Climate Model Uncertainty Research Articles

Direct Spectroscopic Quantification of the Absorption and Scattering Properties for Single Aerosol Particles.

Understanding the optical properties of micrometer-scale light-absorbing aerosol particles is of paramount importance in addressing key challenges in atmospheric and physical chemistry. For example, the absorption of solar radiation by atmospheric aerosols represents one of the largest uncertainties in climate models. Moreover, reaction acceleration within the unique environments of aerosol droplets cannot be replicated in bulk solutions. The causes of these reaction rate enhancements remain controversial, but ultrasensitive spectroscopic measurements of evolving aerosol optical properties should provide new insights. We demonstrate a new approach using cavity ring-down spectroscopy that allows the first direct spectroscopic quantification of the continuously evolving absorption and scattering cross sections for single, levitated, micrometer-scale particles as their size and chromophore concentration change. For two-component droplets composed of nigrosin and 1,2,6-hexanetriol, the unprecedented sensitivity of our measurements reveals the evolving real and imaginary components of the refractive index caused by changes in concentration as 1,2,6-hexanetriol slowly evaporates.

Representation of the autoconversion from cloud to rain using a weighted ensemble approach: a case study using WRF v4.1.3

Abstract. Cloud and precipitation processes remain among the largest sources of uncertainties in weather and climate modelling, and considerable attention has been paid to improving the representation of the cloud and precipitation processes in numerical models in the last several decades. In this study, we develop a weighted ensemble (named EN) scheme by employing several widely used autoconversion (ATC) schemes to represent the ATC from cloud water to rainwater. One unique feature of the EN approach is that the ATC rate is a weighted mean value based on the calculations from several ATC schemes within a microphysics scheme with a negligible increase in computation cost. The EN scheme is compared with the several commonly used ATC schemes by performing real case simulations. In terms of accumulated rainfall and extreme hourly rainfall rate, the EN scheme provides better simulations than by using the single Berry–Reinhardt scheme, which was originally used in the Thompson scheme. It is worth emphasizing, in the present study, that we only pay attention to the ATC process from cloud water into rainwater with the purpose of improving the modelling of the extreme rainfall events over southern China. Actually, any (source and sink) term in a cloud microphysics scheme can be treated with the same approach. The ensemble method proposed herein appears to have important implications for developing cloud microphysics schemes in numerical models, especially for the models with variable grid resolution, which would be expected to improve the representation of cloud microphysical processes in the weather and climate models.

Assessment of soil erosion under long-term projections of climate change in Poland

Soil is a major natural resource fundamental for food production, and a habitat for living organisms. It is therefore extremely important to prevent its erosion. It is assumed that 50% of erosion at the global scale is caused by water. A relatively low number of studies model soil loss in Northern and Eastern Europe. Although that region currently contributes to total soil loss the least, the projected long-term climate change in Europe is likely to affect soil loss due to changes in rainfall characteristics. This study quantifies historical soil loss rates based on high-resolution datasets, and assesses the impact of the projected climate change on long-term average annual soil erosion rates in Poland, located in the temperate climate zone, using the Revised Universal Soil Loss Equation (RUSLE) model. The study investigates the projected changes for two future time horizons, namely: 2021-2050 (near future – NF) and 2071-2100 (far future – FF), under two different Representative Concentration Pathways (RCP) 4.5 and 8.5. To account for the uncertainty of individual climate models, an ensemble of nine EURO-CORDEX model scenarios was used. Based on the multi-model ensemble median, the average soil erosion rate in Poland for the historical period is 0.48 t ha−1 yr−1. The total annual soil loss in Poland is estimated at 14 Mt. The highest soil loss rate is observed in the south of Poland with mean annual erosion rate of 1.7 t ha−1 yr−1. Although it occupies only 16% of the territory of Poland, its share in total soil loss in the country is 62% (8.6 Mt year−1). The average projected potential soil erosion by water in the future increases gradually, reaching +7% (RCP4.5 NF), +8% (RCP8.5 NF), +13% (RCP4.5 FF), and +18% (RCP8.5 FF). The results of this study might be valuable for the development of the national soil conservation policy. The study fills the spatial gap in modelling of changes in soil erosion caused by climate in the Northern and Eastern part of Europe. Due to the lack of other national studies in the region, the directions of such changes could be transferrable to countries in the same climate zones, with a similar vegetation cover structure.

