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Abstract Altered surface albedo due to land cover conversions and management is a significant driver of global climate change. Albedo can be directly measured at ground stations, and remote sensing data can be used to scale-up albedo values to regional and global levels. Some previous studies have retrieved fine resolution (10–30 m) instantaneous albedo and coarse resolution (500–1000 m) daily mean albedo from remote sensing data, but they all required the input of MODIS albedo information at 500 m resolution, and none have assembled both instantaneous and daily albedo based exclusively on fine resolution satellite data. To address this issue, we compiled 387 instantaneous and 346 daily albedo records using field net radiometer measurements from the bioenergy croplands at the W. K. Kellogg Biological Station in southwest Michigan. We then connected these albedo records with a suite of variables derived from Harmonized Landsat and Sentinel-2 data through two machine learning algorithms (random forest regression and extreme gradient boosting) to retrieve clear-sky instantaneous and daily shortwave broadband albedo. The performance statistics indicate reasonable accuracy of model results (RMSE around or below 0.03 except for snow-covered surfaces), suggesting that the retrieval of both instantaneous and daily albedo based exclusively on fine resolution satellite data is promising. To facilitate the use of fine resolution albedo products at the global level, future efforts need to include more albedo records of diverse surface cover types, as well as to accurately model daily albedo for cloudy days to address the “clear-sky bias”. 

Abstract Holocene hydroclimate changes in the western US are poorly constrained, in part because the region's complex topography and seasonally distinct circulation patterns can generate heterogenous responses over small spatial scales. To better understand regional precipitation patterns over the Holocene, we measured the triple oxygen isotopes of goethite from high‐elevation ferricrete deposits across a latitudinal gradient in the Rocky Mountains. Ferricretes form in stream water that reflects the isotopic composition of local precipitation, providing a record of past hydroclimate changes. We also used a 1‐dimensional vapor transport model to simulate precipitation δ¹⁸O and Δ′¹⁷O along a hypothetical storm track, given varying moisture recycling amounts and evaporation versus transpiration contributions. We find that both northern (central Montana) and southern (southwest Colorado) sites exhibit similar increases in goethite δ¹⁸O over time, accompanied by declines in goethite Δ′¹⁷O. To interpret these trends, we considered three potential drivers: temperature, moisture recycling and evapotranspiration partitioning, and moisture source. Δ′¹⁷O is largely insensitive to temperature, and our model shows that changes in moisture recycling and evapotranspiration partitioning along the storm track cannot account for the observed Δ′¹⁷O decreases. Instead, we conclude that the isotopic trends primarily reflect an increased contribution of southerly‐sourced precipitation, since moisture from the Gulf of Mexico—the dominant source of interior western US summer precipitation—is characterized by high δ¹⁸O and low Δ′¹⁷O relative to Pacific moisture. More generally, this work demonstrates how triple oxygen isotopes can be used to disentangle changes in temperature, atmospheric moisture dynamics, and moisture source in semi‐arid to arid regions. 

Carbon dioxide removal technologies such as bioenergy with carbon capture and storage (BECCS) are required if the effects of climate change are to be reversed over the next century. However, BECCS demands extensive land use change that may create positive or negative radiative forcing impacts upstream of the BECCS facility through changes to in situ greenhouse gas fluxes and land surface albedo. When quantifying these upstream climate impacts, even at a single site, different methods can give different estimates. Here we show how three common methods for estimating the net ecosystem carbon balance of bioenergy crops established on former grassland or former cropland can differ in their central estimates and uncertainty. We place these net ecosystem carbon balance forcings in the context of associated radiative forcings from changes to soil N2O and CH4 fluxes, land surface albedo, embedded fossil fuel use, and geologically stored carbon. Results from long term eddy covariance measurements, a soil and plant carbon inventory, and the MEMS 2 process-based ecosystem model all agree that establishing perennials such as switchgrass or mixed prairie on former cropland resulted in net negative radiative forcing (i.e., global cooling) of -26.5 to -39.6 fW m-2 over 100 years. Establishing these perennials on former grassland sites had similar climate mitigation impacts of -19.3 to -42.5 fW m-2. However, the largest climate mitigation came from establishing corn for BECCS on former cropland or grassland, with radiative forcings from -38.4 to -50.5 fW m-2, due to its higher plant productivity and therefore more geologically stored carbon. Our results highlight the strengths and limitations of each method for quantifying the field scale climate impacts of BECCS and show that utilizing multiple methods can increase confidence in the final radiative forcing estimates. 

