Climate Model Uncertainty Research Articles

An Overview of CMIP5 and CMIP6 Simulated Cloud Ice, Radiation Fields, Surface Wind Stress, Sea Surface Temperatures, and Precipitation Over Tropical and Subtropical Oceans

AbstractThe potential links between ice water path (IWP), radiation, circulation, sea surface temperature (SST), and precipitation over the Pacific and Atlantic Oceans resulting from the falling ice radiative effects (FIREs) are examined from Coupled Model Intercomparison Project phase 5 (CMIP5) and phase 6 (CMIP6) historical simulations. The latter is divided into two subsets with (SON6) and without FIREs (NOS6) in CMIP6. Improvement in nonfalling cloud ice (~20 g m−2) is noticeable over convective regions in CMIP6 relative to CMIP5. The inclusion of FIREs in SON6 subset may contribute to reduce biases of overestimated outgoing longwave radiation and downward surface shortwave and underestimated reflected shortwave at the top of the atmosphere (TOA) by magnitudes of ~8 W m−2 over convective regions against CERES, compared to NOS6 subset. The reduced biases in radiative fluxes in convective regions stabilize the atmosphere and lead to circulation, SST, cloud, and precipitation changes over the trade wind regions, as seen from improved radiative fluxes (~15 W m−2), surface wind stress biases, SST (~0.8 K), and precipitation (1 mm day−1) biases. The significant improvement from NOS6 to SON6 leads to improved multimodel means for CMIP6 relative to CMIP5 for radiation fields over the trade wind regions but the degradation over convective zones is attributed to NOS6 subset. The results suggest that other sources of uncertainty and deficiencies in climate models may play significant roles for reducing discrepancies although FIREs, via radiation‐circulation coupling, may be one of the factors that help to reduce regional biases.

New Zealand River Hydrology under Late 21st Century Climate Change

Climate change is increasingly affecting the water cycle and as freshwater plays a vital role in countries’ societal and environmental well-being it is important to develop national assessments of potential climate change impacts. Focussing on New Zealand, a climate-hydrology model cascade is used to project hydrological impacts of late 21st century climate change at 43,862 river locations across the country for seven hydrological metrics. Mean annual and seasonal river flows validate well across the whole model cascade, and the mean annual floods to a lesser extent, while low flows exhibit a large positive bias. Model projections show large swathes of non-significant effects across the country due to interannual variability and climate model uncertainty. Where changes are significant, mean annual, autumn, and spring flows increase along the west and south and decrease in the north and east. The largest and most extensive increases occur during winter, while during summer decreasing flows outnumber increasing. The mean annual flood increases more in the south, while mean annual low flows show both increases and decreases. These hydrological changes are likely to have important long-term implications for New Zealand’s societal, cultural, economic, and environmental well-being.

North Atlantic climate far more predictable than models imply.

Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change1-3. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain4. This leads to low confidence in regional projections, especially for precipitation, over the coming decades5,6. The chaotic nature of the climate system7-9 may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models10, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade.

Comparison of south-east Atlantic aerosol direct radiative effect over clouds from SCIAMACHY, POLDER and OMI–MODIS

