scholarly journals Ubiquitous atmospheric production of organic acids mediated by cloud droplets

Nature ◽  
2021 ◽  
Vol 593 (7858) ◽  
pp. 233-237
Author(s):  
B. Franco ◽  
T. Blumenstock ◽  
C. Cho ◽  
L. Clarisse ◽  
C. Clerbaux ◽  
...  
Keyword(s):  
Organic Acids ◽  
Formic Acid ◽  
Climate Models ◽  
Climate Model ◽  
Cloud Droplets ◽  
Sources And Sinks ◽  
Warm Cloud ◽  
Model Predictions ◽  
Gas Phase Oxidation ◽  
Cloud Evolution

AbstractAtmospheric acidity is increasingly determined by carbon dioxide and organic acids1–3. Among the latter, formic acid facilitates the nucleation of cloud droplets4 and contributes to the acidity of clouds and rainwater1,5. At present, chemistry–climate models greatly underestimate the atmospheric burden of formic acid, because key processes related to its sources and sinks remain poorly understood2,6–9. Here we present atmospheric chamber experiments that show that formaldehyde is efficiently converted to gaseous formic acid via a multiphase pathway that involves its hydrated form, methanediol. In warm cloud droplets, methanediol undergoes fast outgassing but slow dehydration. Using a chemistry–climate model, we estimate that the gas-phase oxidation of methanediol produces up to four times more formic acid than all other known chemical sources combined. Our findings reconcile model predictions and measurements of formic acid abundance. The additional formic acid burden increases atmospheric acidity by reducing the pH of clouds and rainwater by up to 0.3. The diol mechanism presented here probably applies to other aldehydes and may help to explain the high atmospheric levels of other organic acids that affect aerosol growth and cloud evolution.

scholarly journals Why do GCMs overestimate the aerosol cloud lifetime effect? A comparison of CAM5 and a CRM

2016 ◽  
Author(s):  
Cheng Zhou ◽  
Joyce E. Penner
Keyword(s):  
Great Plains ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Cloud Droplet ◽  
Condensation Process ◽  
Cloud Droplets ◽  
Aerosol Cloud ◽  
Aerosol Loading

Abstract. Observation-based studies have shown that the aerosol cloud lifetime effect or the increase of cloud liquid water (LWP) with increased aerosol loading may have been overestimated in climate models. Here, we simulate shallow warm clouds on 05/27/2011 at the Southern Great Plains (SGP) measurement site established by Department of Energy's Atmospheric Radiation Measurement (ARM) Program using a single column version of a global climate model (CAM5.3) and a cloud resolving model (CRM). The LWP simulated by CAM increases substantially with aerosol loading while that in the CRM does not. The increase of LWP in CAM is caused by a large decrease of the autoconversion rate when cloud droplet number increases. In the CRM, the autoconversion rate is also reduced, but this is offset or even outweighed by the increased evaporation of cloud droplets near cloud top, resulting in an overall decrease in LWP. Our results suggest that climate models need to include the dependence of cloud top growth and the evaporation/condensation process on cloud droplet number concentrations.

scholarly journals The Role of Irrigation Expansion in Past and Future Temperature Trends

2008 ◽  
Vol 12 (3) ◽  
pp. 1-11 ◽  
Author(s):  
David B. Lobell ◽  
Céline Bonfils ◽  
Jean-Marc Faurès
Keyword(s):  
Greenhouse Gases ◽  
Climate Models ◽  
Climate Model ◽  
Masking Effect ◽  
Climate Trends ◽  
Local Cooling ◽  
Temperature Trends ◽  
Model Predictions ◽  
Cooling Effects ◽  
Climate Model Simulations

Abstract Expansion of irrigated land can cause local cooling of daytime temperatures by up to several degrees Celsius. Here the authors compare the expected cooling associated with rates of irrigation expansion in developing countries for historical (1961–2000) and future (2000–30) periods with climate model predictions of temperature changes from other forcings, most notably increased atmospheric greenhouse gas levels, over the same periods. Indirect effects of irrigation on climate, via methane production in paddy rice systems, were not considered. In regions of rapid irrigation growth over the past 40 yr, such as northwestern India and northeastern China, irrigation’s expected cooling effects have been similar in magnitude to climate model predictions of warming from greenhouse gases. A masking effect of irrigation can therefore explain the lack of significant increases in observed growing season maximum temperatures in these regions and the apparent discrepancy between observations and climate model simulations. Projections of irrigation for 2000–30 indicate a slowing of expansion rates, and therefore cooling from irrigation expansion over this time period will very likely be smaller than in recent decades. At the same time, warming from greenhouse gases will likely accelerate, and irrigation will play a relatively smaller role in agricultural climate trends. In many irrigated regions, therefore, temperature projections from climate models, which generally ignore irrigation, may be more accurate in predicting future temperature trends than their performance in reproducing past observed trends in irrigated regions would suggest.

