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The study comprehensively evaluates low-cost CO2 sensors from different price tiers, assessing their performance against a reference-grade instrument and exploring the possibility of calibration using different machine learning techniques. Three sensors (Sunrise AB by Senseair, K30 CO2 by Senseair, and GMP 343 by Vaisala) were tested alongside a reference instrument (Los Gatos precision greenhouse gas analyzer). The results revealed differences in sensor performance, with the higher cost Vaisala sensors exhibiting superior accuracy. Despite its lower price, the Sunrise sensors still demonstrated reasonable accuracy. Meanwhile, the K30 sensor measurements displayed higher variability and noise. Machine learning models, including linear regression, gradient boosting regression, and random forest regression, were employed for sensor calibration. In general, linear regression models performed best for extrapolating data, whereas decision tree-based models were generally more useful in handling non-linear datasets. Notably, a stack ensemble model combining these techniques outperformed the individual models and significantly improved sensor accuracy by approximately 65%. Overall, this study contributes to filling the gap in intercomparing CO2 sensors across different price categories and underscores the potential of machine learning for enhancing sensor accuracy, particularly in low-cost sensor applications.

Abstract Vapor buildup in the atmosphere and faster warming over land than over ocean are ubiquitous features of climate change. This combination is a threat to society because the associated heat stress may exceed the limit for human survival. The heat stress due to high humidity and high temperature is quantified with the wet‐bulb temperature (Tw). A common view is that the land Tw should change at the same rate as the ocean surface temperature (Ts). Using climate model data and atmospheric observations, we show that the land Tw increases 17% faster than Ts. This amplification arises from stronger downward longwave radiation (L↓) at the surface in a warmer climate, which causes moist static energy to accumulate in the atmospheric boundary layer. We also find that L↓ is a better predictor of Tw than Ts at interannual to decadal time scales. These relationships are robust across climates and across model simulations.

Abstract Subgrid data from earth system models are a powerful, yet underutilized, data resource for investigating the climatic impacts of land use and land cover change (LULCC). In this paper, we describe a global dataset on subgrid land surface climate variables produced by the Community Earth System Model in a fully coupled mode. The simulation was conducted at a 0.9° × 1.25° resolution under the Representative Concentration Pathway (RCP) 8.5 scenario from 2015 to 2100. Data are archived for eight subgrid tiles (urban, rural, tree, grass, shrub, bare soil, crop and lake) and include variables on the physical state, surface energy fluxes, runoff and atmospheric forcing conditions. Archival intervals are monthly, daily and hourly. Meta data on land surface parameters are also available. The data files are stored in NetCDF‐4 (Network Common Data Form, version 4) format and the meta data follow the latest Coupled Model Intercomparison Project phase 6 standards. We anticipate that this dataset will become a useful resource for characterizing local climate changes due to LULCC. This dataset can be downloaded from the Harvard Dataverse (https://doi.org/10.7910/DVN/HUXAH6).

The rate of surface warming in cities exceeds that in rural areas, but urban greening can partly offset enhanced urban warming that is driven by climate change and urban expansion, according to an analysis of satellite land surface temperature data in 2000 city clusters between 2002 and 2021.

Measurement(s) radiation components at the surface Technology Type(s) machine learning Factor Type(s) radiation Sample Characteristic - Environment climate Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.15090462

There is some disagreement between climate models about how much precipitation changes under global warming. Here, the authors use the ocean surface energy balance to constrain the sensitivity of precipitation to historical warming and find that it is increasing by 0.68 ± 0.51% per degree warming.

Accounting for temporal changes in carbon dioxide (CO _2 ) effluxes from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of flux measurements of CO _2 based on the eddy covariance method from 13 lakes and reservoirs in the Northern Hemisphere, and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO _2 flux at all sites. By accounting for diel variation, only 11% of site-months were net daily sinks of CO _2 . Annual CO _2 emissions had an average of 25% (range 3%–58%) interannual variation. Similar to studies on streams, nighttime emissions regularly exceeded daytime emissions. Biophysical regulations of CO _2 flux variability were delineated through mutual information analysis. Sample analysis of CO _2 fluxes indicate the importance of continuous measurements. Better characterization of short- and long-term variability is necessary to understand and improve detection of temporal changes of CO _2 fluxes in response to natural and anthropogenic drivers. Our results indicate that existing global lake carbon budgets relying primarily on daytime measurements yield underestimates of net emissions.

