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At least 163 records · Page 9

A protocol for model intercomparison of impacts of marine cloud brightening climate intervention

A modeling protocol (defined by a series of climate model simulations with specified model output) is introduced. Studies using these simulations are designed to improve the understanding of climate impacts using a strategy for climate intervention (CI) known as marine cloud brightening (MCB) in specific regions; therefore, the protocol is called MCB-REG (where REG stands for region). The model simulations are not intended to assess consequences of a realistic MCB deployment intended to achieve specific climate targets but instead to expose responses to interventions in six regions with pervasive cloud systems that are often considered candidates for such a deployment. A calibration step involving simulations with fixed sea surface temperatures (SSTs) is first used to identify a common forcing, and then coupled simulations with forcing in individual regions and combinations of regions are used to examine climate impacts. Synthetic estimates constructed by superposing responses from simulations with forcing in individual regions are considered a means of approximating the climate impacts produced when MCB interventions are introduced in multiple regions. A few results comparing simulations from three modern climate models (CESM2, E3SMv2, and UKESM1) are used to illustrate the similarities and differences between model behavior and the utility of estimates of MCB climate responses that were synthesized by summing responses introduced in individual regions. Cloud responses to aerosol injections differ substantially between models (CESM2 clouds appear much more susceptible to aerosol emissions than the other models), but patterns in precipitation and surface temperature responses were similar when forcing is imposed with similar amplitudes in the same regions. A previously identified La Niña-like response to forcing introduced in the Southeast Pacific is evident in this study, but the amplitude of the response was shown to markedly differ across the three models. Other common response patterns were also found and are discussed. Forcing in the Southeast Atlantic consistently (across all three models) produces weaker global cooling than that in other regions, and the Southeast Pacific and South Pacific show the strongest cooling. This indicates that the efficiency of a given intervention depends on not only the susceptibility of the clouds to aerosol perturbations, but also the strength of the underlying radiative feedbacks and ocean responses operating within each region. These responses were generally robust across models, but more studies and an examination of responses with ensembles would be beneficial.

54 ENVIRONMENTAL SCIENCES↗

Future Projections of EURO‐CORDEX Raw and Bias‐Corrected Daily Maximum Wind Speeds Over Scandinavia

Twenty historical and future Coordinated Regional Climate Downscaling Experiment ensemble for Europe simulations are bias corrected to investigate the future changes in the daily maximum wind speed over Scandinavia. We use quantile mapping to adjust the wind for the historical period (1985–2014) and quantile delta mapping for two future periods (2041–2070, 2071–2100, RCP8.5) with the 3-km spatial resolution NORA3 hindcast as the reference data set. Decomposing the variance, we find that most of the inter-model spread in the bias and the response to climate change is due to the regional climate model over land and mainly to the general climate model over sea. On average, the mean daily maximum wind speed is projected to increase everywhere except along the western coast of Norway and Denmark. In summer, we see an opposite sign over Sweden and Finland. The Norwegian Sea experiences weaker mean and high wind whereas the Baltic Sea experiences a strengthening. Bias correction influences the amplitude of the response, not the response pattern. Wind speed distributions can have different shapes in the future, with, for example, a flattening of the distribution off the coast of Norway with more frequent weak and very strong winds. Apart from a few locations in Norway, we find an increase in the number of days in the local highest historical wind category. Overall, summer exhibits opposite signals compared to the three other seasons. At country scale, the sign of the change in the mean and 98th percentile varies greatly depending on the region and among the simulations, especially for Norway.

54 ENVIRONMENTAL SCIENCES↗

Isolating the effect of biomass burning aerosol emissions on 20th century hydroclimate in South America and Southeast Asia