Dominant spatial patterns of interannual variability in summer precipitation across northern Eurasia from Coupled Model Intercomparison Project Phase 5 models

There is a lack of studies elucidating the characteristics of interannual variability in different models, especially for northern Eurasia. Therefore, spatial patterns of interannual variability in summer precipitation over northern Eurasia across 15 models of Coupled Model Intercomparison Project Phase 5 (CMIP5) were investigated using empirical orthogonal function (EOF) analysis and cluster analysis. The spatial patterns of EOF1-3 obtained from the models exhibited similar features across the models. The spatial pattern characteristics of the interannual variability of models were similar to those of the actual observations. However, their contribution rates and the order of magnitude of the EOFs in each model differ among models and from observations. Despite the similarity between the patterns of the EOF in the models and the observations, the atmospheric circulations causing the pattern differed in the models and actual observations, resulting in uncertainty in the models. This indicates that in the analysis of extreme events and their impact, the reproducibility and uncertainty of climate models with regard to interannual variability need to be adequately evaluated. In addition, similar to many climate models, the spatial pattern of interannual variability, which is different from any of EOF1-3 for the observations, has been more strongly represented than the actual observations. Clarification of the factors governing the common but unsuitable variability across models may be expected to decrease the uncertainty of the models in terms of interannual variability.

Evaluation of Temperature‐Dependent Complex Refractive Indices of Supercooled Liquid Water Using Downwelling Radiance and In‐Situ Cloud Measurements at South Pole

AbstractClouds have a large effect on the radiation budget and represent a major source of uncertainty in climate models. Supercooled liquid clouds can exist at temperatures as low as 235 K, and the radiative effect of these clouds depends on the complex refractive index (CRI) of liquid water. Laboratory measurements have demonstrated that the liquid‐water CRI is temperature‐dependent, but corroboration with field measurements is difficult. Here we present measurements of the downwelling infrared radiance and in‐situ measurements of supercooled liquid water in a cloud at temperatures as low as 240 K, made at South Pole Station in 2001. These results demonstrate that including the temperature dependence of the liquid‐water CRI is essential for accurate calculations of radiative transfer through supercooled liquid clouds. Furthermore, we show that when cloud properties are retrieved from infrared radiances (using the spectral range 500–1,200 cm−1) spurious ice may be retrieved if the 300 K CRI is used for cold liquid clouds (∼240 K). These results have implications for radiative transfer in climate models as well as for retrievals of cloud properties from infrared radiance spectra.

Minimax-Regret Climate Policy with Deep Uncertainty in Climate Modeling and Intergenerational Discounting

Integrated assessment models have become the primary tools for comparing climate policies that seek to reduce greenhouse gas emissions. Policy comparisons have often been performed by considering a planner who seeks to make optimal trade-offs between the costs of carbon abatement and the economic damages from climate change. The planning problem has been formalized as one of optimal control, the objective being to minimize the total costs of abatement and damages over a time horizon. Studying climate policy as a control problem presumes that a planner knows enough to make optimization feasible, but physical and economic uncertainties abound. Earlier, Manski, Sanstad, and DeCanio proposed and studied use of the minimax-regret (MMR) decision criterion to account for deep uncertainty in climate modeling. Here we study choice of climate policy that minimizes maximum regret with deep uncertainty regarding both the correct climate model and the appropriate time discount rate to use in intergenerational assessment of policy consequences. The analysis specifies a range of discount rates to express both empirical and normative uncertainty about the appropriate rate. The findings regarding climate policy are novel and informative. The MMR analysis points to use of a relatively low discount rate of 0.02 for climate policy. The MMR decision rule keeps the maximum future temperature increase below 2C above the 1900-10 level for most of the parameter values used to weight costs and damages.

