Uncertainty In Climate Projections Research Articles

Contributions From Cloud Morphological Changes to the Interannual Shortwave Cloud Feedback Based on MODIS and ISCCP Satellite Observations

AbstractThe surface temperature‐mediated change in cloud properties, referred to as the cloud feedback, continues to dominate the uncertainty in climate projections. A larger number of contemporary global climate models (GCMs) project a higher degree of warming than the previous generation of GCMs. This greater projected warming has been attributed to a less negative cloud feedback in the Southern Ocean. Here, we apply a novel “double decomposition method” that employs the “cloud radiative kernel” and “cloud regime” concepts, to two data sets of satellite observations to decompose the interannual cloud feedback into contributions arising from changes within and shifts between cloud morphologies. Our results show that contributions from the latter to the cloud feedback are large for certain regimes. We then focus on interpreting how both changes within and between cloud morphologies impact the shortwave cloud optical depth feedback over the Southern Ocean in light of additional observations. Results from the former cloud morphological changes reveal the importance of the wind response to warming increases low‐ and mid‐level cloud optical thickness in the same region. Results from the latter cloud morphological changes reveal that a general shift from thick storm‐track clouds to thinner oceanic low‐level clouds contributes to a positive feedback over the Southern Ocean that is offset by shifts from thinner broken clouds to thicker mid‐ and low‐level clouds. Our novel analysis can be applied to evaluate GCMs and potentially diagnose shortcomings pertaining to their physical parameterizations of particular cloud morphologies.

Unexpected sources of uncertainty in projecting habitat shifts for Arctic shorebirds under climate change

AbstractAimThe rapidly changing Arctic is ideal for investigating uncertainties in climate projections. Despite the challenges of collecting data in this region, an unprecedented large‐scale survey of shorebirds has been conducted over the last 30 years. Our study aimed to (1) develop probabilistic estimates for the change in suitable habitat for 10 Arctic shorebird species in Canada by 2075 and (2) assess the contribution of modelling decisions to the uncertainty in these estimates.LocationArctic Canada.MethodsTo evaluate uncertainty, we considered six classes of modelling decisions, yielding 216 unique projections for each species. We tested three decisions that are less commonly explored − the pool of candidate variables, a method for selecting variables, and the maximum distance of tree line dispersal, as well as the modelling algorithm, carbon emissions scenario, and global circulation model. We used a bootstrapping approach, creating a probability distribution for the proportional change in suitable habitat for each species.ResultsOur findings indicated a substantial risk for 8/10 species to lose over half of their suitable breeding habitat, but this projection is much less certain than has been described previously. While much uncertainty is unexplained, we were surprised that the largest source of uncertainty among our modelling decisions was from our choice of methods for variable selection, that the other modelling decisions were relatively small sources of uncertainty, overshadowing other modelling decisions.Main ConclusionsWhile most scenarios predict a northward shift and significant habitat loss for Arctic‐breeding shorebirds, the Arctic Archipelago of Canada will remain an important refuge because in many other Arctic regions, there is no land farther north for these species to shift into. A comprehensive understanding of uncertainty is important for deciding if future projections can or should be used when planning climate‐resilient protected area networks.

Future changes in the influence of the NAO on Mediterranean winter precipitation extremes in the EC-Earth3 large Ensemble: The prominent role of internal variability

One of the largest uncertainties in future climate projections is the interplay between internally generated and externally forced changes. This study investigates the changes in the link between the North Atlantic Oscillation (NAO) and Mediterranean winter extreme rainfall and dry days by the end of the 21st century compared to present day. We compare two different future pathways and estimate the extent to which the NAO imprint is affected by the global warming level using the latest EC-Earth3 large ensemble historical and future experiments. It is shown that the expected range of winter extremes changes due to internal and unpredictable fluctuations of the NAO largely overcomes the signal associated with externally-forced NAO variations. The NAO is found to exert a similar control on European climate variability, regardless of the amount of warming. For most of the Mediterranean region, magnitude and even sign of projected changes in the NAO-congruent precipitation indices vary substantially across the individual ensemble members according to the corresponding evolution of the NAO. Internal variability provides an average basin-wide contribution of up to 90% or more to the total NAO-driven variability in SSP1–1.9, and of about 80% in SSP5–8.5. Sub-regionally, the anthropogenic component of the NAO link is more evident over the Iberian Peninsula and parts of the central Mediterranean. This emphasises the role of internal variability and related uncertainty in determining the future impact of the NAO via the large spread in the circulation responses. However, the NAO is found to exert a weaker influence on the extreme precipitation total variability in both future scenarios given their future marked increase in total intensity and variance as opposed to the negligible NAO-related trends. Opposite conclusions are drawn for dry days, which are projected to decrease in the future, especially in the northern Mediterranean. Thus, this study also highlights how the variability of future extreme precipitation intensity in the Mediterranean basin will be less dependent on the principal mode of internal climate variability, posing further challenges for prediction and adaptation to weather-related hazards.