Abstract Aerosols can enhance terrestrial productivity through increased absorption of solar radiation by the shaded portion of the plant canopy—the diffuse radiation fertilization effect. Although this process can, in principle, alter surface evaporation due to the coupling between plant water loss and carbon uptake, with the potential to change the surface temperature, aerosol‐climate interactions have been traditionally viewed in light of the radiative effects within the atmosphere. Here, we develop a modeling framework that combines global atmosphere and land model simulations with a conceptual diagnostic tool to investigate these interactions from a surface energy budget perspective. Aerosols increase the terrestrial evaporative fraction, or the portion of net incoming energy consumed by evaporation, by over 4% globally and as much as ∼40% regionally. The main mechanism for this is the increase in energy allocation from sensible to latent heat due to global dimming (reduction in global shortwave radiation) and slightly augmented by diffuse radiation fertilization. In regions with moderately dense vegetation (leaf area index >2), the local surface cooling response to aerosols is dominated by this evaporative pathway, not the reduction in incident radiation. Diffuse radiation fertilization alone has a stronger impact on gross primary productivity (+2.18 Pg C y−1 or +1.8%) than on land evaporation (+0.18 W m−2 or +0.48%) and surface temperature (−0.01 K). Our results suggest that it is important for land surface models to distinguish between quantity (change in total magnitude) and quality (change in diffuse fraction) of radiative forcing for properly simulating surface climate.

Abstract The isotopic composition of surface water vapor flux (δE) is a quantity frequently used to investigate the local and regional water cycle. This study reports the results of a comparative evaluation of δE determined with the Keeling plot and the flux‐gradient methods using high‐frequency data collected at a cropland site and a lake site. Three regression models, ordinary least squares (OLS), York's solution (YS), and geometric mean regression, were tested with the Keeling plot method. Results show that concentration‐dependent field characterization of measurement errors can improve the estimation of the YS regression. For both sites, broad agreement was achieved among the Keeling plot method with YS regression, the Keeling plot method with OLS regression and the flux‐gradient method. For the lake site, OLS was the least biased of the three regression models in reference to the δE calculated by the Craig‐Gordon (CG) model of isotopic evaporation of open water. Good agreement was aslo achieved between the flux‐gradient method and the CG model at the lake site under open‐fetch conditions. A footprint analysis suggests that the Keeling method with OLS regression may be less sensitive to fetch than the flux‐gradient method.

The concentration of fine particulate matter (PM2.5) is known to vary spatially across a city landscape. Current networks of regulatory air quality monitoring are too sparse to capture these intra-city variations. In this study, we developed a low-cost (60 USD) portable PM2.5 monitor called Smart-P, for use on bicycles, with the goal of mapping street-level variations in PM2.5 concentration. The Smart-P is compact in size (85 × 85 × 42 mm) and light in weight (147 g). Data communication and geolocation are achieved with the cyclist’s smartphone with the help of a user-friendly app. Good agreement was observed between the Smart-P monitors and a regulatory-grade monitor (mean bias error: −3.0 to 1.5 μg m−3 for the four monitors tested) in ambient conditions with relative humidity ranging from 38 to 100%. Monitor performance decreased in humidity > 70% condition. The measurement precision, represented as coefficient of variation, was 6 to 9% in stationary mode and 6% in biking mode across the four tested monitors. Street tests in a city with low background PM2.5 concentrations (8 to 9 μg m−3) and in two cities with high background concentrations (41 to 74 μg m−3) showed that the Smart-P was capable of observing local emission hotspots and that its measurement was not sensitive to bicycle speed. The low-cost and user-friendly nature are two features that make the Smart-P a good choice for empowering citizen scientists to participate in local air quality monitoring.

