to a few cm. Either globally, regionally, or locally, elevation datasets have many grid points. Many software packages calculate gradients over every grid cell or point, but in the mountains, shading by nearby terrain must also be assessed. Terrain may obscure a slope that would otherwise face the Sun. Four decades ago, a fast method to calculate topographic horizons at every point in an elevation grid required computations related only linearly to the size of the grid, but grids now have so many points that parallel computing still provides an advantage. Exploiting parallelism over terrain grids can employ alternative strategies: among columns of a rotated grid, or simultaneously at multiple rotation angles, or on different tiles of a grid. On a multi-processor machine, the improvement in computing time approaches 2/3 the number of processors deployed.

Playing on natural or artificial turf sports field? Assessing heat stress of children, young athletes, and adults in Hong Kong

Exercising in an unusually hot environment may aggravate exertional heat illness. Turf material significantly affects the microenvironment and heat-stress sensation of sports-field users. However, the difference in human- biometeorological effects between different sports-field turf materials demands further investigation. This study compared artificial (AT) with natural turf (NT) fields, investigating three age groups (children, young athletes, and adults), two physical activities (playing soccer and walking), and three heat stress indicators (HI, Heat Index; WBGT, Wet Bulb Globe Thermometer; and COMFA, COMfort FormulA). The results showed heat-stress underestimation by HI and WBGT. In contrast, COMFA, incorporating comprehensive environmental and human physiological parameters, provided a more targeted and reliable heat-stress assessment. COMFA indicated a longer heat-stress duration exercising at AT than NT. Compared to NT, children suffered a 24% longer “Extreme danger” duration at AT in sunny daytime. The AT-NT difference in human-biometeorological effect was limited concerning human convection, evaporation, metabolic heat, and emitted longwave radiation, but was considerable in human absorbed radiation. AT had lower albedo than NT, hence field users absorbed more upward longwave radiation but less upward shortwave radiation, highlighting important control by the radiant environment. NT sports fields are recommended for a healthy outdoor thermal environment, especially for children.

Water and Light‐Use Efficiency Are Enhanced Under Summer Coastal Fog in a California Agricultural System

AbstractIn coastal California, the peak growing season of economically important crops is concurrent with fog events, which buffer drought stress during the dry season. Coastal fog patterns are changing, so we quantified its effects on the energy, water, and carbon fluxes of a strawberry farm located in the fog‐belt of the Salinas Valley, California. We used Geostationary Operational Environmental Satellite (GOES) total albedo to detect and quantify large scale patterns of coastal fog. We used eddy covariance (EC) to quantify actual evapotranspiration and gross primary productivity (GPP) at the field scale (approximately 0.5–3 hectares) from June to September 2016. We measured canopy‐scale (approximately 0.6 m2) strawberry physiology on foggy and non‐foggy days within the measurement footprint of the EC tower. Downwelling longwave radiation (L↓), observed by a surface‐mounted pyrgeometer, was consistently higher on foggy compared to clear‐sky days (regardless of fog‐drip), indicating that emission of longwave radiation was derived almost entirely from the cloud base. L↓ and total GOES albedo were positively and strongly correlated (R2 = 0.68, P < 0.01). For both field‐ and canopy‐scales, water‐use and light‐use efficiency increased by as much as 50% and 70%, respectively, during foggy compared to non‐foggy conditions. The initial slope of the curvilinear relationship fit between GPP and photosynthetically active radiation was twice as steep during foggy (α = 0.0395) than non‐foggy (α = 0.0210) conditions, suggesting that the scattering of light during fog events enhances photosynthetic output of whole‐plants. Our results suggest that irrigation for these fields could be rescheduled during foggy periods without sacrificing plant productivity.