Process Modeling of Aerosol‐Cloud Interaction in Summertime Precipitating Shallow Cumulus Over the Western North Atlantic

AbstractProcess modeling of Aerosol‐cloud interaction (ACI) is essential to bridging gaps between observational analysis and climate modeling of aerosol effects in the Earth system and eventually reducing climate projection uncertainties. In this study, we examine ACI in summertime precipitating shallow cumuli observed during the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE). Aerosols and precipitating shallow cumuli were extensively observed with in‐situ and remote‐sensing instruments during two research flight cases on 02 June and 07 June, respectively, during the ACTIVATE summer 2021 deployment phase. We perform observational analysis and large‐eddy simulation (LES) of aerosol effect on precipitating cumulus in these two cases. Given the measured aerosol size distributions and meteorological conditions, LES is able to reproduce the observed cloud properties by aircraft such as liquid water content (LWC), cloud droplet number concentration (Nc) and effective radius reff. However, it produces smaller liquid water path (LWP) and larger Nc compared to the satellite retrievals. Both 02 and 07 June cases are over warm waters of the Gulf Stream and have a cloud top height over 3 km, but the 07 June case is more polluted and has larger LWC. We find that the Na‐induced LWP adjustment is dominated by precipitation feedback for the 2 June precipitating case and there is no clear entrainment feedback in both cases. An increase of cloud fraction due to a decrease of aerosol number concentration is also shown in the simulations for the 02 June case.

Evaluation of the climate models used by the IPCC based on the position of the ITCZ in the Equatorial South Atlantic

AbstractA number of the uncertainties in climate projections for the end of the century are due to the difficulty of general circulation models (GCMs) to represent large‐scale precipitating systems such as the Intertropical Convergence Zone (ITCZ) in the historical period. In this study, we evaluate the Coupled Model Intercomparison Project Phase 5 and 6 models based on the position of the ITCZ in the South Atlantic during the period in which it is climatologically further south (February–May). This period characterizes the rainy season of the Brazilian semiarid. The models were ranked according to two objective methods and validated through a qualitative analysis. The first method considered a success rate to evaluate the correct position of the areas with maximum precipitation and minimum pressure and meridional wind as representative of the mean position of the ITCZ. Among the 15 coupled GCMs analysed for each CMIP, those that best represented the position of the ITCZ were HADGEM.2‐AO, MIROC5 and CANESM2 from CMIP5 and CESM2 and MIROC6 from CMIP6. These models represented well the following characteristics of the ITCZ in February–May: maximum precipitation near the equator at the height of the coast of South America and with the wind convergence zone sloping north towards the African continent. The most common problem noticed in the other GCMs was to displace the ITCZ to the south of its observed mean position within the period February–May. Also, some GCMs presented a double ITCZ (GISS‐E2‐H, GDFL‐CM3 and IPSL‐CM5A‐LR from CMIP5 and INM‐CM5‐0 from CMIP6) following two high TSM cores. One GCM (ACCESS1‐3 from CMIP5) presented a relatively good performance, but the zone of greater precipitation was shifted to the east, being located in the centre of the Atlantic basin. Knowing how the GCMs represent the ITCZ is a criterion that must be considered when choosing the GCMs for climate change studies.