scholarly journals Climate Cloud Model Forecast Verification-an Engineering Perspective

2020 ◽  
Vol 1 (4) ◽  
Author(s):  
Keith D Hutchison ◽  
◽  
Barbara Iisager ◽  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Cloud Model ◽  
Global Climate ◽  
Model Performance ◽  
Forecast Model ◽  
System Level ◽  
Model Predictions ◽  
Cloud Forecast ◽  
Minimal Effort

The processes relevant to the verification of cloud forecasts generated by climate models are discussed from an engineering perspective. These processes include an assessment of cloud product requirements to be evaluated, the creation of a verification test plan including procedures and data to be analyzed, the development of independent sources of validation or truth datasets, and the quantitative comparisons between the cloud forecast products and the truth data needed to establish model performance. The engineering perspective means minimal effort is focused on assessing the veracity of the physics contained in the cloud forecast model, rather emphasis is upon evaluating the results produced by it. It is postulated that these procedures are critical to improve the reliability of climate model predictions. The World Meteorological Organization has stated accuracy requirements for cloud products created from satellite observations, through the Global Climate Observing System (GSOC) program; however, no similar requirements have been defined for cloud forecast products. A statement of accuracy requirements is urgently needed. Meanwhile, it is assumed herein that cloud observation and cloud forecast requirements are identical. The assessment of model performance exploits high quality, manually-generated cloud truth products created from remotely-sensed satellite data which serve as truth data. Results show clouds under-specified in reanalysis cloud datasets created for use to initialize climate models but an over-specification of clouds by the cloud forecast model, in short-range predictions. This system level analysis demonstrates the need to improve the accuracy of cloud forecasts, especially lower-level water clouds which are responsible for most of the uncertainty in climate model predictions.

scholarly journals Metamodeling of Droplet Activation for Global Climate Models

2016 ◽  
Vol 73 (3) ◽  
pp. 1255-1272 ◽  
Author(s):  
Daniel Rothenberg ◽  
Chien Wang
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Models ◽  
Lookup Table ◽  
Computational Time ◽  
Droplet Growth ◽  
Cloud Droplets ◽  
Time To Build ◽  
Physically Based

Abstract The nucleation of cloud droplets from the ambient aerosol is a critical physical process that must be resolved for global models to faithfully predict aerosol–cloud interactions and aerosol indirect effects on climate. To better represent droplet nucleation from a complex, multimodal, and multicomponent aerosol population within the context of a global model, a new metamodeling framework is applied to derive an efficient and accurate activation parameterization. The framework applies polynomial chaos expansion to a detailed parcel model in order to derive an emulator that maps thermodynamic and aerosol parameters to the supersaturation maximum achieved in an adiabatically ascending parcel and can be used to diagnose droplet number from a single lognormal aerosol mode. The emulator requires much less computational time to build, store, and evaluate than a high-dimensional lookup table. Compared to large sample sets from the detailed parcel model, the relative error in the predicted supersaturation maximum and activated droplet number computed with the best emulator is and (one standard deviation), respectively. On average, the emulators constructed here are as accurate and between 10 and 17 times faster than a leading physically based activation parameterization. Because the underlying parcel model being emulated resolves size-dependent droplet growth factors, the emulator captures kinetic limitations on activation. The results discussed in this work suggest that this metamodeling framework can be extended to accurately account for the detailed activation of a complex aerosol population in an arbitrary coupled global aerosol–climate model.

scholarly journals « Idol with feet of clay »: reliable predictions of forest ecosystem functioning require flawless climate forcings

2021 ◽  
Author(s):  
M. Jourdan ◽  
C. François ◽  
N. Delpierre ◽  
N. Martin St-Paul ◽  
E. Dufrêne
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Impact Model ◽  
Climate Data ◽  
Large Spatial Scale ◽  
Large Variability ◽  
Model Predictions ◽  
Climate Forcings ◽  
The Impact ◽  
Impact Models