Temperature and Short- wave Irradiation Trends in Ikogosi Climatic Change Pattern

Every living or non-living thing has its unique temperature consequent of absorption of electromagnetic radiation, light. The source of this molecular kinetic is Solar energy incident on Earth’s surface, the sole supplier of life on the planet. This phenomenon markedly determines the climatic conditions in our habitat. Atypical of previous much-required characteristic balance in nature, myriads of surface processes, ranging from evaporation, photosynthesis and even terrestrial carbon uptake effects are becoming altered in nature. These effects on long-term timing of events defines global warming, it distinguishes the diurnal from seasonal course of surface temperatures and shortwave radiation which contains larger quantity of energy and longwave radiation which holds less amount of energy. Earth’s emitted longwave radiation, also has major practical implications on solar energy technologies, agricultural productivity, profound environmental, societal, and economic implications. There is cumulative evidence that the volume of solar radiation incident on the Earth’s surface is not constant but experiences substantial decadal variations. In view of the certainty of unpredictability of these climatic changes, a positive recourse lies in utilizing these enormous natural forces to solve Man’s energy crisis challenge. Thus, this study uses meteorological data from MERRE to investigate the prospect of shortwave radiation in Ikogosi SW Nigeria and its implications technologically and environmentally in the future of energy prospecting first locally, and on a spatial scale.

Effective Radiative Forcing in a GCM With Fixed Surface Temperatures

AbstractEffective radiative forcing (ERF) is evaluated in the ACCESS1.0 General Circulation Model (GCM) with fixed land and sea‐surface‐temperatures (SST) as well as sea‐ice. The 4xCO2 ERF is 8.0 W m−2. In contrast, a typical ERF experiment with only fixed SST and sea‐ice gives rise to an ERF of only 7.0 W m−2. This difference arises due to the influence of land warming in the commonly used fixed‐SST ERF experimental design, which results in: (i) increased emission of longwave radiation to space from the land surface (−0.45 W m−2) and troposphere (−0.90 W m−2), (ii) reduced land snow‐cover and albedo (+0.17 W m−2), (iii) increased water‐vapor (+0.49 W m−2), and (iv) a cloud adjustment (−0.26 W m−2) due to reduced stability and cloudiness over land (positive ERF) counteracted by increased lower tropospheric stability and marine cloudiness over oceans (negative ERF). The sum of these radiative adjustments to land warming is to reduce the 4xCO2 ERF in fixed‐SST experiments by ∼1.0 W m−2. CO2 stomatal effects are quantified and found to contribute just over half of the land warming effect and adjustments in the fixed‐SST ERF experimental design in this model. The basic physical mechanisms in response to land warming are confirmed in a solar ERF experiment. We test various methods that have been proposed to account for land warming in fixed‐SST ERFs against our GCM results and discuss their strengths and weaknesses.

Comparison of different techniques for estimation of incoming longwave radiation

Global warming and climate change have left developing countries fragile in terms of agricultural production, and this vulnerability is expected to increase in the near future. The surface energy budget approach is a different perspective to the investigation of energy change over a landscape. In terms of budget items, the net radiation absorbed by the earth is equal to the difference between the sum of the incoming shortwave and longwave radiation and the sum of the reflected shortwave and emitted longwave radiation. The longwave radiation has important effects on dew deposition and drying on crop leaves in agricultural meteorology. A pyranometer provides routine measurement of the daytime radiation, but the longwave part of this radiation cannot be so readily measured at night time. In this study, multiple linear regression, artificial neural networks, deep learning, adaptive network-based fuzzy inference systems (ANFIS) and empirical models have been applied to model and estimate the mean incoming longwave radiation using atmospheric parameters. The ANFIS model appears to show good agreement between the measured and the estimated values for all days considered than other models.

Estudo do colapso da turbulência em um canal aberto estratificado

Shortly after sunset the surface starts its cooling by the emission of longwave radiation. As a result of this process, the air layers along with the surface are cooled, thus giving rise to a Stable Boundary Layer (SBL). This paper aims to perform a numerical simulation of a turbulent flow and observe the effects of stratification on it. To perform this study, a reproduction of a flow in an open channel was made due the fact that the upper wall did not influence the flow, in which a temperature gradient between the lower and upper plates was applied. The CFD software used in this paper was OpenFOAM 2.4.0. The most appropriate solver for the present study is the buoyantPimpleFoam, which is suitable for compressible, turbulent, transient and heat transfer flows. Analyzing the results, it can be observed that before the thermal stratification process was performed the levels are coupled as a function of the turbulence. When this occurs, it causes greater velocity and temperature homogenization in the central region of the domain. For larger temperature gradients, it can be observed a laminarization of the flow due the thermal forcing being bigger than the mechanical ones.