Abstract Lakes differ from lands in water availability, heat capacity, albedo, and roughness, which affect local surface‐atmospheric interactions. This study modified a multilevel upper ocean model (UOM) for lake applications and evaluated its performance in Lake Taihu (China) with comprehensive measurements against three popular one‐dimensional (1‐D) lake models. These models were based on different concepts, including the self‐similarity (FLake), the wind‐driven eddy diffusion (LISSS), the k‐ε turbulence closure (SIMSTRAT), and a simplified turbulence closure (UOM). The surface flux scheme in these models was unified to exclude the discrepancies in representing air‐lake exchanges. All models in their default formulations presented obvious cold water temperature biases and largely underestimated the lake surface temperature (LST) diurnal range. For each model, these deficiencies were significantly reduced by incorporating new physics schemes or calibrated tunable parameters based on systematic sensitivity tests. The primary modifications for UOM included (1) a new scheme of decreased surface roughness lengths to better characterize the shallow lake, (2) a solar radiation penetration scheme with increased light extinction coefficient and surface absorption fraction to account for the high water turbidity, and (3) turbulent Prandtl number increased by a factor of 20 to reduce the turbulent vertical mixing. All other models were improved in these three aspects (roughness, extinction, and mixing) within their original formulations. Given these improvements, UOM showed superior performance to other models in capturing LST diurnal cycle and daily to seasonal variations, as well as summer‐autumn vertical stratification changes. The new UOM is well suited for application in shallow lakes.

The trade-off between spatial and temporal resolutions of satellite imagery is a long-standing problem in satellite remote sensing applications. The lack of daily land surface temperature (LST) data with fine spatial resolution has hampered the understanding of surface climatic phenomena, such as the urban heat island (UHI). Here, we developed a fusion framework, characterized by a scale-separating process, to generate LST data with high spatiotemporal resolution. The scale-separating framework breaks the fusion task into three steps to address errors at multiple spatial scales, with a specific focus on intra-scene variations of LST. The framework was experimented with MODIS and Landsat LST data. It first removed inter-sensor biases, which depend on season and on land use type (urban versus rural), and then produced a Landsat-like sharpened LST map for days when MOIDS observations are available. The sharpened images achieved a high accuracy, with a RMSE of 0.91 K for a challenging heterogeneous landscape (urban area). A comparison between the sharpened LST and the air temperature measured with bicycle-mounted mobile sensors revealed the roles of impervious surface fraction and wind speed in controlling the surface-to-air temperature gradient in an urban landscape.

Abstract Stable isotopes of water (H2O, HDO, and H218O) are tracers that provide powerful constraints on water transport processes in the atmosphere. This paper presents a description of an atmospheric boundary layer (ABL) simulation system called ISOLESC that couples water isotope fractionation processes with a land surface model, a large eddy simulation model, and a two‐moment cloud microphysics parameterization. Results from two model configurations—one with shallow precipitating cumulus and the other for a cloud‐free ABL—are presented to evaluate the model performance and determine its sensitivity to isotopic parameterizations. The coupled model successfully reproduces important ABL statistics (ABL height, cloud fraction, and cloud liquid water content), the expected effects of mixing and fractionation on the time evolution of ABL vapor isotopic composition, and observed diurnal variations of near‐surface water vapor isotopic composition. For the current configuration, nondiscriminating entrainment contributes 17% to the subdaily time variation of near‐surface vapor deuterium excess, while surface evapotranspiration contributes 83%. The isotopic compositions of water vapor and cloud water are insensitive to mesh resolution, but the profiles of cloud water specific humidity, rainwater specific humidity, and its isotopic ratios show moderate response to changes in grid size. Since ISOLESC resolves the energy containing scales of turbulent motions in the ABL and incorporates microphysical processes, it can be used for constraining ABL parameterizations. We find that a further improvement of raindrop reevaporation in the current cloud microphysical scheme is required in order to produce realistic near‐surface raindrop deuterium excess for the case simulated here. We suggest that ISOLESC provides a quantitative framework for utilizing vapor‐phase isotopic measurements to study local hydrological processes.