Sea-surface temperature pattern effects have slowed global warming and biased warming-based constraints on climate sensitivity

The observed rate of global warming since the 1970s has been proposed as a strong constraint on equilibrium climate sensitivity (ECS) and transient climate response (TCR)-key metrics of the global climate response to greenhouse-gas forcing. Using CMIP5/6 models, we show that the inter-model relationship between warming and these climate sensitivity metrics (the basis for the constraint) arises from a similarity in transient and equilibrium warming patterns within the models, producing an effective climate sensitivity (EffCS) governing recent warming that is comparable to the value of ECS governing long-term warming under CO[Formula: see text] forcing. However, CMIP5/6 historical simulations do not reproduce observed warming patterns. When driven by observed patterns, even high ECS models produce low EffCS values consistent with the observed global warming rate. The inability of CMIP5/6 models to reproduce observed warming patterns thus results in a bias in the modeled relationship between recent global warming and climate sensitivity. Correcting for this bias means that observed warming is consistent with wide ranges of ECS and TCR extending to higher values than previously recognized. These findings are corroborated by energy balance model simulations and coupled model (CESM1-CAM5) simulations that better replicate observed patterns via tropospheric wind nudging or Antarctic meltwater fluxes. Because CMIP5/6 models fail to simulate observed warming patterns, proposed warming-based constraints on ECS, TCR, and projected global warming are biased low. The results reinforce recent findings that the unique pattern of observed warming has slowed global-mean warming over recent decades and that how the pattern will evolve in the future represents a major source of uncertainty in climate projections.

Amplified Extreme Floods and Shifting Flood Mechanisms in the Delaware River Basin in Future Climates

AbstractHistorical records in the Delaware River Basin reveal complex and spatially diverse flood generating mechanisms influenced by the region's mountains‐to‐plains gradients. This study focuses on predicting future flood hazards and understanding the underlying drivers of changes across the region. Using a process‐based hydrological model, we analyzed the hydrometeorological condition of each historical and future flood event. For each event, at the subbasin scale, we identified the dominant flood generating mechanism, including snowmelt, rain‐on‐snow, short‐duration rain, and long‐duration rain. The rain‐induced floods are further categorized based on the soil's Antecedent Moisture Condition (AMC) before the event, whether dry, normal, or wet. Our historical analysis suggests that rain‐on‐snow is the primary flood mechanism of the Upper Basin. Although most frequent, the magnitude of rain‐on‐snow floods is often less severe than short rain floods. In contrast, historical floods in the Lower Basin are primarily caused by short rain under normal AMC. Given the uncertainties in climate projections, we used an ensemble of future climate scenarios for flood projections. Despite variations in regional climate projections, coherent perspectives emerge: the region will shift toward a warmer, wetter climate, with a projected intensification of extreme floods. The Upper Basin is projected to experience a marked decrease in rain‐on‐snow floods, but a substantial increase in short rain floods with wet AMC. The largest increase in flood magnitude will be driven by short rains with wet AMC in the Upper Basin and by short rains with normal AMC in the Lower Basin.

Biodiversity losses associated with global warming of 1.5 to 4 °C above pre-industrial levels in six countries

We quantify the projected impacts of alternative levels of global warming upon the climatically determined geographic ranges of plants and vertebrates in six countries (China, Brazil, Egypt, Ethiopia, Ghana and India), accounting for uncertainties in regional climate projection. We quantify in a spatially explicit fashion the species richness remaining or lost, allowing the identification of climate refugia which we define as areas where > 75% of the species currently present remain in a world with a particular level of global warming above pre-industrial levels. In all countries and in both taxa, species richness declines with warming, as does the proportion of each country remaining a climate refugium for plants or vertebrates. In percentage terms, refugia loss relative to a 1961–1990 baseline period is greatest in India and Brazil, and least in Ghana and Ethiopia for the same level of warming, and is greater for plants than for vertebrates. Taking account of present land uses (i.e. area still considered natural), and using species richness of plants as a proxy to indicate biodiversity more generally, the proportion of land acting as climate refugia for biodiversity in five of the countries variously declines from 32–75% of a country in the 1961–1990 baseline period to 20–64% for 1.5 °C global warming, 11–53% for 2 °C, 3–33% for 3 °C and 2–24% for 4 °C warming. In Ethiopia, India, Brazil and China, climate refugia decline rapidly with warming while in Ghana and China some refugia persist even with 3–4 °C of warming. Only small percentages of Brazil, India and China are both climate refugia and lie within protected areas; hence, an expansion of the protected area networks in these countries would be required to deliver climate resilient biodiversity conservation. These percentages are larger in Ethiopia and Ghana and, in some areas of Ghana, the only remaining refugia are in protected areas, the remaining landscape converted to other uses.