Abstract. The direct radiative effect (DRE) of aerosols above clouds has been found to be significant over the south-east Atlantic Ocean during the African biomass burning season due to elevated smoke layers absorbing radiation above the cloud deck. So far, global climate models have been unsuccessful in reproducing the high DRE values measured by various satellite instruments. Meanwhile, the radiative effects by aerosols have been identified as the largest source of uncertainty in global climate models. In this paper, three independent satellite datasets of DRE during the biomass burning season in 2006 are compared to constrain the south-east Atlantic radiation budget. The DRE of aerosols above clouds is derived from the spectrometer SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY), the polarimeter Polarization and Directionality of the Earth's Reflectances (POLDER), and collocated measurements by the spectrometer Ozone Monitoring Instrument (OMI) and the imager Moderate Resolution Imaging Spectroradiometer (MODIS). All three datasets confirm the high DRE values during the biomass season, underlining the relevance of local aerosol effects. Differences between the instruments can be attributed mainly to sampling issues. When these are accounted for, the remaining differences can be explained by a higher cloud optical thickness (COT) derived from POLDER compared to the other instruments and a neglect of aerosol optical thickness (AOT) at shortwave infrared (SWIR) wavelengths in the method used for SCIAMACHY and OMI–MODIS. The higher COT from POLDER by itself can explain the difference found in DRE between POLDER and the other instruments. The AOT underestimation is mainly evident at high values of the aerosol DRE and accounts for about a third of the difference between POLDER and OMI–MODIS DRE. The datasets from POLDER and OMI–MODIS effectively provide lower and upper bounds for the aerosol DRE over clouds over the south-east Atlantic, which can be used to challenge global circulation models (GCMs). Comparisons of model and satellite datasets should also account for sampling issues. The complementary DRE retrievals from OMI–MODIS and POLDER may benefit from upcoming satellite missions that combine spectrometer and polarimeter measurements.

Reactive VOC Production from Photochemical and Heterogeneous Reactions Occurring at the Air-Ocean Interface.

The ocean surface serves as a source and sink for a diverse set of reactive trace gases in the atmosphere, including volatile organic compounds (VOCs), reactive halogens, and oxidized and reduced nitrogen compounds. The exchange of reactive trace gases between the atmosphere and ocean has been shown to alter atmospheric oxidant concentrations and drive particle nucleation and growth. Uncertainties in cloud radiative forcing and aerosol-cloud interactions are among the largest uncertainties in current global climate models. Climate models are particularly sensitive to cloud cover over the remote ocean due to large changes in albedo between the ocean surface and cloud tops. Oceanic emissions contribute to cloud condensation nuclei concentrations, either through the direct emission of particles during wave breaking or through the formation of secondary aerosol particles following the emission of reactive gas-phase compounds. Despite generally small and diffuse oceanic emission rates for reactive trace gases, it has been shown that oxidant and particle number concentrations are acutely sensitive to air-sea trace gas exchange rates and the chemical composition of emitted species. To date, field measurements of air-sea reactive gas exchange have focused primarily on the emission of gases of biological origin, such as dimethyl sulfide (DMS). While DMS emissions are relatively well constrained, the gas-phase oxidation that connects DMS to sulfate aerosol is less well understood. Recent laboratory measurements suggest that heterogeneous and photochemical reactions occurring at the air-sea interface can also lead to the production and emission of a wide array of reactive VOC. When laboratory-based measurements are used to derive global scale emissions, the calculated sea-to-air fluxes of reactive VOC generated from heterogeneous and photochemical processes are comparable or larger in magnitude to the sea-to-air flux of DMS. It is not yet clear how the mechanisms proposed in these laboratory experiments translate to atmospheric conditions. The proposed abiotic emissions are also a potential source of VOC in regions of low biological activity, which carries important implications for regional and global modeling.This Account reviews recent laboratory and field experiments of biotic and abiotic ocean VOC emissions, with a specific focus on exploring open questions related to proposed abiotic reactive VOC emissions and the impact of including a large, abiotic VOC emission source on atmospheric oxidants and aerosol particles. To date, abiotic emissions are not typically included in global chemical transport models. The proposed abiotic emissions mechanisms discussed here have the potential to drive significant changes to current understanding of chemistry in the marine atmosphere if present at the magnitudes suggested by laboratory studies. In order to validate their proposed significance, a coordinated set of laboratory, field, and modeling studies under ocean-relevant conditions are necessary.

