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Megafires are increasing in the Western US due to sustained drought and prolonged fire exclusion. LANL uniquely positioned to lead in both modeling (fine scales, coupled fire-atmosphere, 3D fuels) and observations (CAFÉ).
Abstract. Biomass burning emits vapors and aerosols into the atmosphere that can rapidly evolve as smoke plumes travel downwind and dilute, affecting climate- and health-relevant properties of the smoke. To date, theory has been unable to explain observed variability in smoke evolution. Here, we use observational data from the Biomass Burning Observation Project (BBOP) field campaign and show that initial smoke organic aerosol mass concentrations can help predict changes in smoke aerosol aging markers, number concentration, and number mean diameter between 40–262 nm. Because initial field measurements of plumes are generally >10 min downwind, smaller plumes will have already undergone substantial dilution relative to larger plumes and have lower concentrations of smoke species at these observations closest to the fire. The extent to which dilution has occurred prior to the first observation is not a directly measurable quantity. We show that initial observed plume concentrations can serve as a rough indicator of the extent of dilution prior to the first measurement, which impacts photochemistry, aerosol evaporation, and coagulation. Cores of plumes have higher concentrations than edges. By segregating the observed plumes into cores and edges, we find evidence that particle aging, evaporation, and coagulation occurred before the first measurement. We further find that on the plume edges, the organic aerosol is more oxygenated, while a marker for primary biomass burning aerosol emissions has decreased in relative abundance compared to the plume cores. Finally, we attempt to decouple the roles of the initial concentrations and physical age since emission by performing multivariate linear regression of various aerosol properties (composition, size) on these two factors.
Projecting future climate variables is essential for comprehending the potential impacts on hydroclimatic hazards like floods and droughts. Evaluating these impacts is challenging due to the coarse spatial resolution of global climate models (GCMs); therefore, bias correction is widely used. Here, we applied two statistical methods—standard empirical quantile mapping (EQM) and a hybrid approach, EQM with linear correction (EQM-LIN)—to bias correct precipitation and air temperature simulated by nine GCMs. We used historical observations from 20 weather stations across South Florida to project future climate under three shared socioeconomic pathways (SSPs). Compared to the EQM, the hybrid EQM-LIN method improved R 2 of daily quantiles by up to 30% over the historical period and improved MAE up to 70% in months that contain most extreme values. Projected extreme precipitation at the weather stations showed that, compared to the EQM-LIN, the EQM method underestimates the high quantiles by up to 26% in SSP585. The projected changes in annual maximum precipitation from historical period (1985–2014) to near future (2040–2069) and far future (2070–2100) were between 2% and 16% across the study area. Projected future precipitation suggested a slight decrease during summer but an increase in fall. This, along with rising summer temperatures, suggested that South Florida can experience rapid oscillations from warmer summers and increased flooding in fall under future climate. Additionally, our comparative analyses with globally and nationally downscaled studies showed that such coarse scale studies do not represent the climatic extremes well, particularly for high quantile precipitation.