Climate change impact on sub-tropical lakes – Lake Kinneret as a case study

Climate change is anticipated to alter lake ecosystems by affecting water quality, potentially resulting in loss of ecosystem services. Subtropical lakes have high temperatures to begin with and are expected to exhibit higher temperatures all year round which might affect the thermal structure and ecological processes in a different manner than lakes in temperate zones.In this study the ecosystem response of the sub-tropical Lake Kinneret to climate change was explored using lake ecosystem models. Projection reliability was increased by using a weather generator and ensemble modelling, confronting uncertainty of both climate projections and lake models. The study included running two 1D hydrodynamic-biogeochemical models over one thousand realizations of two gradual temperature increase scenarios that span over 49 years.Our predictions show that an increase in air temperature would have subtle effects on stratification properties but may result in considerable changes to biogeochemical processes. Water temperature rise would cause a reduction in dissolved oxygen. Both of these changes would produce elevated phosphate and lowered ammonium concentrations. In turn, these changes are predicted to modify the phytoplankton community, expressed chiefly in increased cyanobacteria blooms at the expense of green phytoplankton and dinoflagellates; these changes may culminate in overall reduction of primary production. Identification of these trends would not be possible without the use of many realizations of climate scenarios. The use of ensemble modelling increased prediction reliability and highlighted elements of uncertainty. Though we use Lake Kinneret, the patterns identified most likely indicate processes that are expected in sub-tropical lakes in general.

A New Framework for Estimating and Decomposing the Uncertainty of Climate Projections

Abstract Climate projections obtained by running global climate models (GCMs) are subject to multisource uncertainties. The existing framework based on analysis of variance (ANOVA) for decomposing such uncertainties is unable to include the interaction effect between GCM and internal climate variability, which ranks only second to the main effect of GCM in significance. In this study, a three-way ANOVA framework is presented, and all main effects and interaction effects are investigated. The results show that, although the overall uncertainty (O) is mainly contributed by main effects, interaction effects are considerable. Specifically, in the twenty-first century, the global mean (calculated at the grid-cell level and then averaged, and likewise below) relative contributions of all main effects are 54% for precipitation and 82% for temperature; those of all interaction effects are, respectively, 46% and 18%. As the three-way ANOVA cannot investigate the uncertainty components resulting from uncertainty sources, it is improved by deducing the relationship between uncertainty components resulting from uncertainty sources and those resulting from the main effects and interaction effects. By the improved three-way ANOVA, O is decomposed into uncertainty components resulting from the emission scenario (S), GCM (M), and internal climate variability (V). The results reveal that O is mainly contributed by M in the twenty-first century for precipitation, and by M before the 2060s whereas by S thereafter for temperature. The robustness of the V characterization is explored by investigating the variation of V on the number of included ensemble members. The extent of the underestimation of the V contribution is roughly an average of 4% for precipitation and 1% for temperature.

Systematic Underestimation of Canopy Conductance Sensitivity to Drought by Earth System Models

AbstractThe response of vegetation canopy conductance (gc) to changes in moisture availability () is a major source of uncertainty in climate projections. While vegetation typically reduces stomatal conductance during drought, accurately modeling how and to what degree stomata respond to changes in moisture availability at global scales is particularly challenging, because no global scale gc observations exist. Here, we leverage a collection of satellite, reanalysis and station‐based near‐surface air and surface temperature estimates, which are physically and statistically linked to due to the local cooling effect of gc through transpiration, to develop a novel emergent constraint of in an ensemble of Earth System Models (ESMs). We find that ESMs systematically underestimate by ∼33%, particularly in grasslands, croplands, and savannas in semi‐arid and bordering regions of the Central United States, Central Europe, Southeastern South America, Southern Africa, Eastern Australia, and parts of East Asia. We show that this underestimation occurs because ESMs inadequately reduce gc when soil moisture decreases. As gc controls carbon, water and energy fluxes, the misrepresentation of modeled contributes to biases in ESM projections of gross primary production, transpiration, and temperature during droughts. Our results suggest that the severity and duration of droughts may be misrepresented in ESMs due to the impact of sustained gc on both soil moisture dynamics and the biosphere‐atmosphere feedbacks that affect local temperatures and regional weather patterns.