Added Value of Aerosol-Cloud Interactions for Representing Aerosol Optical Depth in an Online Coupled Climate-Chemistry Model over Europe

Aerosol-cloud interactions (ACI) represent one of the most important sources of uncertainties in climate modelling. In this sense, realistic simulations of ACI are needed for a better understanding of the complex interactions between air pollution and the climate system. This work quantifies the added value of including ACI in an online coupled climate/chemistry model (WRF-Chem, 0.44 ∘ horizontal resolution, years 2003 to 2010) in order to assess whether there is an improvement in the representation of aerosol optical depth (AOD). Modelling results for each species have been evaluated against the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, and AOD at 675 nm has been compared to AERONET data. Results indicate that the improvements of the monthly biases are around 8% for total AOD550 when including ACI, reaching 20% for the monthly bias in AOD550 coming from dust. Moreover, the temporal representation of AOD550 largely improves (increase in the Pearson time correlation coefficients), ranging from 6% to 20% depending on the chemical species considered. The benefits from this improvement overcome the problems derived from the high computational time required in ACI simulations (eight times higher with respect to simulations not including aerosol-cloud interactions).

Features and Characteristics of the new NASA MicroPuLse NETwork (MPLNET) automatic rain detection algorithm

The water cycle strongly influences life on Earth. In particular, the precipitation modifies the atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially low-intensity precipitation) at global scale, bedsides improving our understanding of the hydrological cycle, it is crucial to reduce the associated uncertainty of the global climate models to correctly forecast future scenarios, i.e. to apply fast mitigation strategies. In this study we developed an algorithm to automatically detect precipitation from lidar measurements obtained by the National and Aeronautics Space Administration (NASA) Micropulse lidar network (MPLNET) permanent observational site in Goddard. The algorithm, once full operational, will deliver in Near Real Time (latency 1.5h) a new rain mask product that will be publicly available on MPLNET website as part of the new Version 3 Level 1.5 data. The methodology, based on an image processing technique, can detect only light precipitation events (defined by intensity and duration) as the morphological filters used through the detection process are applied on the lidar volume depolarization ratio range corrected composite images, i.e. heavy rain events are unusable as the lidar signal is completely extinguished after few meters in the precipitation or no signal detected because of the water accumulated on the receiver optics.

Precipitation phase uncertainty in cold region conceptual models resulting from meteorological forcing time-step intervals

Abstract Precipitation phase determination is a known source of uncertainty in surface-based hydrological, ecological, safety, and climate models. This is primarily due to the surface precipitation phase being a result of cloud and atmospheric properties not measured at surface meteorological or hydrological stations. Adding to the uncertainty, many conceptual hydrological models use a 24-h average air temperature to determine the precipitation phase. However, meteorological changes to atmospheric properties that control the precipitation phase often substantially change at sub-daily timescales. Model uncertainty (precipitation phase error) using air temperature (AT), dew-point temperature (DP), and wet-bulb temperature (WB) thresholds were compared using averaged and time of observation readings at 1-, 3-, 6-, 12-, and 24-h periods. Precipitation phase uncertainty grew 35–65% from the use of 1–24 h data. Within a sub-dataset of observations occurring between AT −6 and 6 °C representing 57% of annual precipitation, misclassified precipitation was 7.9% 1 h and 11.8% 24 h. Of note, there was also little difference between 1 and 3 h uncertainty, typical time steps for surface meteorological observations.

Ranking uncertainty: Wave climate variability versus model uncertainty in probabilistic assessment of coastline change