ABSTRACT Carbon dioxide removal technologies such as bioenergy with carbon capture and storage (BECCS) are required if the effects of climate change are to be reversed over the next century. However, BECCS demands extensive land use change that may create positive or negative radiative forcing impacts upstream of the BECCS facility through changes to in situ greenhouse gas fluxes and land surface albedo. When quantifying these upstream climate impacts, even at a single site, different methods can give different estimates. Here we show how three common methods for estimating the net ecosystem carbon balance of bioenergy crops established on former grassland or former cropland can differ in their central estimates and uncertainty. We place these net ecosystem carbon balance forcings in the context of associated radiative forcings from changes to soil N₂O and CH₄fluxes, land surface albedo, embedded fossil fuel use, and geologically stored carbon. Results from long term eddy covariance measurements, a soil and plant carbon inventory, and the MEMS 2 process‐based ecosystem model all agree that establishing perennials such as switchgrass or mixed prairie on former cropland resulted in net negative radiative forcing (i.e., global cooling) of −26.5 to −39.6 fW m⁻²over 100 years. Establishing these perennials on former grassland sites had similar climate mitigation impacts of −19.3 to −42.5 fW m⁻². However, the largest climate mitigation came from establishing corn for BECCS on former cropland or grassland, with radiative forcings from −38.4 to −50.5 fW m⁻², due to its higher plant productivity and therefore more geologically stored carbon. Our results highlight the strengths and limitations of each method for quantifying the field scale climate impacts of BECCS and show that utilizing multiple methods can increase confidence in the final radiative forcing estimates. 

The relationship between precipitation and evapotranspiration (ET) is critical to understanding water cycle related dynamics in ecosystems, including crops. Existing studies of bioenergy crops have primarily focused on annual or seasonal ET rates, with less attention given to the immediate ET response following precipitation events. This study examines the variation in ET rates in the days subsequent to precipitation events across various bioenergy crops—corn, switchgrass, and prairies—utilizing 13 years (2010–2022) of growing season data. Meteorological and eddy covariance flux data were collected from seven eddy covariance flux towers as part of the GLBRC scale-up experiment at the Kellogg Biological Station Long Term Ecological Research sites. The analysis revealed that average ET peaked the day after precipitation and declined linearly over the following days, with a statistically significant relationship (p-value = 0.00027, R²= 0.96). Neither the type of biofuel vegetation nor the historical land use significantly influenced ET post-precipitation events (p-values = 0.53 and 0.153, respectively). Key predictors of ET following precipitation events include shortwave radiation, season, day of the year, ambient temperature, vapor pressure deficit (VPD), long-wave radiation, precipitation amount, soil moisture, and annual variability. These findings enhance our comprehension of ET responses in bioenergy crop systems, with implications for water management in sustainable agriculture. 