AbstractClimate change affects various aspects of the functioning of ecosystem, especially photosynthesis, respiration and carbon storage. We need accurate modelling approaches (impact models) to simulate the functioning, vitality and provision of ecosystem services of forests in a warmer world. These impact models require climate data as forcings, which are often produced by climate models comparing more or less well with observational climate data. The bias percentage of the climate forcings propagates throughout the modeling chain from the climate model to the impact model.In this study, we aimed to quantify these bias percentage, addressing three questions: (1) Do the impact model predictions vary when forcing it with different climate models, and how do the predictions under climate model vs. observational climate forcing differ? (2) Does the variability in the impact climate simulations caused by climate forcings fade out at large spatial scale? (3) How the fact of using simulated climatic data affects the process-based model predictions in the case of stressful events?To answer these questions, we present results obtained over the historical period (e.g. 1970-2010) with the CASTANEA ecophysiological forest model and use the data from three climate models. Our analysis focuses on French forests, studying European beech (Fagus sylvatica), temperate deciduous oaks (Quercus robur and Q. petraea), Scots pine (Pinus sylvestris) and spruce (Picea abies) monospecific stands.We show that prediction of photosynthesis, respiration and wood growth highly depends on the climate model used, whether debiased or not, and also on species and region considered. Overall, we observed an improvement of prediction after a monthly mean bias or monthly quantile mapping correction for three model considered, but not with the same success. Then we highlighted a large variability in the processes simulated by the impact model under different climate forcings when considering the plot (i.e. scale of a few hectares) scale. This variability fades out at larger scale (e.g. the scale of an ecological region, i.e. 100 km2), owing to an aggregation effect. Moreover, process predictions obtained under different climate forcings are more variable during driest years. These results highlight the necessity to quantify bias and uncertainties in climate forcings before predicting fluxes dynamics with process-based model.

scholarly journals Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods

2021 ◽  
Vol 18 (4) ◽  
pp. 1992
Author(s):  
Weijia Qian ◽  
Howard H. Chang
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Climate Science ◽  
Regional Climate Models ◽  
Health Impact ◽  
Quantile Mapping ◽  
Ed Visits

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.

scholarly journals Sensitivity of projected climate impacts to climate model weighting: multi-sector analysis in eastern Africa

2021 ◽  
Vol 164 (3-4) ◽  
Author(s):  
Seshagiri Rao Kolusu ◽  
Christian Siderius ◽  
Martin C. Todd ◽  
Ajay Bhave ◽  
Declan Conway ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Investment Decisions ◽  
Climate Impacts ◽  
Future Climate ◽  
Eastern Africa ◽  
Climate Projections ◽  
Systematic Effect ◽  
Risk Profiles ◽  
Model Weighting

AbstractUncertainty in long-term projections of future climate can be substantial and presents a major challenge to climate change adaptation planning. This is especially so for projections of future precipitation in most tropical regions, at the spatial scale of many adaptation decisions in water-related sectors. Attempts have been made to constrain the uncertainty in climate projections, based on the recognised premise that not all of the climate models openly available perform equally well. However, there is no agreed ‘good practice’ on how to weight climate models. Nor is it clear to what extent model weighting can constrain uncertainty in decision-relevant climate quantities. We address this challenge, for climate projection information relevant to ‘high stakes’ investment decisions across the ‘water-energy-food’ sectors, using two case-study river basins in Tanzania and Malawi. We compare future climate risk profiles of simple decision-relevant indicators for water-related sectors, derived using hydrological and water resources models, which are driven by an ensemble of future climate model projections. In generating these ensembles, we implement a range of climate model weighting approaches, based on context-relevant climate model performance metrics and assessment. Our case-specific results show the various model weighting approaches have limited systematic effect on the spread of risk profiles. Sensitivity to climate model weighting is lower than overall uncertainty and is considerably less than the uncertainty resulting from bias correction methodologies. However, some of the more subtle effects on sectoral risk profiles from the more ‘aggressive’ model weighting approaches could be important to investment decisions depending on the decision context. For application, model weighting is justified in principle, but a credible approach should be very carefully designed and rooted in robust understanding of relevant physical processes to formulate appropriate metrics.

scholarly journals Observations and Simulations of Meteorological Conditions over Arctic Thick Sea Ice in Late Winter during the Transarktika 2019 Expedition

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 174
Author(s):  
Günther Heinemann ◽  
Sascha Willmes ◽  
Lukas Schefczyk ◽  
Alexander Makshtas ◽  
Vasilii Kustov ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Open Water ◽  
The Arctic ◽  
Late Winter ◽  
Good Representation ◽  
Hourly Temperature ◽  
Ice Model

The parameterization of ocean/sea-ice/atmosphere interaction processes is a challenge for regional climate models (RCMs) of the Arctic, particularly for wintertime conditions, when small fractions of thin ice or open water cause strong modifications of the boundary layer. Thus, the treatment of sea ice and sub-grid flux parameterizations in RCMs is of crucial importance. However, verification data sets over sea ice for wintertime conditions are rare. In the present paper, data of the ship-based experiment Transarktika 2019 during the end of the Arctic winter for thick one-year ice conditions are presented. The data are used for the verification of the regional climate model COSMO-CLM (CCLM). In addition, Moderate Resolution Imaging Spectroradiometer (MODIS) data are used for the comparison of ice surface temperature (IST) simulations of the CCLM sea ice model. CCLM is used in a forecast mode (nested in ERA5) for the Norwegian and Barents Seas with 5 km resolution and is run with different configurations of the sea ice model and sub-grid flux parameterizations. The use of a new set of parameterizations yields improved results for the comparisons with in-situ data. Comparisons with MODIS IST allow for a verification over large areas and show also a good performance of CCLM. The comparison with twice-daily radiosonde ascents during Transarktika 2019, hourly microwave water vapor measurements of first 5 km in the atmosphere and hourly temperature profiler data show a very good representation of the temperature, humidity and wind structure of the whole troposphere for CCLM.