Minimizing uncertainties in climate projections and water budget reveals the vulnerability of freshwater to climate change

Minimizing uncertainties in climate projections and water budget reveals the vulnerability of freshwater to climate change

Synergistic Retrievals of Ice in High Clouds from Elastic Backscatter Lidar, Ku-Band Radar, and Submillimeter Wave Radiometer Observations

Abstract In this study, we investigate the synergy of elastic backscatter lidar, Ku-band radar, and submillimeter-wave radiometer measurements in the retrieval of ice from satellite observations. The synergy is analyzed through the generation of a large dataset of ice water content (IWC) profiles and simulated lidar, radar and radiometer observations. The characteristics of the instruments (frequencies, sensitivities, etc.) are set based on the expected characteristics of instruments of the Atmosphere Observing System (AOS) mission. A hold-out validation methodology is used to assess the accuracy of the IWC profiles retrieved from various combinations of observations from the three instruments. Specifically, the IWC and associated observations are randomly divided into two datasets, one for training and the other for evaluation. The training dataset is used to train the retrieval algorithm, while the evaluation dataset is used to assess the retrieval performance. The dataset of IWC profiles is derived from CloudSat reflectivity and CALIOP lidar observations. The retrieval of the ice water content IWC profiles from the computed observations is achieved in two steps. In the first step, a class, of 18 potential classes characterized by different vertical distribution of IWC, is estimated from the observations. The 18 classes are predetermined based on the k-means clustering algorithm. In the second step, the IWC profile is estimated using an ensemble Kalman smoother algorithm that uses the estimated class as a priori information. The results of the study show that the synergy of lidar, radar, and radiometer observations is significant in the retrieval of the IWC profiles. Nevertheless, it should be mentioned that this synergy was found under idealized conditions, and additional work might be required to materialize it in practice. The inclusion of the lidar backscatter observations in the retrieval process has a larger impact on the retrieval performance than the inclusion of the radar observations. As ice clouds have a significant impact on atmospheric radiative processes, this work is relevant to ongoing efforts to reduce uncertainties in climate analyses and projections.

The Persian Gulf and Oman Sea: Climate variability and trends inferred from satellite observations

The warming trend in the Indian Ocean since the mid-20th century, with a strong signature over the Persian Gulf and Oman Sea, has been recognized as indisputable evidence of anthropogenic global warming. Further amplified warming is predicted for the coming decades. Therefore, a climatological assessment over recent decades is essential for mitigating environmental disasters. A comprehensive overview of the climate mean-state and variability across this region is provided using satellite-derived sea surface temperature (SST), surface wind, and Chlorophyll-a concentration (Chl-a). With more signature over the northwest and center of the Gulf, SST and wind are closely connected in which summers with intensified winds are not dramatically warm. It supports the notion that summertime northwesterly winds in the Gulf protect marine ecosystems from severe warm anomalies. Except in the south of the Gulf, the SST and Chl-a are anti-correlated. Our finding suggests that the Indian Ocean Dipole cycles considerably affect SST and winds over the western and central portions of the Gulf and the southern sector of the Oman Sea. Our evaluation of regional SST trends indicates a heterogeneous regional warming, which implies a significant contribution of small-scale processes in SST changes. This widens the uncertainty in regional climate projections. Furthermore, locally measured temperatures in several intertidal zones depict pronounced diurnal variations missing in satellite images, highlighting the need for an observation network to assess the localized impacts of global warming on the intertidal biota.

Persistent Ocean Anomalies as a Response to Northern Hemisphere Heating Induced by Biomass Burning Variability