Sand nourishments are increasingly applied as adaptive coastal protection measures. Predictions of the evolution of these nourishments and their impact on the surrounding coastline contain many uncertainties. The sources that add to this uncertainty can be delineated between intrinsic and epistemic uncertainty, i.e. inevitably in the system or related to knowledge limitations. Effects of intrinsic uncertainty (e.g. due to wave climate variability) on coastal evolution can be significant. In studying these effects, it has often been assumed that intrinsic uncertainty is dominant over epistemic uncertainty (e.g. introduced by the model), yet the magnitude of both contributions have not been explicitly quantified to assess the validity of this assumption. This paper examines the relative importance of intrinsic and epistemic uncertainty in coastline modeling of a large-scale nourishment. It uses a probabilistic framework in which sediment transport is considered to be a function of random wave forcing (intrinsic) and model (epistemic) uncertainty, calculating transport using a one-line model. The test case for this analysis is the mega-nourishment, the Sand Engine, located in the Netherlands. The applied wave climate variability is obtained from long term wave observations, whereas model uncertainty is quantified using the Generalized Likelihood Uncertainty Estimation (GLUE) method relying on monthly observations. We find that the confidence intervals on predicted volume losses increase substantially when including both intrinsic and epistemic sources of uncertainty. A global sensitivity analysis shows that ignoring model uncertainty would underestimate the variance by at least 50% after a 2.5-year simulation period for the Sand Engine, hence producing significant overconfidence in the results. These findings imply that for coastal modeling purposes a dual approach should be considered, evaluating both epistemic and intrinsic uncertainties.

Projected changes of bivariate flood quantiles and estimation uncertainty based on multi-model ensembles over China

As atmospheric moisture capacity is highly sensitive to rising temperatures, precipitation extremes are widely projected to intensify with a warming climate and thus altering the flooding generation regime. Previous works seldomly focused on bivariate flood quantiles under climate change at a national scale, and fewer flooding projections quantified the estimation uncertainty sourced from sample size limitation. This study systematically investigates the changes in bivariate quantiles of flood peak and volume with incorporation of sampling uncertainty for 151 catchments over China, with climate trajectories projected by a set of multi-model ensemble under representative concentration pathway (RCP) 8.5. After correcting the systematical biases of eight CMIP5 GCM outputs, four state-of-the-art hydrological models are driven and validated for each catchment, and the best-simulation model is selected to project future streamflow scenarios. The copula function is employed to construct the joint distribution of flood peak and volume, and then the most likely realizations of bivariate quantiles are derived under different Joint Return Periods (JRPs), with the uncertainty envelope quantified with the area of 90% confidence ellipse by a copula-based parametric bootstrapping uncertainty (C-PBU) approach. Our results project an overall ascending trend of temperature and precipitation over China, and the bivariate flood quantiles and corresponding estimation uncertainty of most catchments in the future period (2056–2100) are much larger than the baseline (1961–2005), despite accompanied by substantial climate model uncertainty and spatial variability in magnitude. Many basins would be subjected to a dramatic increase of flood magnitude by over 50%, while only few basins are projected to experience a decreasing flood risk, suggesting an urgent need to increase societal resilience to a warming climate over China.

Amplification of hydrological model uncertainties in projected climate simulations of the Upper Indus Basin: Does it matter where the water is coming from?

AbstractThere is a growing appreciation of the uncertainties in the estimation of snow‐melt and glacier‐melt as a result of climate change in high elevation catchments. Through a detailed examination of three hydrological models in two catchments, and interpretation of results from previous studies, we observed that many variations in estimated streamflow could be explained by the selection of a best parameter set from the possible good model parameters. The importance of understanding changing glacial dynamics is critically important for our study areas in the Upper Indus Basin where Pakistan's policymakers are planning infrastructure to meet the future energy and water needs of hundreds of millions of people downstream. Yet, the effect of climate on glacial runoff and climate on snowmelt runoff is poorly understood. With the HBV model, for example, we estimated glacial melt as between 56% and 89% for the Hunza catchment. When rainfall was a scaled parameter, the models estimated glacial melt as between 20% and 100% of streamflow. These parameter sets produced wildly different projections of future climate for RCP8.5 scenarios in 2046–2075 compared to 1976–2005. Assuming no glacial shrinkage, for one climate projection, we found that the choice among good parameter sets resulted in projected values of future streamflow across a range from +54% to +125%. Parameter selection was the most significant source of uncertainty in the glaciated catchment and amplified climate model uncertainty, whereas climate model choice was more important in the rainfall dominated catchment. Although the study focuses on Pakistan, the overall conclusions are instructive for other similar regions in the world. We suggest that modellers of glaciated catchments should present results from at least the book‐ends: models with low sensitivity to ice‐melt and models with high sensitivity to ice‐melt. This would reduce confusion among decision makers when they are faced with similar contrasting results.