Abstract Biomass burning is a significant source of aerosol emissions in some regions and has a considerable impact on regional climate. Earth system model simulations indicate that increased biomass burning aerosol emissions contributed to statistically significant decreases in tropical precipitation over the 20th century. In this study, we use the Community Earth System Model version 1 Large Ensemble (CESM1-LENS) experiment to evaluate the mechanisms by which biomass burning aerosol contributed to decreased tropical precipitation, with a focus on South America and Southeast Asia. We analyze the all-but-one forcing simulations in which biomass burning aerosol emissions are held constant while other forcings (e.g., greenhouse gas concentrations) vary throughout the 20th century. This allows us to isolate the influence of biomass burning aerosol on processes that contribute to decreasing precipitation, including cloud microphysics, the radiative effects of absorbing aerosol particles, and alterations in regional circulation. We also show that the 20th century reduction in precipitation identified in the CESM1-LENS historical and biomass burning experiments is consistent across Coupled Model Intercomparison Project Phase 5 models with interactive aerosol schemes and the CESM2 single-forcing experiment. Our results demonstrate that higher concentrations of biomass burning aerosol increases the quantity of cloud condensation nuclei and cloud droplets, limiting cloud droplet size and precipitation formation. Additionally, absorbing aerosols (e.g., black carbon) contribute to a warmer cloud layer, which promotes cloud evaporation, increases atmospheric stability, and alters regional circulation patterns. Corresponding convectively coupled circulation responses, particularly over the tropical Andes, contribute to further reducing the flow of moisture and moisture convergence over tropical land. These results elucidate the processes that affect the water cycle in regions prone to biomass burning and inform our understanding of how future changes in aerosol emissions may impact tropical precipitation over the 21st century.

Environmental Sciences & Ecology↗

Midwest US Croplands Determine Model Divergence in North American Carbon Fluxes

Large uncertainties in North American terrestrial carbon fluxes hinder regional climate projections. Terrestrial biosphere models (TBMs), the essential tools for understanding continental-scale carbon cycle, diverge on whether temperate forests or croplands dominate carbon uptake in North America. Evidence from novel photosynthetic proxies, such as those based on chlorophyll fluorescence, has cast doubt on the “weak cropland, strong forest” carbon uptake patterns simulated by most TBMs. However, no systematic evaluation of TBMs has yet been attempted to pin down space-time patterns that are most consistent with regional CO 2 observational constraints. Here, we leverage atmospheric CO 2 observations and satellite-observed photosynthetic proxies to understand emergent space-time patterns in North American carbon fluxes from a large suite of TBMs and data-driven models. To do so, we evaluate how well the atmospheric signals resulting from carbon flux estimates reproduce the space-time variability in atmospheric CO 2 , as is observed by a network of continuous-monitoring towers over North America. Models with gross or net carbon fluxes that are consistent with the observed CO 2 variability share a salient feature of growing-season carbon uptake in Midwest US croplands. Conversely, the remaining models place most growing-season uptake in boreal or temperate forests. Differences in model explanatory power depend mainly on the simulated annual cycles of cropland uptake—especially, the timing of peak uptake—rather than the distribution of annual mean fluxes across biomes. Our results suggest that improved model representation of cropland phenology is crucial to robust, policy-relevant estimation of North American carbon exchange.

54 ENVIRONMENTAL SCIENCES↗

Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM–MPAS variable-resolution model

Abstract. Comprehensive assessment of climate datasets is important for communicating model projections and associated uncertainties to stakeholders. Uncertainties can arise not only from assumptions and biases within the model but also from external factors such as computational constraint and data processing. To understand sources of uncertainties in global variable-resolution (VR) dynamical downscaling, we produced a regional climate dataset using the Model for Prediction Across Scales (MPAS; dynamical core version 4.0) coupled to the Community Atmosphere Model (CAM; version 5.4), which we refer to as CAM–MPAS hereafter. This document provides technical details of the model configuration, simulations, computational requirements, post-processing, and data archive of the experimental CAM–MPAS downscaling data. The CAM–MPAS model is configured with VR meshes featuring higher resolutions over North America as well as quasi-uniform-resolution meshes across the globe. The dataset includes multiple uniform- (240 and 120 km) and variable-resolution (50–200, 25–100, and 12–46 km) simulations for both the present-day (1990–2010) and future (2080–2100) periods, closely following the protocol of the North American Coordinated Regional Climate Downscaling Experiment. A deviation from the protocol is the pseudo-warming experiment for the future period, using the ocean boundary conditions produced by adding the sea surface temperature and sea-ice changes from the low-resolution version of the Max Planck Institute Earth System Model (MPI-ESM-LR) in the Coupled Model Intercomparison Project Phase 5 to the present-day ocean state from a reanalysis product. Some unique aspects of global VR models are evaluated to provide background knowledge to data users and to explore good practices for modelers who use VR models for regional downscaling. In the coarse-resolution domain, strong resolution sensitivity of the hydrological cycles exists over the tropics but does not appear to affect the midlatitude circulations in the Northern Hemisphere, including the downscaling target of North America. The pseudo-warming experiment leads to similar responses of large-scale circulations to the imposed radiative and boundary forcings in the CAM–MPAS and MPI-ESM-LR models, but their climatological states in the historical period differ over various regions, including North America. Such differences are carried to the future period, suggesting the importance of the base state climatology. Within the refined domain, precipitation statistics improve with higher resolutions, and such statistical inference is verified to be negligibly influenced by horizontal remapping during post-processing. Limited (≈50 % slower) throughput of the current code is found on a recent many-core/wide-vector high-performance computing system, which limits the lengths of the 12–46 km simulations and indirectly affects sampling uncertainty. Our experience shows that global and technical aspects of the VR downscaling framework require further investigations to reduce uncertainties for regional climate projection.