Abstract Biomass burning aerosol (BBA) emissions in the Coupled Model Intercomparison Project phase 6 (CMIP6) historical forcing fields have enhanced temporal variability during the years 1997–2014 compared to earlier periods. Recent studies document that the corresponding inhomogeneous shortwave forcing over this period can cause changes in clouds, permafrost, and soil moisture, which contribute to a net terrestrial Northern Hemisphere warming relative to earlier periods. Here, we investigate the ocean response to the hemispherically asymmetric warming, using a 100-member ensemble of the Community Earth System Model version 2 Large Ensemble forced by two different BBA emissions (CMIP6 default and temporally smoothed over 1990–2020). Differences between the two subensemble means show that ocean temperature anomalies occur during periods of high BBA variability and subsequently persist over multiple decades. In the North Atlantic, surface warming is efficiently compensated for by decreased northward oceanic heat transport due to a slowdown of the Atlantic meridional overturning circulation. In the North Pacific, surface warming is compensated for by an anomalous cross-equatorial cell (CEC) that reduces northward oceanic heat transport. The heat that converges in the South Pacific through the anomalous CEC is shunted into the subsurface and contributes to formation of long-lasting ocean temperature anomalies. The anomalous CEC is maintained through latitude-dependent contributions from narrow western boundary currents and basinwide near-surface Ekman transport. These results indicate that interannual variability in forcing fields may significantly change the background climate state over long time scales, presenting a potential uncertainty in CMIP6-class climate projections forced without interannual variability.

Persistent Model Biases in the Spatial Variability of Winter North Atlantic Atmospheric Circulation

AbstractThe three leading modes of the North Atlantic atmospheric circulation explain about 70% of the winter climate variability. Although climate models generally can capture these modes, biases may induce large uncertainties in regional climate predictions. Here, we evaluate the leading winter modes simulated by CMIP5‐PMIP3 and CMIP6‐PMIP4 models from the last millennium to future scenarios in comparison with historical reanalysis and paleo‐reconstructions. The models generally have a good representation of the average spatial pattern of the North Atlantic Oscillation (NAO) while showing a larger spread in performance for the East Atlantic and Scandinavian patterns. In contrast to historical reanalysis, the simulated NAO pattern tends to be rather stationary under various climate states over the years 861–2100. Such underestimated spatial variability in the simulated NAO is directly related to the biased spatial shifts in NAO‐related regional temperature and precipitation changes, inducing uncertainties in climate projections over the North Atlantic sector.

Improving building resilience in the face of future climate uncertainty: A comprehensive framework for enhancing building life cycle performance

This research introduces an innovative resilient design framework, addressing gaps in building performance optimization by considering a holistic life cycle perspective and factoring in climate projection uncertainties from the 2020s to 2090s. The study concludes that in the case study, future climate scenarios in different Shared Socio-Economic Paths significantly impact building life cycle performance, with wall U-value, windows U-value, and wall density identified as major factors affecting building performance under future climate uncertainty. The ensemble learning model achieves high fidelity in predicting life cycle carbon emissions (LCCE), life cycle cost (LCC), and indoor discomfort hours (IDH) through input feature screening and hyperparameter optimization. Comparing optimization algorithms, Two-Archive Evolutionary Algorithm for Constrained multi-objective optimization (C-TAEA) demonstrates better convergence degree and optimization effect. Among the tested multi-criteria decision making methods, the VIKOR method selects the best building resilient design scheme from the Pareto data set, effectively reducing LCCE, LCC, and IDH, and leading to an overall improvement in building life cycle performance. Applying this framework, the finalized scheme for an office building in cold region optimizes 44.0% of LCCE, 8.2% of LCC, and 4.3% of IDH. This framework develops a new approach to improve building life cycle performance under future climate uncertainty and supports informed decision-making for resilient building design. To promote carbon neutrality in construction, it is urged that future studies incorporate additional sources of uncertainty, such as occupant behavior, into the proposed framework.

On the assessment of the wave modeling uncertainty in wave climate projections

Abstract This study investigates the epistemic uncertainty associated with the wave propagation modeling in wave climate projections. A single-forcing, single-scenario, seven-member global wave climate projection ensemble is used, developed using three wave models with a consistent numerical domain. The uncertainty is assessed through projected changes in wave height, wave period, and wave direction. The relative importance of the wave model used and its internal parameterization are examined. The former is the dominant source of uncertainty in approximately two-thirds of the global ocean. The study reveals divergences in projected changes from runs of different models and runs of the same model with different parameterizations over 75% of the ensemble mean change in several ocean regions. Projected changes in the wave period shows the most significant uncertainties, particularly in the Pacific Ocean basin, while the wave height shows the least. Over 30% of global coastlines exhibit significant uncertainties in at least two out of the three wave climate variables analyzed. The coasts of western North America, the Maritime Continent and the Arabian Sea show the most significant wave modeling uncertainties.