scholarly journals Simulations of the Mid-Pliocene Warm Period using the NASA/GISS ModelE2-R Earth System Model

2012 ◽  
Vol 5 (3) ◽  
pp. 2811-2842 ◽  
Author(s):  
M. A. Chandler ◽  
L. E. Sohl ◽  
J. A. Jonas ◽  
H. J. Dowsett
Keyword(s):  
North Atlantic ◽  
Climate Models ◽  
Climate Model ◽  
Geographic Region ◽  
Earth System Model ◽  
Warm Period ◽  
System Model ◽  
Earth System ◽  
Intergovernmental Panel ◽  
Future Global Warming

Abstract. Climate reconstructions of the mid-Pliocene Warm Period (mPWP) bear many similarities to aspects of future global warming as projected by the Intergovernmental Panel on Climate Change. In particular, marine and terrestrial paleoclimate data point to high latitude temperature amplification, with associated decreases in sea ice and land ice and altered vegetation distributions that show expansion of warmer climate biomes into higher latitudes. NASA GISS climate models have been used to study the Pliocene climate since the USGS PRISM project first identified that the mid-Pliocene North Atlantic sea surface temperatures were anomalously warm. Here we present the most recent simulations of the Pliocene using the AR5/CMIP5 version of the GISS Earth System Model known as ModelE2-R. These simulations constitute the NASA contribution to the Pliocene Model Intercomparison Project (PlioMIP) Experiment 2. Many findings presented here corroborate results from other PlioMIP multi-model ensemble papers, but we also emphasize features in the ModelE2-R simulations that are unlike the ensemble means. We provide discussion of features that show considerable improvement compared with simulations from previous versions of the NASA GISS models, improvement defined here as simulation results that more closely resemble the ocean core data as well as the PRISM3D reconstructions of the mid-Pliocene climate. In some regions even qualitative agreement between model results and paleodata are an improvement over past studies, but the dramatic warming in the North Atlantic and Greenland-Iceland-Norwegian Sea in these new simulations is by far the most accurate portrayal ever of this key geographic region by the GISS climate model. Our belief is that continued development of key physical routines in the atmospheric model, along with higher resolution and recent corrections to mixing parameterizations in the ocean model, have led to an Earth System Model that will produce more accurate projections of future climate.

scholarly journals Transferability Intercomparison: An Opportunity for New Insight on the Global Water Cycle and Energy Budget

2007 ◽  
Vol 88 (3) ◽  
pp. 375-384 ◽  
Author(s):  
E. S. Takle ◽  
J. Roads ◽  
B. Rockel ◽  
W. J. Gutowski ◽  
R. W. Arritt ◽  
...  
Keyword(s):  
Energy Budget ◽  
Climate Models ◽  
Climate Model ◽  
Water Cycle ◽  
Regional Climate ◽  
Climate Conditions ◽  
Continental Scale ◽  
Global Water Cycle ◽  
Wide Range ◽  
Global Water

A new approach, called transferability intercomparisons, is described for advancing both understanding and modeling of the global water cycle and energy budget. Under this approach, individual regional climate models perform simulations with all modeling parameters and parameterizations held constant over a specific period on several prescribed domains representing different climatic regions. The transferability framework goes beyond previous regional climate model intercomparisons to provide a global method for testing and improving model parameterizations by constraining the simulations within analyzed boundaries for several domains. Transferability intercomparisons expose the limits of our current regional modeling capacity by examining model accuracy on a wide range of climate conditions and realizations. Intercomparison of these individual model experiments provides a means for evaluating strengths and weaknesses of models outside their “home domains” (domain of development and testing). Reference sites that are conducting coordinated measurements under the continental-scale experiments under the Global Energy and Water Cycle Experiment (GEWEX) Hydrometeorology Panel provide data for evaluation of model abilities to simulate specific features of the water and energy cycles. A systematic intercomparison across models and domains more clearly exposes collective biases in the modeling process. By isolating particular regions and processes, regional model transferability intercomparisons can more effectively explore the spatial and temporal heterogeneity of predictability. A general improvement of model ability to simulate diverse climates will provide more confidence that models used for future climate scenarios might be able to simulate conditions on a particular domain that are beyond the range of previously observed climates.

Sign in / Sign up

Export Citation Format

Share Document