58 GEOSCIENCES↗

Mechanistic Simulations Suggest Riparian Restoration Can Partly Counteract Climate Impacts to Juvenile Salmon

Climate change is reducing summertime water availability and elevating water temperature, placing human consumptive needs in competition with needs of coldwater fishes. Here we worked with natural resource managers in the Snoqualmie River (Washington, USA) to develop riparian management scenarios, and used a process-based modeling system to examine how a threatened population of Chinook salmon (Oncorhynchus tschawytcha) may respond to climate change and whether riparian restoration could reduce climate effects. Linking models of global climate, regional hydrology and water temperature, and fish, we projected that streams would become warmer year-round and drier during summer, further stressing salmon. Climate change accelerated egg emergence, increased juvenile growth and survival, and accelerated outmigration of sub-yearling migrants. Growth was depressed for salmon remaining instream during summer (potential yearling migrants). Riparian restoration counteracted ~10% of summer increases in water temperature, and affected salmon similarly regardless of whether riparian buffers were partially or fully restored, whereas riparian degradation further warmed streams. Riparian restoration fully mitigated climate change effects on potential yearling migrant size, but only minimally affected sub-yearling migrants (assessment metrics changed <2%). Our results will be useful for watershed managers in aligning priorities for fish and humans and our framework can be applied elsewhere.

54 ENVIRONMENTAL SCIENCES↗

A Multi-Instrument Cloud Condensation Nuclei Spectrum Product (Final Technical Report)

A wealth of observational data exists on the characteristics of atmospheric particulate matter, over multiple years, at the DOE ARM Southern Great Plains (SGP) Central Facility site. This site is located in a region of the country that frequently experiences weather extremes, and that is removed from many local sources of pollution but is affected by transported smoke, dust, and urban emissions. The relationships between particulate matter, cloud formation and evolution, and precipitation are therefore of strong interest, and are being explored via modeling on a variety of scales. These models require as input detailed information on the characteristics of particles capable of serving as the nuclei for cloud formation. Sufficient data exist to be able to put together a picture of the nature of the total aerosol and the cloud condensation nuclei (CCN) subset, and their variability, through merged data products. This study was aimed at exploiting the multiple measurement types at SGP to develop the first such multi-year estimates. Further, the resulting data were analyzed to understand temporal patterns ranging from hourly to seasonal, thereby gaining insights into the particle sources affecting the atmosphere in this region. DOE-funded datasets that were analyzed in this study include total particle number concentrations, submicron aerosol scattering coefficients, dry aerosol size distributions, and more recently, time-resolved submicron aerosol chemical composition. Data are also available for the number concentrations of particles that are activated in a cloud condensation nucleus instrument at a series of setpoint supersaturations, providing direct observations of the number concentrations of “CCN”. This variable is the quantity that is generally desired for inclusion in numerical models that seek to represent and predict the impacts of varying aerosol characteristics on the formation and microphysical properties of clouds. One limitation of the use of direct CCN observations is that they are not available for supersaturations larger than about 1%, which is insufficient for deep convection and may be insufficient even for shallow convection, depending on the nature of the available CCN and the dynamics of the cloud. We developed a data-based approach to representing the full aerosol size spectrum with size-dependent hygroscopicity, and used this to extrapolate CCN spectra beyond the limited measurements. Five years of SGP aerosol data (2009 -2013) were analyzed. As a side product of our work, we identified and communicated several previously-unflagged data quality issues. The resulting merged aerosol distributions, along with fits for seasonal averages, were published and submitted to the ARM archive as a special value-added product (VAP; submitted as a PI product). CCN spectra were computed for the same data period and will similarly be published and submitted to the archive for use by the community. We also note that our methodologies and findings have been discussed at several Joint ARM User Facility/Atmospheric System Research (ASR) Principal Investigators Meetings and that recent ARM/ASR aerosol data reporting strategies have included similar ideas for data merging, indicating that this work has had a lasting impact on ARM aerosol data acquisition and reporting. The proposed work advances the science of the interactions of aerosols, clouds and precipitation, with direct application to improve representation of such interactions for clouds in regional and global climate models. The archived data will continue to serve research studies in the future.