Abstract The role of time-dependent freezing of ice nucleating particles (INPs) is evaluated with the “Aerosol–Cloud” (AC) model in 1) deep convection observed over Oklahoma during the Midlatitude Continental Convective Cloud Experiment (MC3E), 2) orographic clouds observed over North California during the Atmospheric Radiation Measurement (ARM) Cloud Aerosol Precipitation Experiment (ACAPEX), and 3) supercooled, stratiform clouds over the United Kingdom, observed during the Aerosol Properties, Processes And Influences on the Earth’s climate (APPRAISE) campaign. AC uses the dynamical core of the WRF Model and has hybrid bin–bulk microphysics and a 3D mesoscale domain. AC is validated against coincident aircraft, ground-based, and satellite observations for all three cases. Filtered concentrations of ice (>0.1–0.2 mm) agree with those observed at all sampled levels. AC predicts the INP activity of various types of aerosol particles with an empirical parameterization (EP), which follows a singular approach (no time dependence). Here, the EP is modified to represent time-dependent INP activity by a purely empirical approach, using our published laboratory observations of time-dependent INP activity. In all simulated clouds, the inclusion of time dependence increases the predicted INP activity of mineral dust particles by 0.5–1 order of magnitude. However, there is little impact on the cloud glaciation because the total ice is mostly (80%–90%) from secondary ice production (SIP) at levels warmer than about −36°C. The Hallett–Mossop process and fragmentation in ice–ice collisions together initiate about 70% of the total ice, whereas fragmentation during both raindrop freezing and sublimation contributes <10%. Overall, total ice concentrations and SIP are unaffected by time-dependent INP activity. In the simulated APPRAISE case, the main causes of persistence of long-lived clouds and precipitation are predicted to be SIP in weak embedded convection and reactivation following recirculation of dust particles in supercooled layer cloud.
Tibetan Plateau sensible heating (TPSH) effect is recognized as a key driver of the South Asian climate; however its role in regulating the regional dust burdens over South Asia during the pre-monsoon season remains unclear. Using long-term reanalysis and/or observational dust and TPSH data, we show that the dust optical depth at 550 nm (DOD 550 ) over South Asia is significantly positively correlated with TPSH at the interannual scale. DOD 550 over South Asia increases (decreases) by 20 % (12 %) in the strongest (weakest) TPSH years. The phenomenon of TPSH-dust relationships is reinforced by sensitivity experiments by altering TPSH from global climate models. Further mechanism analysis reveals that TPSH perturbation induces regional circulation anomalies over South Asia, which features a low-tropospheric cyclonic response around the Tibetan Plateau. This cyclonic anomaly strengthens the prevailing northwesterly winds, increasing regional dust emissions and transportation over South Asia. This novel mechanism helps understand the South Asian dust change.
Focal Area(s): Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI-driven model/component/parameterization selection). Science Challenge: Watershed processes, such as the fate and transport of sediment, carbon and nutrients across landscapes and their fluxes to water bodies (e.g., streams, rivers and lakes), have important implications for global and regional carbon and nutrient dynamics, biogeochemical functioning of terrestrial ecosystems, and soil functions. The magnitude of lateral surface/subsurface transport and fluxes of sediment, carbon and nutrients are key factors controlling the vulnerability of watersheds to climate extremes such as droughts, wildfires, and floods. Recent field observations and other scientific evidence suggest that the magnitudes of lateral transport and fluxes of sediment, carbon and nutrients are governed primarily by the spatial and vertical heterogeneity of landscape and soil properties and by pedogenic processes. However, the current generation of land surface and watershed models do not mechanistically couple the terrestrial and hydrologic systems, nor do they represent sufficiently the spatial and vertical heterogeneity of land surface and subsurface properties. On the other hand, increasing complexity of coupled watershed and land surface models requires more data to parameterize, calibrate and validate. Remote sensing (RS) provides a means to acquire spatial data and characterize their heterogeneity at the watershed scale, overcoming a major limitation associated with conventional point measurements. To improve the representation of land-surface and surface/subsurface process coupling and sub-grid heterogeneity in watershed models, it is essential to build our predictive understanding by learning from both the multi-scale multi-process modeling and diverse multi-scale data while leveraging powerful artificial intelligence (AI) techniques.