Reduced‐precision parametrization: lessons from an intermediate‐complexity atmospheric model

AbstractReducing numerical precision can save computational costs which can then be reinvested for more useful purposes. This study considers the effects of reducing precision in the parametrizations of an intermediate complexity atmospheric model (SPEEDY). We find that the difference between double‐precision and reduced‐precision parametrization tendencies is proportional to the expected machine rounding error if individual timesteps are considered. However, if reduced precision is used in simulations that are compared to double‐precision simulations, a range of precision is found where differences are approximately the same for all simulations. Here, rounding errors are small enough to not directly perturb the model dynamics, but can perturb conditional statements in the parametrizations (such as convection active/inactive) leading to a similar error growth for all runs. For lower precision, simulations are perturbed significantly. Precision cannot be constrained without some quantification of the uncertainty. The inherent uncertainty in numerical weather and climate models is often explicitly considered in simulations by stochastic schemes that will randomly perturb the parametrizations. A commonly used scheme is stochastic perturbation of parametrization tendencies (SPPT). A strong test on whether a precision is acceptable is whether a low‐precision ensemble produces the same probability distribution as a double‐precision ensemble where the only difference between ensemble members is the model uncertainty (i.e., the random seed in SPPT). Tests with SPEEDY suggest a precision as low as 3.5 decimal places (equivalent to half precision) could be acceptable, which is surprisingly close to the lowest precision that produces similar error growth in the experiments without SPPT mentioned above. Minor changes to model code to express variables as anomalies rather than absolute values reduce rounding errors and low‐precision biases, allowing even lower precision to be used. These results provide a pathway for implementing reduced‐precision parametrizations in more complex weather and climate models.

Modelling the effect of condensed-phase diffusion on the homogeneous nucleation of ice in ultra-viscous particles

Abstract. Computational models of cloud formation typically use homogeneous nucleation to predict the ice nucleated in supercooled water. However, the existence of ultra-viscous organic aerosol in the upper troposphere has offered alternative ice nucleation pathways, which have been observed in laboratory studies. The possible effects of aerosol viscosity on cloud microphysical properties have traditionally been interpreted from simple model simulations of an individual aerosol particle based on equilibration timescales. In this study, to gain insight into the formation of ice in an ensemble of ultra-viscous aerosol particles, we have developed the first cloud parcel model with bin microphysics to simulate condensed phase diffusion through each individual aerosol particle. Our findings demonstrate, for the first time, the complex relationship between the rate of ice formation and the viscosity of secondary organic aerosol, driven by two competing effects – which cannot be explained using existing modelling approaches. The first is inhibition of homogeneous ice nucleation below 200 K, due to restricted particle growth and low water volume. The second occurs at temperatures between 200 and 220 K, where water molecules are slightly more mobile, and a layer of water condenses on the outside of the particle, causing an increase in the number of frozen aerosol particles. Our new model provides a basis to better understand and simulate ice cloud formation on a larger scale, addressing a major source of uncertainty in climate modelling through the representation of microphysical cloud processes.

Dust Source, Vertical Profile and Climate Impact by RegCM3 Regional Climate Model over West Africa during 2006