Abstract As we increasingly understand the impact that land management intensification has on local and global climate, the call for nature-based solutions (NbS) in agroecosystems has expanded. Moreover, the pressing need to determine when and where NbS should be used raises challenges to socioecological data integration as we overcome spatiotemporal resolutions. Natural and working lands is an effort promoting NbS, particularly emissions reduction and carbon stock maintenance in forests. To overcome the spatiotemporal limitation, we integrated life cycle assessments (LCA), an ecological carbon stock model, and a land cover land use change model to synthesize rates of global warming potential (GWP) within a fine-scale geographic area (30 m). We scaled National Agricultural Statistic Survey land management data to National Land Cover Data cropland extents to assess GWP of cropland management over time and among management units (i.e. counties and production systems). We found that cropland extent alone was not indicative of GWP emissions; rather, rates of management intensity, such as energy and fertilizer use, are greater indicators of anthropogenic GWP. We found production processes for fuel and fertilizers contributed 51.93% of GWP, where 33.58% GWP was estimated from N₂O emissions after fertilization, and only 13.31% GWP was due to energy consumption by field equipment. This demonstrates that upstream processes in LCA should be considered in NbS with the relative contribution of fertilization to GWP. Additionally, while land cover change had minimal GWP effect, urbanization will replace croplands and forests where NbS are implemented. Fine-scale landscape variations are essential for NbS to identify, as they accumulate within regional and global estimates. As such, this study demonstrates the capability to harness both LCA and fine-resolution imagery for applications in spatiotemporal and socioecological research towards identifying and monitoring NbS. 

Abstract Radiative forcing (RF) resulting from changes in surface albedo is increasingly recognized as a significant driver of global climate change but has not been adequately estimated, including by Intergovernmental Panel on Climate Change (IPCC) assessment reports, compared with other warming agents. Here, we first present the physical foundation for modeling albedo-induced RF and the consequent global warming impact (GWI_Δα). We then highlight the shortcomings of available current databases and methodologies for calculating GWI_Δαat multiple temporal scales. There is a clear lack of comprehensivein situmeasurements of albedo due to sparse geographic coverage of ground-based stations, whereas estimates from satellites suffer from biases due to the limited frequency of image collection, and estimates from earth system models (ESMs) suffer from very coarse spatial resolution land cover maps and associated albedo values in pre-determined lookup tables. Field measurements of albedo show large differences by ecosystem type and large diurnal and seasonal changes. As indicated from our findings in southwest Michigan, GWI_Δαis substantial, exceeding the RF_Δαvalues of IPCC reports. Inclusion of GWI_Δαto landowners and carbon credit markets for specific management practices are needed in future policies. We further identify four pressing research priorities: developing a comprehensive albedo database, pinpointing accurate reference sites within managed landscapes, refining algorithms for remote sensing of albedo by integrating geostationary and other orbital satellites, and integrating the GWI_Δαcomponent into future ESMs. 

Abstract Surface albedo can affect the energy budget and subsequently cause localized warming or cooling of the climate. When we convert a substantial portion of lands to agriculture, land surface properties are consequently altered, including albedo. Through crop selection and management, one can increase crop albedo to obtain higher levels of localized cooling effects to mitigate global warming. Still, there is little understanding about how distinctive features of a cropping system may be responsible for elevated albedo and consequently for the cooling potential of cultivated lands. To address this pressing issue, we conducted seasonal measurements of surface reflectivity during five growing seasons on annual crops of corn-soybean–winter wheat (Zea mays L.- Glycine max L.Merrill—Triticum aestivum L.; CSW) rotations at three agronomic intensities, a monoculture of perennial switchgrass, and perennial polycultures of early successional and restored prairie grasslands. We found that crop-species, agronomic intensity, seasonality, and plant phenology had significant effects on albedo. The mean ± SD of albedo was highest in perennial crops of switchgrass (Panicum virgatum; 0.179 ± 0.04), intermediate in early successional crops (0.170 ± 0.04), and lowest in a reduced input corn systems with cover crops (0.154 ± 0.02). Thestrongest cooling potentials were found in soybean (−0.450 kg CO₂e m⁻²yr⁻¹) and switchgrass (−0.367 kg CO₂e m⁻²yr⁻¹), with up to −0.265 kg CO₂e m⁻²yr⁻¹of localized climate cooling annually provided by different agroecosystems. We also demonstrated how diverse ecosystems, leaf canopy, and agronomic practices can affect surface reflectivity and provide another potential nature-based solution for reducing global warming at localized scales.