Abstract More than 40 model groups worldwide are participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), providing a new and rich source of information to better understand past, present, and future climate change. Here, we use the Earth System Model Evaluation Tool (ESMValTool) to assess the performance of the CMIP6 ensemble compared to the previous generations CMIP3 and CMIP5. While CMIP5 models did not capture the observed pause in the increase in global mean surface temperature between 1998 and 2013, the historical CMIP6 simulations agree well with the observed recent temperature increase, but some models have difficulties in reproducing the observed global mean surface temperature record of the second half of the twentieth century. While systematic biases in annual mean surface temperature and precipitation remain in the CMIP6 multimodel mean, individual models and high‐resolution versions of the models show significant reductions in many long‐standing biases. Some improvements are also found in the vertical temperature, water vapor, and zonal wind speed distributions, and root‐mean‐square errors for selected fields are generally smaller with reduced intermodel spread and higher average skill in the correlation patterns relative to observations. An emerging property of the CMIP6 ensemble is a higher effective climate sensitivity with an increased range between 2.3 and 5.6 K. A possible reason for this increase in some models is improvements in cloud representation resulting in stronger shortwave cloud feedbacks than in their predecessor versions.
Smoke from southern Africa blankets the southeastern Atlantic Ocean from June to October, producing strong and competing aerosol radiative effects. Smoke effects on the transition between overcast stratocumulus and scattered cumulus clouds are investigated along a Lagrangian (air-mass-following) trajectory in regional climate and large eddy simulation models. Results are compared with observations from three recent field campaigns that took place in August 2017: ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES), CLouds and Aerosol Radiative Impacts and Forcing: Year 2017 (CLARIFY), and Layered Atlantic Smoke Interactions with Clouds (LASIC). The case study is set up around the joint ORACLES–CLARIFY flight that took place near Ascension Island on 18 August 2017. Smoke sampled upstream on an ORACLES flight on 15 August 2017 likely entrained into the marine boundary layer later sampled during the joint flight. The case is first simulated with the WRF-CAM5 regional climate model in three distinct setups: (1) FireOn, in which smoke emissions and any resulting smoke–cloud–radiation interactions are included; (2) FireOff, in which no smoke emissions are included; (3) RadOff, in which smoke emissions and their microphysical effects are included but aerosol does not interact directly with radiation. Over the course of the Lagrangian trajectory, differences in free tropospheric thermodynamic properties between FireOn and FireOff are nearly identical to those between FireOn and RadOff, showing that aerosol–radiation interactions are primarily responsible for the free tropospheric effects. These effects are non-intuitive: in addition to the expected heating within the core of the smoke plume, there is also a “banding” effect of cooler temperature (~1–2 K) and greatly enhanced moisture (>2 g kg -1 ) at the plume top. This banding effect is caused by a vertical displacement of the former continental boundary layer in the free troposphere in the FireOn simulation resulting from anomalous diabatic heating due to smoke absorption of sunlight that manifests primarily as a few hundred meters per day reduction in large-scale subsidence over the ocean. A large eddy simulation (LES) is then forced with free tropospheric fields taken from the outputs for the WRF-CAM5 FireOn and FireOff runs. Cases are run by selectively perturbing one variable (e.g., aerosol number concentration, temperature, moisture, vertical velocity) at a time to better understand the contributions from different indirect (microphysical), “large-scale” semi-direct (above-cloud thermodynamic and subsidence changes), and “local” semi-direct (below-cloud smoke absorption) effects. Despite a more than 5-fold increase in cloud droplet number concentration when including smoke aerosol concentrations, minimal differences in cloud fraction evolution are simulated by the LES when comparing the base case with a perturbed aerosol case with identical thermodynamic and dynamic forcings. A factor of 2 decrease in background free tropospheric aerosol concentrations from the FireOff simulation shifts the cloud evolution from a classical entrainment-driven “deepening–warming” transition to trade cumulus to a precipitation-driven “drizzle-depletion” transition to open cells, however. The thermodynamic and dynamic changes caused by the WRF-simulated large-scale adjustments to smoke diabatic heating strongly influence cloud evolution in terms of both the rate of deepening (especially for changes in the inversion temperature jump and in subsidence) and in cloud fraction on the final day of the simulation (especially for the moisture “banding” effect). Such large-scale semi-direct effects would not have been possible to simulate using a small-domain LES model alone.