This study aims to evaluate dust impact on climate parameters over the Sahel region by RegCM3 regional model during 2006. Indeed, aerosols are one of the main uncertainties in climate models. The aerosol optical depth (AOD) derived from RegCM3 model has been validated with various observed datasets. The aerosol sources are identified over North Algeria and East of Sahel (Bodele depression). Discrepancies are noted when considering dust temporal and spatial distribution. Dust season extends between March and October, with two peaks of AOD recorded in March (spring) and June (summer). The dust vertical distribution showed that the mineral aerosol layer is located between 850 hPa and 300 hPa (1.5 km to 7 km). The RegCM3 model simulates fairly well the transport in the upper layers, especially in the Saharan Air Layer (SAL) during the summer. However, RegCM3 simulates poorly the transport and sedimentation of particles in the lower layers (below 2 km). The investigation of dust radiative impact shows a general cooling. The maximum of radiative forcing is located around 18°N - 20°N, with values of about -80 W/m2 in June - August (JJA) and -40 W/m2 at the surface during March - May (MAM). This study also showed the indirect effect of dust with a decrease in precipitation about -0.7 mm/day around 15 - 20°N during the rainy season.

Fully Automated Light Precipitation Detection from MPLNET and EARLINET Network Lidar Measurements

The water cycle strongly influence life on Earth and precipitation especially modifies the atmospheric column thermodynamics through the evaporation process and serving as a proxy for latent heat modulation. For this reason, a correct light precipitation parameterization at global scale, it is of fundamental importance, bedsides improving our understanding of the hydrological cycle, to reduce the associated uncertainty of the global climate models to correctly forecast future scenarios. In this context we developed a full automatic algorithm based on morphological filters that, once operational, will make available a new rain product for the NASA Micropulse Lidar Network (MPLNET) and the European Aerosol Research Lidar Network (EARLINET) in the frame of WMO GALION Project

Overview of the New Version 3 NASA Micro-Pulse Lidar Network (MPLNET) Automatic Precipitation Detection Algorithm

Precipitation modifies atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially for low-intensity precipitation) within global scale models is crucial. In addition to improving our modeling of the hydrological cycle, this will reduce the associated uncertainty of global climate models in correctly forecasting future scenarios, and will enable the application of mitigation strategies. In this manuscript we present a proof of concept algorithm to automatically detect precipitation from lidar measurements obtained from the National Aeronautics and Space Administration Micropulse lidar network (MPLNET). The algorithm, once tested and validated against other remote sensing instruments, will be operationally implemented into the network to deliver a near real time (latency <1.5 h) rain masking variable that will be publicly available on MPLNET website as part of the new Version 3 data products. The methodology, based on an image processing technique, detects only light precipitation events (defined by intensity and duration) such as light rain, drizzle, and virga. During heavy rain events, the lidar signal is completely extinguished after a few meters in the precipitation or it is unusable because of water accumulated on the receiver optics. Results from the algorithm, in addition to filling a gap in light rain, drizzle, and virga detection by radars, are of particular interest for the scientific community as they help to fully characterize the aerosol cycle, from emission to deposition, as precipitation is a crucial meteorological phenomenon accelerating atmospheric aerosol removal through the scavenging effect. Algorithm results will also help the understanding of long term aerosol–cloud interactions, exploiting the multi-year database from several MPLNET permanent observational sites across the globe. The algorithm is also applicable to other lidar and/or ceilometer network infrastructures in the framework of the Global Aerosol Watch (GAW) aerosol lidar observation network (GALION).

Study of aerosol optical properties in the Middle East during 2013.

Aerosols affect the earth's atmospheric radiative fluxes via direct, semi-direct, and indirect mechanisms. Aerosols also are one of the main sources of uncertainty in climate models. In the Middle East, in addition to climate effects, various problems such as reduced visibility, human health hazards, and air pollution are caused by aerosols. Studying the optical and physical properties of aerosols on local and global scales helps reduce the uncertainties in climate forcing. In this study, aerosol optical properties, including Aerosol Optical Depth (AOD), Angstrom Exponent (AE), ASYmmetry parameter (ASY), Single Scattering Albedo (SSA), and phase function were analyzed. These properties were investigated over five sites in the Middle East during 2013 using the Aerosol Robotic NETwork (AERONET) data. The results revealed an inverse relationship between AOD and AE in all sites. A high AOD value and a low AE value were detected in spring and summer in all studied sites, suggestive of coarse mode dust particles. ASY initially decreased due to the dominance of absorbing type aerosols in the visible spectrum with the increase in wavelength. Afterwards, ASY increased with the increase in wavelength in the infrared region due to the dominance of the coarse mode particles. In most sites, SSA increased, particularly in spring and summer, with the increase in the wavelength because of the dominance of desert dust. In spring and summer, the phase function was high over all sites. High phase functions associated with small scattering angles were caused by the coarse mode particles.