Minimax-Regret Climate Policy with Deep Uncertainty in Climate Modeling and Intergenerational Discounting

Minimax-Regret Climate Policy with Deep Uncertainty in Climate Modeling and Intergenerational Discounting

Minimax-Regret Climate Policy with Deep Uncertainty in Climate Modeling and Intergenerational Discounting

Minimax-Regret Climate Policy with Deep Uncertainty in Climate Modeling and Intergenerational Discounting

Minimax-Regret Climate Policy with Deep Uncertainty in Climate Modeling and Intergenerational Discounting

Minimax-Regret Climate Policy with Deep Uncertainty in Climate Modeling and Intergenerational Discounting

Contribution of climate models and APSIM phenological parameters to uncertainties in spring wheat simulations: Application of SUFI‐2 algorithm in northeast Australia

AbstractWe used SUFI‐2 for the first time to calibrate the phenology module of the APSIM‐wheat model for 10 spring wheat cultivars cultivated in northeast Australia (south‐eastern Queensland). Calibration resulted in an average root mean square error (RMSE) of 5.5 days for developmental stages from stem elongation up to flowering. Projections from 33 climate models under the representative concentration pathway 8.5 were used for simulations at 17 sites. Using adapted sowing times, we simulated significantly shorter crop cycles and grain yield improvements for the period 2036–2065 relative to 1990–2019 for three selected cultivars (Hartog, Scout and Gregory). Photoperiod and vernalisation sensitivities were shown to be the largest and smallest contributors to total uncertainties in the simulated flowering day and grain yield, respectively. Uncertainties in climate models had a relatively minor contribution to the total uncertainties in the simulated values of target traits. This contribution significantly increased when climate change impact on the target traits was quantified.

Low‐Volatility Vapors and New Particle Formation Over the Southern Ocean During the Antarctic Circumnavigation Expedition

AbstractDuring summer, the Southern Ocean is largely unaffected by anthropogenic emissions, which makes this region an ideal place to investigate marine natural aerosol sources and processes. A better understanding of natural aerosol is key to constrain the preindustrial aerosol state and reduce the aerosol radiative forcing uncertainty in global climate models. We report the concentrations of gaseous sulfuric acid, iodic acid, and methanesulfonic acid (MSA) together with a characterization of new particle formation (NPF) events over a large stretch of the Southern Ocean. Measurements were conducted on board the Russian icebreaker Akademik Tryoshnikov from January to March 2017. Iodic acid is characterized by a particular diurnal cycle with reduced concentration around noon, suggesting a lower formation yield when solar irradiance is higher. Gaseous MSA does not have a diurnal cycle and measured concentrations in gas and condensed phase are compatible with this species being primarily produced via heterogeneous oxidation of dimethyl sulfide and subsequent partitioning into the gas phase. We also found that NPF in the boundary layer is mainly driven by sulfuric acid but it occurred very rarely over the vast geographical area probed and did not contribute to the cloud condensation nuclei budget in a directly observable manner. Despite the near absence of NPF events in the boundary layer, Aitken mode particles were frequently measured, supporting the hypothesis of a free tropospheric source. Iodic acid and MSA were not found to participate in nucleation, however, MSA may contribute to aerosol growth via heterogeneous formation in the aqueous phase.

Multi‐Source Data Based Investigation of Aerosol‐Cloud Interaction Over the North China Plain and North of the Yangtze Plain