The COVID-19 lockdowns drastically reduced human activity, emulating a controlled experiment on human–land–atmosphere coupling. Here, using a fusion of satellite and reanalysis products, we examine this coupling through changes in the surface energy budget during the lockdown (1 April to 15 May 2020) in the Indo-Gangetic Basin, one of the world’s most populated and polluted regions. During the lockdown, the reduction (>10%) in columnar air pollution compared to a five year baseline, expected to increase incoming solar radiation, was counteracted by a ∼30% enhancement in cloud cover, causing little change in available energy at the surface. More importantly, the delay in winter crop harvesting during the lockdown increased surface vegetation cover, causing almost half the regional cooling via evapotranspiration. Since this cooling was higher for rural areas, the daytime surface urban heat island (SUHI) intensity increased (by 0.20–0.41 K) during a period of reduced human activity. Our study provides strong observational evidence of the influence of agricultural activity on rural climate in this region and its indirect impact on the SUHI intensity.

The impact of locally-sourced aerosols on the Urban Heat Island (UHI) effect has been difficult to quantify due to opposing long and shortwave radiation effects. Here, using satellite observations and climate model simulations, the authors reveal that urban haze pollution intensifies the nighttime UHI in China.

Unmanned aerial vehicles (UAVs) support a large array of technological applications and scientific studies due to their ability to collect high-resolution image data. The processing of UAV data requires the use of mosaicking technology, such as structure-from-motion, which combines multiple photos to form a single image mosaic and to construct a 3-D digital model of the measurement target. However, the mosaicking of thermal images is challenging due to low lens resolution and weak contrast in the single thermal band. In this study, a novel method, referred to as four-band thermal mosaicking (FTM), was developed in order to process thermal images. The method stacks the thermal band obtained by a thermal camera onto the RGB bands acquired on the same flight by an RGB camera and mosaics the four bands simultaneously. An object-based calibration method is then used to eliminate inter-band positional errors. A UAV flight over a natural park was carried out in order to test the method. The results demonstrated that with the assistance of the high-resolution RGB bands, the method enabled successful and efficient thermal mosaicking. Transect analysis revealed an inter-band accuracy of 0.39 m or 0.68 times the ground pixel size of the thermal camera. A cluster analysis validated that the thermal mosaic captured the expected contrast of thermal properties between different surfaces within the scene.

Afforestation activities in the Kubuqi Desert, Inner Mongolia, China, have substantially increased tree and shrub coverage in this region. In this study, the response of the surface temperature to afforestation is simulated with the Weather Research and Forecasting model. The surface temperature changes are decomposed into contributions from the intrinsic surface biophysical effect and atmospheric feedback, using the theory of intrinsic biophysical mechanism. The effect of afforestation on the surface temperature is 1.34 K, −0.48 K, 2.09 K and 0.22 K for the summer daytime, the summer nighttime, the winter daytime and the winter nighttime, respectively, for the grid cells that have experienced conversion from bare soil to shrub. The corresponding domain mean values are 0.15 K, −0.2 K, 0.67 K, and 0.06 K. The seasonal variation of surface temperature change is mainly caused by changes in roughness and Bowen ratio. In the daytime, the surface temperature changes are dominated by the biophysical effect, with albedo change being the main biophysical factor. In the nighttime, the biophysical effect (mainly associated with roughness change) and the atmospheric feedback (mainly associated with change in the background air temperature) contribute similar amounts to the surface temperature changes. We conclude that the atmospheric feedback can amplify the influence of the surface biophysical effect, especially in the nighttime.