54 ENVIRONMENTAL SCIENCES↗

Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS

With recent developments in computational techniques, Data-driven Machine Learning Models (DMLs) have shown great potential in simulating streamflow and capturing the rainfall-runoff relationship in given watersheds, which are traditionally fulfilled by Process-based Hydrologic Models (PHMs). There are debates on whether the DMLs can outperform and possibly replace the classical PHMs for streamflow simulation and river forecasting, but no clear conclusions have been made. This study aims to investigate whether the newer DMLs have any potential in further improving the simulation accuracy of classical PHMs, and vice versa. To do this, we compared a few popular PHMs and DMLs over four watersheds across the Continental US (CONUS) that are associated with different input, climate, and regional conditions. A total of five hydrologic models were chosen, including (1) two classical lumped models, i.e., the Sacramento Soil Moisture Accounting (SAC-SMA) and Xinanjiang (XAJ); (2) one modern distributed model, termed Coupled Routing and Excess Storage (CREST); (3) and two DMLs including an Artificial Neural Networks (ANN) and a deep learning model, termed Long Short Term Memory (LSTM). Our results demonstrated that the DMLs still significantly biased when using the baseline input scenario with the PHMs. However, the DMLs fed with delayed input scenarios had great potential and can reach high simulation accuracy. The DMLs, especially the ANN, outperformed other employed models under the rainfall-runoff relationship in which rainfall dominantly drives. Furthermore, the DMLs also showed better performance in the high-flow regime, while the PHMs had a better performance for the low-flow regime, implying both PHMs and DMLs have their own merits and are worthy of joint development. In general, our study indicated a great potential of using DMLs to simulate streamflow, but further studies are still needed to verify the transferability and scalability of DMLs in large-scale experiments, such as the Distributed Model Intercomparison Projects 1&2 conducted by National Weather Services but to compare modern DMLs and PHMs.

58 GEOSCIENCES↗

Modulation of the Eastern Equatorial Pacific Seasonal Cycle by Tropical Instability Waves

Feedbacks from tropical instability waves (TIWs) on the seasonal cycle of the eastern Pacific Ocean are studied using two eddy-rich ocean simulations, with and without TIWs. By warming the equatorial waters by up to 0.4°C through nonlinear advection in boreal summer and fall, TIWs reduce the amplitude of the seasonal cycle in upper ocean temperatures. In addition, TIWs stabilize the upper part of the Equatorial Undercurrent (EUC) through enhanced barotropic energy conversion, leading to a year-round weakening by -0.15 m s -1 and preventing an unrealistic re-intensification in boreal fall usually found in non-eddy resolving models. A coarser simulation at 1-degree horizontal resolution fails to reproduce the TIW-induced nonlinear warming of equatorial waters, but succeeds in inhibiting the EUC re-intensification. This suggests a threshold effect in TIW strength, associated with the model's ability to simulate eddies, which may be responsible for long-standing biases displayed by global climate models in this region.