AbstractAlthough great progress has been made in the study of aerosol‐cloud interactions (ACIs), representation of ACI is still the largest uncertainty in current weather and climate models. In this study, the ACI over the North China Plain and north of the Yangtze Plain is investigated using multi‐source data including satellite, ground‐based, reanalysis, and fusion data. The effects of aerosol characteristics (PM2.5, PM10, and PM2.5/PM10 ratio), atmospheric conditions including liquid water path (LWP) and lower tropospheric stability (LTS), and drizzle on ACI are analyzed. Results suggest that both aerosol characteristics and atmospheric conditions can affect cloud effective radius (), but drizzle in clouds plays a decisive role on ACI. In the case of fixed LTS and LWP, the average cloud droplet increases first and then fluctuates for all clouds examined with the increase of PM2.5 concentration. The PM2.5 concentration corresponding to the turning point of increases with the increasing LWP due to the increased chance of collision and coalescence. In contrast, the average cloud droplet and PM2.5 concentration show a negative correlation for clouds without drizzle, which is mainly caused by competition of water vapor. The quantitative ACI results show that the values of the aerosol first indirect effect are negative for most cases when considering all clouds examined, but positive when only considering clouds without drizzle.

Assessing Multi‐Stream Radiative Transfer Schemes for the Calculation of Aerosol Radiative Forcing in the Martian Atmosphere

AbstractThe atmosphere of Mars presents a strong response to aerosol radiative forcing compared to that of the Earth, thus atmospheric models should have accurate radiative transfer algorithms for the simulation of temperatures and circulation. In this work, we evaluate the radiative effects of dust aerosol calculated by different radiative transfer schemes and the influence of dust properties in these calculations. Two‐stream solutions were compared with multistream discrete ordinate methods. Fluxes and heating rates were evaluated for a variety of atmospheric conditions, including dust storms. The results show that in low‐to‐medium dust opacity scenarios, the highly efficient two‐stream methods provide accurate estimations, with heating rate errors of less than 2 K/sol. These errors increase with opacity, when differences of 20 K/sol are reached, which may be relevant in the simulations of temperature fields and atmospheric circulation under regional and global dust storm conditions. In such cases, the use of four‐stream or higher order methods may be required, although accuracy improvements for eight‐ or higher‐stream schemes were negligible. The influence of dust particle properties in aerosol radiative forcing estimations is mainly due to the particle size, where variations of 20% of the effective radius resulted in differences of 5 K/sol; the shape of the particles showed a minor impact, with differences of <2 K/sol. The results of this study contribute to quantification of the uncertainties in current Mars climate models and may help modellers to select the appropriate approach depending on the scenario.

Monomers of Glycine and Serine Have a Limited Ability to Hydrate in the Atmosphere.

The role of atmospheric aerosols on climate change is one of the biggest uncertainties in most global climate models. Organic aerosols have been identified as potential cloud condensation nuclei (CCN), and amino acids are organic molecules that could serve as CCN. Amino acids make up a significant portion of the total organic material in the atmosphere, and herein we present a systematic study of hydration for two of the most common atmospheric amino acids, glycine and serine. We compute DLPNO/CCSD(T)//M08-HX/MG3S thermodynamic properties and atmospheric concentrations of Gly(H2O)n and Ser(H2O)n, where n = 1-5. We predict that serine-water clusters have higher concentrations at n = 1 and 5, while glycine-water clusters have higher concentrations at n = 2-4. However, both glycine and serine are inferred to exist primarily in their nonhydrated monomer forms in the absence of other species such as sulfuric acid.