Atmospheric Radiation Measurement (ARM) Aerosol Observing Systems (AOS) for Surface-Based In Situ Atmospheric Aerosol and Trace Gas Measurements

AbstractAerosols alter Earth’s radiative budget both directly and indirectly through interaction with clouds. Continuous observations are required to reduce the uncertainties in climate models associated with atmospheric processing and the interactions between aerosols and clouds. Field observations of aerosols are a central component of the Atmospheric Radiation Measurement (ARM) Facility’s global measurements. The ARM mission goal is to “provide the climate research community with strategically located in situ and remote sensing observatories designed to improve the understanding and representation, in climate and earth system models, of clouds and aerosols as well as their interactions and coupling with the Earth’s surface.” Since 1996, ARM has met this goal by operating Aerosol Observing Systems (AOS) for in situ measurement of aerosols. Currently the five ARM AOSs are the most comprehensive field deployable aerosol systems in the United States. The AOS suite includes seven measurement classes: number concentration, size distribution, chemical composition, radiative and optical properties, hygroscopicity, trace gases, and supporting meteorological conditions. AOSs are designed as standardized measurement platforms to enable intercomparison across the ARM Facility for regional process studies within a global context. The instrumentation and measurement capabilities of the ARM AOSs, along with a history of their design and field deployments are presented here.

Latitudinal heterogeneity and hotspots of uncertainty in projected extreme precipitation

Projected precipitation from climate models is used in a wide range of fields for climate change impact assessment. However, the spatial pattern of uncertainty across latitudes and the global uncertainty hotspots are not well understood despite their importance for regional adaptation planning. In this study, we describe uncertainties in projected extreme precipitation changes per K global warming across latitudes, and decompose the overall uncertainty into climate model and internal variability uncertainties. We then identify global uncertainty hotspots and discuss the broader implications. Our results show that both uncertainty sources are highly heterogeneous across latitudes, while climate model uncertainty exceeds internal variability uncertainty for all seasons and precipitation intensities. The largest difference between model and internal variability uncertainties is found in tropical regions where model uncertainty is thrice as large as internal variability uncertainty in June–July–August season and twice as large as that in the other seasons. Tropical and subtropical regions are identified as the global uncertainty hotspots, with the Sahara desert and the southern part of the Middle East being the local hotspots. The large uncertainty in the tropics and subtropics is primarily due to the convective nature of rainstorms which cannot be adequately represented by coarse-scale climate models, and also to sparse observation networks based on which climate models can be tuned and improved. The results highlight areas where future model development and improvement efforts should focus to reduce the overall uncertainties in projected precipitation extremes.

Viewing Forced Climate Patterns Through an AI Lens

AbstractMany problems in climate science require extracting forced signals from a background of internal climate variability. We demonstrate that artificial neural networks (ANNs) are a useful addition to the climate science “toolbox” for this purpose. Specifically, forced patterns are detected by an ANN trained on climate model simulations under historical and future climate scenarios. By identifying spatial patterns that serve as indicators of change in surface temperature and precipitation, the ANN can determine the approximate year from which the simulations came without first explicitly separating the forced signal from the noise of both internal climate variability and model uncertainty. Thus, the ANN indicator patterns are complex, nonlinear combinations of signal and noise and are identified from the 1960s onward in simulated and observed surface temperature maps. This approach suggests that viewing climate patterns through an artificial intelligence (AI) lens has the power to uncover new insights into climate variability and change.