58 GEOSCIENCES↗

Burning conditions and transportation pathways determine biomass-burning aerosol properties in the Ascension Island marine boundary layer

Abstract. African biomass-burning aerosol (BBA) in the southeast Atlantic Ocean (SEA) marine boundary layer (MBL) is an important contributor to Earth's radiation budget, yet its representation remains poorly constrained in regional and global climate models. Data from the Layered Atlantic Smoke Interactions with Clouds (LASIC) field campaign on Ascension Island (7.95° S, 14.36° W) provide insight into how burning conditions, fuel type, transport pathways, and atmospheric processing affect the chemical, microphysical, and optical properties of BBA between June and September 2017. A total of 10 individual plume events characterize the seasonal evolution of the BBA properties. Early-season inefficient fires, determined by low refractory black carbon to above-background carbon monoxide mixing ratios (rBC : ΔCO), led to enhanced concentrations of organic- and sulfate-rich aerosols. Mid-season efficient fires, determined by higher rBC : ΔCO values, led to rBC-enriched BBA. A mix of efficient and inefficient fires later in the season resulted in conflicting BBA properties. Prolonged transport (∼ 10 d) through the MBL and lower free troposphere (FT) facilitated chemical and aqueous-phase processing, which led to a reduction in organic aerosol mass concentrations. This resulted in lower organic aerosol (OA) to rBC (OA : rBC) mass ratios (2–5) in the MBL compared to higher values (5–15) in the nearby FT. These atmospheric and cloud oxidation processes yield more light-absorbing BBA and explain the notably low single-scattering albedo at 530 nm (SSA530) values (< 0.80) observed in the MBL. This study establishes a robust correlation between SSA530 and OA : rBC across the MBL and FT, underscoring the dependency of optical properties on chemical composition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

An Improved Zhang's Dynamic Water Balance Model Using Budyko-Based Snow Representation for Better Streamflow Predictions

Understanding the water balance of a catchment in relation to its regional climate forcings and catchment characteristics is critical for predicting current and future water resources amid changing climate and land cover. This study intends to improve Zhang's monthly water balance model (a physics-based conceptual hydrologic model) that reflects the physical partitioning process of the hydrological cycle at the basin level based on regional climate and catchment characteristics. The existing model does not include snow process and has confronted evident limitations in snow-affected areas, which is a critical aspect since snowmelt water has been a significant source of water resources for many regions, especially in the temperate and frigid zones. We introduce a snow module based on surface energy balance and Budyko-limits on melting and combine it with the existing water balance equations. Moreover, monthly parameterization is applied to the model to better explain the time-varying hydrological characteristics of a catchment. The proposed model involves five different monthly parameters, which determine the physical partitioning process of the hydrological cycle, and they are regionally calibrated and validated under Budyko-type constraints. The model is applied to 1,210 basins across the continental United States (CONUS), and the simulated streamflow is compared to the observed data. The proposed model significantly outperformed the original model, improving the median NSE by 31% (from 0.51 to 0.67) and increasing the number of catchments with an acceptable NSE by 58%. The spatial variability of the basin characteristics across the CONUS is also investigated based on the calibrated parameters.

54 ENVIRONMENTAL SCIENCES↗

Land–Atmosphere Coupling at the U.S. Southern Great Plains: A Comparison on Local Convective Regimes between ARM Observations, Reanalysis, and Climate Model Simulations

Using the 9-yr warm-season observations at the Atmospheric Radiation Measurement Southern Great Plains site, in this work we assess the land–atmosphere (LA) coupling in the North American Regional Reanalysis (NARR) and two climate models: hindcasts with the Community Atmosphere Model version 5.1 by Cloud-Associated Parameterizations Testbed (CAM5-CAPT) and nudged runs with the Energy Exascale Earth System Model Atmosphere Model version 1 Regionally Refined Model (EAMv1-RRM). We focus on three local convective regimes and diagnose model behaviors using the local coupling metrics. NARR agrees well with observations except a slightly warmer and drier surface with higher downwelling shortwave radiation and lower evaporative fraction. On clear-sky days, it shows warmer and drier early-morning conditions in both models with significant underestimates in surface evaporation by EAMv1-RRM. On the majority of the ARM-observed shallow cumulus days, there is no or little low-level clouds in either model. When captured in models, the simulated shallow cumulus shows much less cloud fraction and lower cloud bases than observed. On the days with late-afternoon deep convection, models tend to present a stable early-morning lower atmosphere more frequently than the observations, suggesting that the deep convection is triggered more often by elevated instabilities. Generally, CAM5-CAPT can reproduce the local LA coupling processes to some extent due to the constrained early-morning conditions and large-scale winds. EAMv1-RRM exhibits large precipitation deficits and warm and dry biases toward mid-to-late summers, which may be an amplification through a positive LA feedback among initial atmosphere and land states, convection triggering and large-scale circulations.