Southern Ocean latitudinal gradients of cloud condensation nuclei

Abstract. The Southern Ocean region is one of the most pristine in the world and serves as an important proxy for the pre-industrial atmosphere. Improving our understanding of the natural processes in this region is likely to result in the largest reductions in the uncertainty of climate and earth system models. While remoteness from anthropogenic and continental sources is responsible for its clean atmosphere, this also results in the dearth of atmospheric observations in the region. Here we present a statistical summary of the latitudinal gradient of aerosol (condensation nuclei larger than 10 nm, CN10) and cloud condensation nuclei (CCN at various supersaturations) concentrations obtained from five voyages spanning the Southern Ocean between Australia and Antarctica from late spring to early autumn (October to March) of the 2017/18 austral seasons. Three main regions of influence were identified: the northern sector (40–45∘ S), where continental and anthropogenic sources coexisted with background marine aerosol populations; the mid-latitude sector (45–65∘ S), where the aerosol populations reflected a mixture of biogenic and sea-salt aerosol; and the southern sector (65–70∘ S), south of the atmospheric polar front, where sea-salt aerosol concentrations were greatly reduced and aerosol populations were primarily biologically derived sulfur species with a significant history in the Antarctic free troposphere. The northern sector showed the highest number concentrations with median (25th to 75th percentiles) CN10 and CCN0.5 concentrations of 681 (388–839) cm−3 and 322 (105–443) cm−3, respectively. Concentrations in the mid-latitudes were typically around 350 cm−3 and 160 cm−3 for CN10 and CCN0.5, respectively. In the southern sector, concentrations rose markedly, reaching 447 (298–446) cm−3 and 232 (186–271) cm−3 for CN10 and CCN0.5, respectively. The aerosol composition in this sector was marked by a distinct drop in sea salt and increase in both sulfate fraction and absolute concentrations, resulting in a substantially higher CCN0.5/CN10 activation ratio of 0.8 compared to around 0.4 for mid-latitudes. Long-term measurements at land-based research stations surrounding the Southern Ocean were found to be good representations at their respective latitudes; however this study highlighted the need for more long-term measurements in the region. CCN observations at Cape Grim (40∘39′ S) corresponded with CCN measurements from northern and mid-latitude sectors, while CN10 observations only corresponded with observations from the northern sector. Measurements from a simultaneous 2-year campaign at Macquarie Island (54∘30′ S) were found to represent all aerosol species well. The southernmost latitudes differed significantly from both of these stations, and previous work suggests that Antarctic stations on the East Antarctic coastline do not represent the East Antarctic sea-ice latitudes well. Further measurements are needed to capture the long-term, seasonal and longitudinal variability in aerosol processes across the Southern Ocean.

Evaluation of a new 12\xa0km regional perturbed parameter ensemble over Europe

We evaluate a 12-member perturbed parameter ensemble of regional climate simulations over Europe at 12 km resolution, carried out as part of the UK Climate Projections (UKCP) project. This ensemble is formed by varying uncertain parameters within the model physics, allowing uncertainty in future projections due to climate modelling uncertainty to be explored in a systematic way. We focus on present day performance both compared to observations, and consistency with the driving global ensemble. Daily and seasonal temperature and precipitation are evaluated as two variables commonly used in impacts assessments. For precipitation we find that downscaling, even whilst within the convection-parameterised regime, generally improves daily precipitation, but not everywhere. In summer, the underestimation of dry day frequency is worse in the regional ensemble than in the driving simulations. For temperature we find that the regional ensemble inherits a large wintertime cold bias from the global model, however downscaling reduces this bias. The largest bias reduction is in daily winter cold temperature extremes. In summer the regional ensemble is cooler and wetter than the driving global models, and we examine cloud and radiation diagnostics to understand the causes of the differences. We also use a low-resolution regional simulation to determine whether the differences are a consequence of resolution, or due to other configuration differences, with the predominant configuration difference being the treatment of aerosols. We find that use of the EasyAerosol scheme in the regional model, which aims to approximate the aerosol effects in the driving model, causes reduced temperatures by around 0.5 K over Eastern Europe in Summer, and warming of a similar magnitude over France and Germany in Winter, relative to the impact of interactive aerosol in the global runs. Precipitation is also increased in these regions. Overall, we find that the regional model is consistent with the global model, but with a typically better representation of daily extremes and consequently we have higher confidence in its projections of their future change.

Future operation of hydropower in Europe under high renewable penetration and climate change

SummaryAs large renewable capacities penetrate the European energy system and the climate faces significant alterations, the future operation of hydropower reservoirs might deviate from today. In this work, we first analyze the changes in hydropower operation required to balance a wind- and solar-dominated European energy system. Second, we apply runoff data obtained from combining five different global circulation models and two regional climate models to estimate future reservoir inflow at three CO2 emissions scenarios (RCP2.6, RCP4.5, and RCP8.5). This enables us to address the climate model uncertainty reported in previous literature. Despite large interannual and intermodel variability, significant changes are measured in the climate model signal between today and future climate. Annual inflow decreases by 31% (20%) in Southern countries and increases by 21% (14%) in Northern countries for high (mid)-emission scenarios. Projections also show impacts on seasonal profiles and more frequent and prolonged droughts in Mediterranean countries.