There remains significant uncertainty in the estimation of anthropogenic CH4 emissions at local and regional scales. We used atmospheric CH4 and CO2 concentration data to constrain the anthropogenic CH4 emission in the Yangtze River Delta one of the most populated and economically important regions in China. The observation of atmospheric CH4 and CO2 concentration was carried out from May 2012 to April 2017 at a rural site. A tracer correlation method was used to estimate the anthropogenic CH4 emission in this region, and compared this “top-down„ estimate with that obtained with the IPCC inventory method. The annual growth rates of the atmospheric CO2 and CH4 mole fractions are 2.5 ± 0.7 ppm year−1 and 9.5 ± 4.7 ppb year−1, respectively, which are 9% and 53% higher than the values obtained at Waliguan (WLG) station. The average annual anthropogenic CH4 emission is 4.37 (± 0.61) × 109 kg in the YRD (excluding rice cultivation). This “top-down„ estimate is 20–70% greater than the estimate based on the IPCC method. We suggest that possible sources for the discrepancy include low biases in the IPCC calculation of emission from landfills, ruminants and the transport sector.

Surface albedo is a critical parameter in surface energy balance, and albedo change is an important driver of changes in local climate. In this study, we developed a workflow for landscape albedo estimation using images acquired with a consumer-grade camera on board unmanned aerial vehicles (UAVs). Flight experiments were conducted at two sites in Connecticut, USA and the UAV-derived albedo was compared with the albedo obtained from a Landsat image acquired at about the same time as the UAV experiments. We find that the UAV estimate of the visibleband albedo of an urban playground (0.037 ± 0.063, mean ± standard deviation of pixel values) under clear sky conditions agrees reasonably well with the estimates based on the Landsat image (0.047 ± 0.012). However, because the cameras could only measure reflectance in three visible bands (blue, green, and red), the agreement is poor for shortwave albedo. We suggest that the deployment of a camera that is capable of detecting reflectance at a near-infrared waveband should improve the accuracy of the shortwave albedo estimation.

Turbulence statistics and spectra in a radiatively heated convective boundary layer (CBL) under aerosol pollution conditions are less investigated than their counterparts in the clear CBL. In this study, a large-eddy simulation (LES) coupled with an aerosol radiative transfer model is employed to determine the impact of aerosol radiative heating on CBL turbulence statistics. One-dimensional velocity spectra and velocity–temperature cospectra are invoked to characterize the turbulence flow in the CBL with varying aerosol pollution conditions. The results show that aerosol heating makes the profiles of turbulent heat flux curvilinear, while the total (turbulent plus radiative) heat flux profile retains the linear relationship with height throughout the CBL. The horizontal and vertical velocity variances are reduced significantly throughout the radiatively heated CBL with increased aerosol optical depth (AOD). The potential temperature variance is also reduced, especially in the entrainment zone and near the surface. The velocity spectral density tends to be smaller overall, and the peak of the velocity spectra is shifted toward larger wavenumbers as AOD increases. This shift reveals that the energy-containing turbulent eddies become smaller, which is also supported by visual inspection of the vertical velocity pattern over horizontal planes. The modified CBL turbulence scales for velocity and temperature are found to be applicable for normalizing the corresponding profiles, indicating that a correction factor for aerosol radiative heating is needed for capturing the general features of the CBL structure in the presence of aerosol radiative heating.

Climate models simulating continental scale deforestation suggest a warming effect of land clearing on the surface air temperature in the tropical zone and a cooling effect in the boreal zone due to different control of biogeochemical and biophysical processes. Ongoing land-use/cover changes mostly occur at local scales (hectares), and it is not clear whether the local-scale deforestation will generate temperature patterns consistent with the climate model results. Here we paired 40 and 12 flux sites with nearby weather stations in North and South America and in Eastern Asia, respectively, and quantified the temperature difference between these paired sites. Our goal was to investigate the response of the surface air temperature to local-scale (hectares) land clearing across latitudes using the surface weather stations as proxies for localized land clearing. The results show that north of 10°N, the annual mean temperature difference (open land minus forest) decreases with increasing latitude, but the temperature difference shrinks with latitude at a faster rate in the Americas [−0.079 (±0.010) °C per degree] than in Asia [−0.046 (±0.011) °C per degree]. Regression of the combined data suggests a transitional latitude of about 35.5°N that demarks deforestation warming to the south and cooling to the north. The warming in latitudes south of 35°N is associated with increase in the daily maximum temperature, with little change in the daily minimum temperature while the reverse is true in the boreal latitudes.