Soil organic carbon (SOC) comprises particulate (POC) and mineral-associated organic carbon (MAOC), which differ in formation, stabilization, and loss mechanisms. While the current global distribution of POC and MAOC is characterized, their vulnerability under future climate scenarios remains unclear. Using 3284 topsoil (0-30 cm) observations from six continents, we identify high-latitude soils as global hotspots of SOC vulnerability under shared socioeconomic pathway scenarios (SSP126, SSP245, and SSP585). Under a high-emission scenario (SSP585), high-latitude soils are projected to lose substantial POC by 2100, accounting for about 81 ± 10% of total SOC losses. These declines are driven by the high proportion of SOC stored as POC (f POC ) and its high temperature sensitivity. We show that f POC is a robust indicator of SOC vulnerability to climate change. Globally, the projected POC decline corresponds to a cumulative carbon dioxide (CO 2 ) release of 81.34 Pg CO 2 -equivalent by 2100, highlighting the importance of preserving POC to mitigate climate feedbacks.
Abstract Climate change is projected to worsen ozone pollution over many populated regions, with larger impacts at higher concentrations. More intense and frequent ozone episodes risk setbacks to human health and environmental policy achievements. However, assessing these changes is complicated by uncertain climate sensitivity, closely related to climate model response, and internal variability in simulations projecting climate's influence on air quality. Here, leveraging a global modeling framework that one‐way couples a human activity model, an Earth system model of intermediate complexity, and an atmospheric chemistry model, we investigate the role of climate sensitivity in climate‐induced changes to high ozone pollution episodes in the United States using multiple greenhouse gas emissions scenarios, representations of climate sensitivity, and initial condition members. We bias correct and evaluate historical model simulations, identifying modeled and observed O 3 episodes using extreme value theory, and extend the approach to projections of mid‐ and end‐century climate impacts. Results show that the influence of climate sensitivity can be as significant as that of greenhouse gas emissions scenario absent precursor emissions changes. Climate change is projected to increase the magnitude of the highest annually occurring O 3 concentrations by over 2.3 ppb on average across the U.S. at mid‐century under a high climate sensitivity and moderate emissions scenario, but the increase is limited to less than 0.3 ppb under lower climate sensitivity. Further, we show that areas in the U.S. currently meeting air quality standards risk being pushed into non‐compliance due to a climate‐induced increase in frequency of high ozone days.
Abstract. Estimating past aerosol radiative effects and their uncertainties is an important topic in climate science. Aerosol radiative effects propagate into large uncertainties in estimates of how present and future climate evolves with changing greenhouse gas emissions. A deeper understanding of how aerosols affected the atmospheric energy budget under past climates is hindered in part by a lack of relevant paleo-observations and in part because less attention has been paid to the problem. Because of the lack of information we do not seek here to determine the change in the radiative forcing due to aerosol changes but rather to estimate the uncertainties in those changes. Here we argue that current uncertainties from emission uncertainties (90 % confidence interval range spanning 2.8 W m−2) are just as large as model spread uncertainties (2.8 W m−2) in calculating preindustrial to present-day aerosol radiative effects. There are no estimates of radiative forcing for important aerosols such as wildfire and dust aerosols in most paleoclimate time periods. However, qualitative analysis of paleoclimate proxies suggests that changes in aerosols between different past climates are similar in magnitude to changes in aerosols between the preindustrial and present day; plus, there is the added uncertainty from the variability in aerosols and fires in the preindustrial. From the limited literature we crudely estimate a paleoclimate aerosol uncertainty for the Last Glacial Maximum relative to preindustrial of 4.8 W m−2, and we estimate the uncertainty in the aerosol feedback in the natural Earth system over the paleoclimate (Last Glacial Maximum to preindustrial) to be about 3.2 W m−2 K−1. In order to more accurately assess the uncertainty in historical aerosol radiative effects, we propose a new model intercomparison project, which would include multiple plausible emission scenarios tested across a range of state-of-the-art climate models over the historical period. These emission scenarios would then be compared to the available independent aerosol observations to constrain which are most probable. In addition, future efforts should work to characterize and constrain paleo-aerosol forcings and uncertainties. Careful propagation of aerosol uncertainties in the literature is required to ensure an accurate quantification of uncertainties in projections of future climate changes.
