Urbanization (urban land change) alters local and regional climate through biophysical and biogeochemical processes and has broader climate impacts through atmospheric feedbacks. Despite its critical climate impacts, urban areas have rarely been explicitly represented in global-scale Earth system models, and physically-based transient urban representations are missing as well. The Community Earth System Model (CESM) has a physically based urban land parameterization – Community Land Model Urban (CLMU) – that is sufficiently detailed to represent the properties and processes in the urban environment. We improve this model by implementing a dynamic urban scheme to represent transient land use due to urbanization. Leveraging existing urbanization projection datasets, the new scheme allows urban extent to be updated annually during a climate simulation while conserving energy and mass balance during the transition. Land-only simulation results confirm the robustness of the new dynamic urban scheme and demonstrate the direct local climate effects induced by urban land expansion. In the appendix of this paper, we also document two recent improvements to the building energy scheme of CLMU.

Radiative skin temperature is often used to examine heat exposure in multi-city studies and for informing urban heat management efforts since urban air temperature is rarely measured at the appropriate scales. Cities also have lower relative humidity, which is not traditionally accounted for in large-scale observational urban heat risk assessments. Here using crowdsourced measurements from over 40,000 weather stations in ≈600 urban clusters in Europe, we show the moderating effect of this urbanization-induced humidity reduction on heat stress during the 2019 heatwave. We demonstrate that daytime differences in heat index between urban clusters and their surroundings are weak and associations of this urban-rural difference with background climate, generally examined from the skin temperature perspective, is diminished due to moisture feedback. We also examine the spatial variability of skin temperature, air temperature, and heat indices within these clusters, relevant for detecting hotspots and potential disparities in heat exposure, and find that skin temperature is a poor proxy for the intra-urban distribution of heat stress. Finally, urban vegetation shows much weaker (~1/6th as strong) associations with heat stress than with skin temperature, which has broad implications for optimizing urban heat mitigation strategies. Our results are valid for both operational metrics of heat stress (such as apparent temperature and Humidex) and for various empirical heat indices from epidemiological studies. This study provide large-scale empirical evidence that skin temperature, used due to the lack of better alternatives, is weakly suitable for informing heat mitigation strategies within and across cities, necessitating more urban meteorological observations.

Accounting for temporal changes in carbon dioxide (CO2) emissions from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of eddy covariance flux measurements of CO2 from 13 lakes and reservoirs in the Northern Hemisphere (NH) and quantify the magnitude and dynamics at multiple temporal scales. We found pronounced diel and sub-monthly oscillatory variations in CO2 flux at all sites. Diel variation converted sites to daily net sinks of CO2 in only 11% of site-months. Upscaled annual emissions had an average of 25% (range 3-58%) interannual variation. Given temporal variation remains under-represented in inventories of CO2 emissions from lakes and reservoirs, revisions in CO2 flux are needed using a better representation of sub-daily to interannual variability. Constraining short- and long-term variability is necessary to improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers.

Accounting for temporal changes in carbon dioxide (CO2) emissions from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of eddy covariance flux measurements of CO2 from 13 lakes and reservoirs in the Northern Hemisphere (NH) and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO2 flux at all sites. Accounting for diel variation, only 11% of site-months were net daily sinks of CO2. Annual CO2 emissions had an average of 25% (range 3-58%) interannual variation. Nighttime emissions regularly exceeded daytime emissions. Sources of CO2 flux variability were delineated through mutual information analysis. Sample analysis of CO2 fluxes indicate importance of continuous sampling. Constraining short- and long-term variability is necessary to improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers.

The diffuse radiation fertilization effect – the increase in plant productivity in the presence of higher diffuse radiation (K↓,d) – is an important yet understudied aspect of atmosphere-biosphere interactions and can modify the terrestrial carbon, energy, and water budgets. The K↓,d fertilization effect links the carbon cycle with clouds and aerosols, all of which are large sources of uncertainties for our current understanding of the Earth system and for future climate projections. Here we establish to what extent observational and modeling uncertainty in sunlight’s diffuse fraction (kd) affects simulated gross primary productivity (GPP) and terrestrial evapotranspiration (λE). We find only 48 eddy covariance sites with simultaneous sufficient measurements of K↓,d with none in the tropical climate zone, making it difficult to constrain this mechanism globally using observations. Using a land modeling framework based on the latest version of the Community Land Model, we find that global GPP ranges from 114 Pg C year-1 when using kd forcing from the MERRA-2 reanalysis to a ~7% higher value of 122 Pg C year-1 when using the CERES satellite product, with especially strong differences apparent over the tropical region (mean increase ~9%). The differences in λE, although smaller (-0.4%) due to competing changes in shaded and sunlit leaf transpiration, can be greater than regional impacts of individual forcing agents like aerosols. Our results demonstrate the importance of comprehensively and systematically validating the simulated kd by atmosphere modules as well as the response differences in diffuse fraction within land modules across Earth System Models.

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 (GMR), were tested with the Keeling plot method. Results show that field characterization of measurement errors can improve the estimation of the YS regression. For both sites, broad agreement was achieved between 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 model of isotopic evaporation of open water. These results favor the OLS over the YS regression for studies of isotopic evaporation when measurement errors in field conditions are unavailable.

The isotopic composition of surface water vapor flux (δ) is a quantity frequently used to investigate the local and regional water cycle. In this study, the δ determined with the Keeling method was evaluated against the flux-gradient method and the Craig-Gordon model prediction. Previous studies have shown that the choice of regression fitting methods can bias the δ intercept results and precision of the Keeling method. Here, the Keeling method was applied to high-frequency (0.2 to 1 Hz) data measured at a cropland and a lake site to test different regression methods. Results show that the Keeling method with the York’s solution (YS) and the ordinary least squares (OLS) regression produced robust estimates of δ when compared with the flux-gradient method. Increasing concentration range reduced the standard error of estimate but did not bring obvious improvement to the bias error for the YS and OLS regression. The Keeling result was better using data from two sampling heights than only one. There was evidence that the Keeling method with the OLS regression slightly outperformed the flux-gradient method during periods with small vertical vapor gradient. Results also show that the Keeling method with the geometric mean regression gave highly biased estimate of δ for the types of isotope ratio infrared spectroscopy analyzer deployed in this study. These results can inform δ calculations and future experimental designs.