Pessimism and Optimism in the Debate on Climate Change: A Critical Analysis

In the debate on climate change commentators often express pessimistic or optimistic views. We see this mainly in the media and popular literature, but also in various academic fields. The aim of this paper is to investigate different kinds of pessimistic and optimistic views put forward in this debate and suggest explanations of the diversity of views. The paper concludes that pessimism and optimism may concern, for example, climate change as an unmitigated or poorly mitigated process, mitigation of climate change or specific measures of mitigation. These aspects are important to distinguish, because a person can be pessimist concerning climate change as an unmitigated or poorly mitigated process and optimist concerning mitigation of climate change, and be pessimist concerning one specific mitigation measure and optimist concerning another. It is suggested that the diversity of pessimistic and optimistic views is due to the uncertainty of scientific climate models and the influence of evaluative and ideological assumptions.

Assessment of the uncertainties of global climate models in the evaluation of standardized precipitation and runoff indices: a case study

ABSTRACT Uncertainties in climate change projection can originate from various sources and cause challenges. Thus, two specific approaches were developed in this study, for use in the selection of global climate models and in the assessment of drought occurrence. Considering the bias-corrected data, the performance of global climate models was evaluated using statistical methods, and the 14 best-ranked models were selected. These climate scenarios were used in the Long Ashton Research Station (LARS) downscaling model to obtain the precipitation and temperature time series. Identification of unit Hydrographs And Component flows from Rainfall, Evaporation, and Streamflow (IHACRES) was used to model the runoff time series. Standardized precipitation and runoff indices were considered to assess the probability of meteorological and hydrological droughts. Finally, the Bayesian method was used to analyse the uncertainty assessment of drought occurrence. This methodology was applied in the Karkheh River basin and presented the moderate drought condition as the most probable state.

Prediction of Discharge Using Artificial Neural Network and IHACRES Models Due to Climate Change

Understanding of climate change and its impacts on river discharge has affected the quality and quantity of water and also supplying water requirements for drinking, agriculture and industry. Therefore, prediction of precipitation and temperature by climate models as well as simulation and optimization of their runoff with suitable models are very important. In this study, four climate models of the Fifth Coupled Model Inter comparison Project (CMIP5) and RCP8.5 scenario were used to forecast future precipitation and temperature for the next two periods including 2020-2052 and 2053-2085. Mean Observed Temperature-Precipitation (MOTP) method was used to reduce the uncertainty of climate models and the change factor method was used to downscale the climate data. Then, the Lumped-conceptual Identification of unit Hydrographs and Component flows from Rainfall, Evaporation and Stream flow data (IHACRES) model and multi-layer Artificial Neural Network (ANN) model were employed to estimate the effects of these parameters on the Khorramrood River runoff. The neural network model is written and implemented using Scikit-Learn library and the Python programming language. The comparison of performance of ANN models with different input variables like monthly precipitation, monthly precipitation of previous months, monthly discharge, monthly discharge of previous months, monthly temperature was made to find the best and most efficient network structure. The results showed that the precipitation in Khorramrood River basin based on the weighted combination model decreased by 8.18 % and 9.75 % in the first and the second periods, respectively, while the temperature increased by 1.85 and 4.22 °C, respectively. The discharge parameter in the calibration and validation period in the IHACRES model based on criteria to evaluate the parameters of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), The Coefficient of Determination (R), and the Nash-Sutcliffe Efficiency (NSE) performed better than the artificial neural network model. However, due to the small differences of these changes, the predictions were performed for both periods and using both models and the results indicated that future discharge in the IHACRES model decreased by 12.72 % during the first period and by 20.3 % in the second period, while the model of artificial neural network showed decrease rates of 2.12 % and 6.97 %, respectively.