Abstract The basic dynamics of the spatiotemporal diversity for El Niño–Southern Oscillation (ENSO) has been the subject of extensive research and, while several hypotheses have been proposed, remains elusive. One promising line of studies suggests that the observed eastern Pacific (EP) and central Pacific (CP) ENSO may originate from two coexisting leading ENSO modes. We show that the coexistence of unstable EP-like and CP-like modes in these studies arises from contaminated linear stability analysis due to unnoticed numerical scheme caveats. In this two-part study, we further investigate the dynamics of ENSO diversity within a Cane–Zebiak-type model. We first revisit the linear stability issue to demonstrate that only one ENSO-like linear leading mode exists under realistic climate conditions. This single leading ENSO mode can be linked to either a coupled recharge-oscillator (RO) mode favored by the thermocline feedback or a wave-oscillator (WO) mode favored by the zonal advective feedback at the weak air–sea coupling end. Strong competition between the RO and WO modes for their prominence in shaping this ENSO mode into a generalized RO mode makes it sensitive to moderate changes in these two key feedbacks. Modulations of climate conditions yield corresponding modulations in spatial pattern, amplitude, and period associated with this ENSO mode. However, the ENSO behavior undergoing this linear climate condition modulations alone does not seem consistent with the observed ENSO diversity, suggesting the inadequacy of linear dynamics in explaining ENSO diversity. A nonlinear mechanism for ENSO diversity will be proposed and discussed in Part II.
Periods of water surplus and deficit in Northern California follow a pronounced quasi-decadal cycle. This cycle is largely driven by the frequency of atmospheric rivers (ARs), affecting the region’s wet and dry periods. Our analyses demonstrate that the quasi-decadal cycle of AR frequency relies on moisture transport associated with the position and intensity of the Aleutian Low. In observations, the Aleutian Low is shown to covary with tropical Pacific sea surface temperature anomalies. A modeling experiment, which incorporates ocean observations from the equatorial Pacific into the fully coupled climate model, provides support that the quasi-decadal cycle of the Aleutian Low is forced by the tropical Pacific. Subsequently, the tropical Pacific modulates the wet season moisture transport toward California on decadal time scales, affecting AR frequency. These results provide metrics for improving interannual-to-decadal prediction of AR activity, which drives hydrological cycles in Northern California.
Studies of land-atmosphere interactions under a clear sky and low cumulus cloud conditions are common from long-term observatories like at the southern great plains. How well the relationships and responses of surface radiative and turbulent heat fluxes determined from these investigations hold for more heterogeneous surfaces in other climate regimes, however, is uncertain. In this study, detailed observations of the surface energy budget and daytime boundary layer properties are analyzed using measurements from the Chequamegon Heterogenous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, July-October 2019, across a heterogeneous forested landscape of northern Wisconsin. A cloud regime framework is employed to classify consecutive periods of clear skies from lower atmosphere stratiform and cumulus clouds. A seasonal transition from low cumulus to low stratiform periods occurred, together with a diurnal pattern in cloudy or clear sky period dominance. Radiative forcing was highly dependent on sky conditions, leading to changes in the redistribution efficiency of radiative energy by the surface turbulent heat fluxes. During CHEESEHEAD19, small Bowen ratios dominated with daytime latent heat fluxes three times as large as sensible heat fluxes for all sky conditions studied; the forested region, therefore, falls within an energy-limited regime. The depth of the daytime mixed layer depended upon the sky condition and thermodynamic setting; deeper mixed layers occurred during periods of low cumulus and not clear skies. Profiles of vertical velocity were found to have enhanced variance under low cumulus compared to clear sky periods, suggesting potential for cloud feedbacks on boundary layer structure and surface energy fluxes.