58 GEOSCIENCES↗

Impact of Orographic Drag Schemes on East Asia Rainfall

Current generation of climate models often has significant biases in mountainous regions where the gradient of elevation is steep, and the terrain is complex. Potential reasons for these biases include under‐representation of orographic drag process in climate models. In this study, we assess the impact of orographic drag on East Asia rainfall by comparing the impact of a new orographic drag scheme that considers 3D orographic anisotropy (3D‐oro) with a 2‐D scheme in a general circulation model. Two sets of simulations (medium‐range and seasonal forecast) are carried out for the comparison and validation against observation. It is shown that through local/remote forcing of the drag in the mountainous regions, the 3D‐oro alleviates part of the excessive rainfall in west Tibetan Plateau and parts of insufficient rainfall in Southeast China by about 25%∼50% in the January/winter forecasts; it alleviates about 25%∼50% of the rainfall bias in part of south Tibetan Plateau and of East Asia in the July/summer forecasts. The results suggest the importance of improved orographic drag process and its impact in climate modeling for those regions that are prone to significant impact of hydroclimate events.

orographic anisotropy↗

Limited potential of irrigation to prevent potato yield losses in Germany under climate change

Climate models project higher temperatures and a substantial net water deficit during the summer months over Germany until the end of the 21st century. The agricultural sector is particularly vulnerable to changing weather and climate conditions. In this study, we examine the role of irrigation for potato yields as a climate change adaptation measure in Northeast Lower Saxony (Germany). The region represents the largest irrigated area in Germany and is one of the main growing areas of potatoes. We develop a statistical multivariate regression model to investigate whether regional climate and irrigation data at the monthly and county level are suitable to describe potato yield variability from 1978 to 2018. Irrigation is estimated based on the climatic water balance and crop water demand. These estimates are calibrated against irrigation data from local agencies to account for realistic monthly water withdrawals. We analyze future yield changes based on climate model projections for the 21st century and different irrigation scenarios. We find that the potato yield will decrease significantly by 18% on average until 2050 with climate projection uncertainty of ±6.4% under the high emission scenario RCP 8.5 and without irrigation. Irrigation at current levels could compensate for about 54% of these yield losses. Doubling the amount of irrigation minimizes yield losses by as much as 80%. Under the low (middle) emission scenario RCP 2.6 (RCP 4.5), potato yield are projected to decrease moderately by 4.5% ± 3.4% (5.4% ± 2.8%) with irrigation at current levels. We show that the yield losses mainly originate from higher temperatures during the summer. Our analysis indicates that the capacity of irrigation to minimize yield losses is limited. Strengthening climate change mitigation efforts may be more effective in preventing yield losses. However, the effect of elevated CO 2 might buffer the high yield losses, especially in RCP 8.5. The studies supports farmers and decision makers in assessing future risks in potato yields. It can guide policymakers in allocating economic resources to cope with climate change.

60 APPLIED LIFE SCIENCES↗

FACETS Dynamical Downscaling Simulations over North America by the CAM-MPAS Variable-Resolution Model