Biomass-burning smoke's properties and its interactions with marine stratocumulus clouds in WRF-CAM5 and southeastern Atlantic field campaigns

Abstract. A large part of the uncertainty in climate projections comes from uncertain aerosol properties and aerosol–cloud interactions as well as the difficulty in remotely sensing them. The southeastern Atlantic functions as a natural laboratory to study biomass-burning smoke and to constrain this uncertainty. We address these gaps by comparing the Weather Research and Forecasting with Chemistry Community Atmosphere Model (WRF-CAM5) to the multi-campaign observations ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS), CLARIFY (CLoud–Aerosol–Radiation Interaction and Forcing), and LASIC (Layered Atlantic Smoke Interactions with Clouds) in the southeastern Atlantic in August 2017 to evaluate a large range of the model's aerosol chemical properties, size distributions, processes, and transport, as well as aerosol–cloud interactions. Overall, while WRF-CAM5 is able to represent smoke properties and transport, some key discrepancies highlight the need for further analysis. Observations of smoke composition show an overall decrease in aerosol mean diameter as smoke ages over 4–12 d, while the model lacks this trend. A decrease in the mass ratio of organic aerosol (OA) to black carbon (BC), OA:BC, and the OA mass to carbon monoxide (CO) mixing ratio, OA:CO, suggests that the model is missing processes that selectively remove OA from the particle phase, such as photolysis and heterogeneous aerosol chemistry. A large (factor of ∼2.5) enhancement in sulfate from the free troposphere (FT) to the boundary layer (BL) in observations is not present in the model, pointing to the importance of properly representing secondary sulfate aerosol formation from marine dimethyl sulfide and gaseous SO2 smoke emissions. The model shows a persistent overprediction of aerosols in the marine boundary layer (MBL), especially for clean conditions, which multiple pieces of evidence link to weaker aerosol removal in the modeled MBL than reality. This evidence includes several model features, such as not representing observed shifts towards smaller aerosol diameters, inaccurate concentration ratios of carbon monoxide and black carbon, underprediction of heavy rain events, and little evidence of persistent biases in modeled entrainment. The average below-cloud aerosol activation fraction (NCLD/NAER) remains relatively constant in WRF-CAM5 between field campaigns (∼0.65), while it decreases substantially in observations from ORACLES (∼0.78) to CLARIFY (∼0.5), which could be due to the model misrepresentation of clean aerosol conditions. WRF-CAM5 also overshoots an observed upper limit on liquid cloud droplet concentration around NCLD= 400–500 cm−3 and overpredicts the spread in NCLD. This could be related to the model often drastically overestimating the strength of boundary layer vertical turbulence by up to a factor of 10. We expect these results to motivate similar evaluations of other modeling systems and promote model development to reduce critical uncertainties in climate simulations.

Generalized Aerosol/Chemistry Interface (GIANT): A Community Effort to Advance Collaborative Science across Weather and Climate Models

Abstract Atmospheric aerosol and chemistry modules are key elements in Earth system models (ESMs), as they predict air pollutant concentrations and properties that can impact human health, weather, and climate. The current uncertainty in climate projections is partly due to the inaccurate representation of aerosol direct and indirect forcing. Aerosol/chemistry parameterizations used within ESMs and other atmospheric models span large structural and parameter uncertainties that are difficult to assess independently of their host models. Moreover, there is a strong need for a standardized interface between aerosol/chemistry modules and the host model to facilitate portability of aerosol/chemistry parameterizations from one model to another, allowing not only a comparison between different parameterizations within the same modeling framework, but also quantifying the impact of different model frameworks on aerosol/chemistry predictions. To address this need, we have initiated a new community effort to coordinate the construction of a Generalized Aerosol/Chemistry Interface (GIANT) for use across weather and climate models. We aim to organize a series of community workshops and hackathons to design and build GIANT, which will serve as the interface between a range of aerosol/chemistry modules and the physics and dynamics components of atmospheric host models. GIANT will leverage ongoing efforts at the U.S. modeling centers focused on building next-generation ESMs and the international AeroCom initiative to implement this common aerosol/chemistry interface. GIANT will create transformative opportunities for scientists and students to conduct innovative research to better characterize structural and parametric uncertainties in aerosol/chemistry modules, and to develop a common set of aerosol/chemistry parameterizations.