The variability of surface air temperature is of great importance for both society and the environment, is impacted by global warming and local-scale changes. In the arid eastern part of Washington state of USA, substantial urbanization has transformed the Tri-cities into the state’s third-largest urban cluster. This study utilizes a combination of in situ observations and reanalysis datasets to investigate the influence of land use changes on the region’s 2-meter temperature, revealing local effects that compensate for the background global warming. Within the urban fabric, distinct microclimates have emerged due to varying land use, establishing unique relationships between greenness and temperature alterations caused by land use transitions. Notably, our findings demonstrate that the observed compensating signal in the temperature of farmland locations is primarily attributed to local vegetation increases. Through these observations, this research highlights the urban impacts on local climate, offering valuable insights into the complexities surrounding land use and its consequences on the environment.
This paper presents a preliminary reliability analysis of field-aged glass/glass (G/G) PV modules. Several characterization tests were performed on more than 60 modules with two different encapsulant types (EVA and ionomer) exposed in hot-dry Arizona (AZ) climate over 10-35 years. The common degradation mode observed in both encapsulant types was the delamination of encapsulant along with encapsulant browning (EVA only), glass cracking (EVA only), and metallic corrosion over junction box area (ionomer only). The EVA encapsulated G/G modules showed the higher median power degradation rate at 0.91-1.99%/yr whereas modules with ionomer showed the lower at 0.21%/yr.
Here, land surface phenology (LSP), the characterization of plant phenology with satellite data, is essential for understanding the effects of climate change on ecosystem functions. Considerable LSP variation is observed within local landscapes, and the role of biotic factors in regulating such variation remains underexplored. In this study, we selected four National Ecological Observatory Network terrestrial sites with minor topographic relief to investigate how biotic factors regulate intra-site LSP variability. We utilized plant functional type (PFT) maps, functional traits, and LSP data to assess the explanatory power of biotic factors for the start and end of season (SOS and EOS) variability. Our results indicate that PFTs alone explain only 0.8–23.4% of intra-site SOS and EOS variation, whereas including functional traits significantly improves explanatory power, with cross-validation correlations ranging from 0.50 to 0.85. While functional traits exhibited diverse effects on SOS and EOS across different sites, traits related to competitive ability and productivity were important for explaining both SOS and EOS variation at these sites. These findings reveal that plants exhibit diverse phenological responses to comparable environmental conditions, and functional traits significantly contribute to intra-site LSP variability, highlighting the importance of intrinsic biotic properties in regulating plant phenology.
The estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth’s radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies identify the crucial role of clouds in modulating the temporal and spatial variability of DLR. In this study, a new machine learning algorithm that uses multivariate adaptive regression splines (MARS) and the combination of near-surface meteorological data with satellite cloud information is proposed. The new algorithm is compared with the current operational formulation used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF). Both algorithms use near-surface temperature and dewpoint temperature along with total column water vapor from the latest European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis ERA5 and satellite cloud information from the Meteosat Second Generation. The algorithms are trained and validated using both ECMWF-ERA5 and DLR acquired from 23 ground stations as part of the Baseline Surface Radiation Network (BSRN) and the Atmospheric Radiation Measurement (ARM) user facility. Results show that the MARS algorithm generally improves DLR estimation in comparison with other model estimates, particularly when trained with observations. When considering all the validation data, root mean square errors (RMSEs) of 18.76, 23.55, and 22.08 W·m –2 are obtained for MARS, operational LSA-SAF, and ERA5, respectively. The added value of using the satellite cloud information is accessed by comparing with estimates driven by ERA5 total cloud cover, showing an increase of 17% of the RMSE. The consistency of MARS estimate is also tested against an independent dataset of 52 ground stations (from FLUXNET2015), further supporting the good performance of the proposed model.