Comprehensive assessment of climate datasets created by statistical or dynamical models is important for effectively communicating model projection and associated uncertainty to stakeholders and decision-makers. The Department of Energy FACETS project aims to foster such communication through development of metrics and their demonstration on a hierarchy of downscaled climate datasets to quantify aspects of climate change projections that are credible, particularly for supporting decisions related to the energy-water-land nexus. As a part of this effort, we have produced a regional climate dataset using the Model for Prediction Across Scales coupled to the Community Atmosphere Model (CAM-MPAS). This global modeling framework is configured with variable-resolution meshes featuring higher resolutions over North America, as well as quasi-uniform resolution meshes across the globe. The variable-resolution configurations allow fine-scale features to be better resolved inside the refinement and interact with global-scale circulations. The dataset includes multiple uniform- (240km and 120km) and variable-resolution (200-50km, 100-25km, and 46-12km) simulations that are designed to be compatible with other regional climate simulations that contribute to the hierarchy of downscaled climate datasets of the project. Furthermore, the dataset consists of simulations for both the present-day (1989-2010) and future (2079-2100) climate and post-processing of the model output has been coordinated across the project for consistency to facilitate common analysis across the hierarchy of datasets. Altogether, this CAM-MPAS model dataset provides a unique opportunity to assess the influence of resolutions and modeling framework on model credibility and climate change projection.

Sakaguchi, Koichi↗

Weak influence of anthropogenic emissions on aerosol, cloud, and rain in the wet season of the Amazon rainforest

Anthropogenic emissions have been shown to affect new particle formation, aerosol concentrations, and clouds. Such effects vary with region, environmental conditions, and cloud types. In the wet season of Amazonia, anthropogenic emissions emitted from Manaus, Brazil, can significantly increase the cloud condensation nuclei (CCN) concentrations compared to the background of mainly natural aerosols. However, the regional response of cloud and rain to anthropogenic emissions in Amazonia remains very uncertain. Here, we aim to quantify how aerosol concentration, cloud, and rain respond to changes in anthropogenic emissions through parameterized new particle formation and primary aerosol emission in the Manaus region and to understand the underlying mechanisms. We ran the atmosphere-only configuration of the HadGEM3 climate model with a nested regional domain that covers most of the rainforest region (720 km by 1200 km with 3 km resolution) under scaled regional emissions. The 7 d simulations show that, in the areas that are affected by anthropogenic emissions, when aerosol and precursor gas emissions are doubled from the baseline emission inventories, aerosol number concentrations increase by 13 %. The nucleation rate that involves sulfuric acid and biogenic compounds generally increases with pollution levels. However, nucleation is suppressed very close to the pollution source, resulting in lower nucleation and soluble Aitken mode aerosol number concentrations. We also found that doubling the anthropogenic emission can increase the cloud droplet number concentrations ( N d ) by 9 %, but cloud water and rain mass mixing ratios do not change significantly. Even very strong reductions in aerosol number concentrations by a factor of 4, which is an unrealistic condition, cause only a 4 % increase in rain over the domain. If we assume our simulation has a fine enough grid resolution and an accurate representation of the relevant atmospheric processes, the simulated weak and non-linear response of cloud and rain properties to linearly scaled anthropogenic emissions suggests that the interactions among aerosol, cloud, and precipitation in the Amazonian convective environment are buffered by microphysical processes. It also implies that the convective environment is resilient to the changes in Nd that occur in response to localized anthropogenic aerosol perturbations.

Wang, Xuemei [University of Leeds (United Kingdom)↗

The Need for Urban‐Resolving Climate Modeling Across Scales

Abstract Increasing urbanization, evolving urban landscapes, and growing populations require an accurate representation of urban areas and urban processes at microscale, regional, and global scales and their feedback across the scales. Important urban processes should be represented in climate models, and current approaches used are discussed. We discuss strategies for urban‐resolving climate research to capture the effect of climate on urban regions and the non‐linear urban processes on climate. Advancing urban‐resolving climate modeling will take us closer to capabilities that can truly capture relevant natural and human components.

Sharma, Ashish↗

Combining artificial intelligence, Earth observations, and climate models to improve predictability of ice-biogeochemistry interactions

Focal Area: Predictive Modeling. We describe how artificial intelligence (AI) can be combined with state-of-the-science Earth system models to better predict future regional climate responses. To demonstrate, we describe a case study in biogeochemical interactions with sea ice. Science Challenge: Biogeochemical models are poorly constrained for high latitude systems. Machine learning methods and edge computing can be combined with Earth system models, such as the Energy Exascale Earth System Model (E3SM), to gain insight into ice-biogeochemical interactions and improve sea ice extent prediction.

54 ENVIRONMENTAL SCIENCES↗