Changes in clouds and thermodynamics under solar geoengineering and implications for required solar reduction

Abstract. The amount of solar constant reduction required to offset the global warming from an increase in atmospheric CO2 concentration is an interesting question with implications for assessing the feasibility of solar geoengineering scenarios and for improving our theoretical understanding of Earth's climate response to greenhouse gas and solar forcings. This study investigates this question by analyzing the results of 11 coupled atmosphere–ocean global climate models running experiment G1 of the Geoengineering Model Intercomparison Project, in which CO2 concentrations are abruptly quadrupled and the solar constant is simultaneously reduced by an amount tuned to maintain the top-of-atmosphere energy balance and pre-industrial global mean temperature. The required solar constant reduction in G1 is between 3.2 % and 5.0 %, depending on the model, and is uncorrelated with the models' equilibrium climate sensitivity, while a formula from the experiment specifications based on the models' effective CO2 forcing and planetary albedo is well correlated with but consistently underpredicts the required solar reduction. We propose an explanation for the required solar reduction based on CO2 instantaneous forcing and the sum of radiative adjustments to the combined CO2 and solar forcings. We quantify these radiative adjustments in G1 using established methods and explore changes in atmospheric temperature, humidity, and cloud fraction in order to understand the causes of these radiative adjustments. The zonal mean temperature response in G1 exhibits cooling in the tropics and warming in high latitudes at the surface; greater cooling in the upper troposphere at all latitudes; and stratospheric cooling which is mainly due to the CO2 increase. Tropospheric specific humidity decreases due to the temperature decrease, while stratospheric humidity may increase or decrease depending on the model's temperature change in the tropical tropopause layer. Low cloud fraction decreases in all models in G1, an effect that is robust and widespread across ocean and vegetated land areas. We attribute this to a reduction in boundary layer inversion strength over the ocean, and a reduction in the release of water from plants due to the increased CO2. High cloud fraction increases in the global mean in most models. The low cloud fraction reduction and atmospheric temperature decrease have strong warming effects on the planet, due to reduced reflection of shortwave radiation and reduced emission of longwave radiation, respectively. About 50 % to 75 % of the temperature effect is caused by the stratospheric cooling, while the reduction in atmospheric humidity results in increased outgoing longwave radiation that roughly offsets the tropospheric temperature effect. The longwave (LW) effect of the cloud changes is small in the global mean, despite the increase in high cloud fraction. Taken together, the sum of the diagnosed radiative adjustments and the CO2 instantaneous forcing explains the required solar forcing in G1 to within about 6 %. The cloud fraction response to the G1 experiment raises interesting questions about cloud rapid adjustments and feedbacks under solar versus greenhouse forcings, which would be best explored in a model intercomparison framework with a solar-forcing-only experiment.

Observations and simulations of the seasonal evolution of snowpack cold content and its relation to snowmelt and the snowpack energy budget

Abstract. Cold content is a measure of a snowpack's energy deficit and is a linear function of snowpack mass and temperature. Positive energy fluxes into a snowpack must first satisfy the remaining energy deficit before snowmelt runoff begins, making cold content a key component of the snowpack energy budget. Nevertheless, uncertainty surrounds cold content development and its relationship to snowmelt, likely because of a lack of direct observations. This work clarifies the controls exerted by air temperature, precipitation, and negative energy fluxes on cold content development and quantifies the relationship between cold content and snowmelt timing and rate at daily to seasonal timescales. The analysis presented herein leverages a unique long-term snow pit record along with validated output from the SNOWPACK model forced with 23 water years (1991–2013) of quality controlled, infilled hourly meteorological data from an alpine and subalpine site in the Colorado Rocky Mountains. The results indicated that precipitation exerted the primary control on cold content development at our two sites with snowfall responsible for 84.4 and 73.0 % of simulated daily gains in the alpine and subalpine, respectively. A negative surface energy balance – primarily driven by sublimation and longwave radiation emission from the snowpack – during days without snowfall provided a secondary pathway for cold content development, and was responsible for the remaining 15.6 and 27.0 % of cold content additions. Non-zero cold content values were associated with reduced snowmelt rates and delayed snowmelt onset at daily to sub-seasonal timescales, while peak cold content magnitude had no significant relationship to seasonal snowmelt timing. These results suggest that the information provided by cold content observations and/or simulations is most relevant to snowmelt processes at shorter timescales, and may help water resource managers to better predict melt onset and rate.