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  • 1
    Online Resource
    Online Resource
    Cham :Springer International Publishing AG,
    Keywords: Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (396 pages)
    Edition: 1st ed.
    ISBN: 9783319772738
    Series Statement: Space Sciences Series of ISSI Series ; v.65
    Language: English
    Note: Intro -- Contents -- Preface to the Special Issue ''ISSI Workshop on Shallow Clouds and Water Vapor, Circulation and Climate Sensitivity'' -- 1 Convective Self-Aggregation in Numerical Simulations: A Review -- 1 Introduction -- 2 Characteristics of Self-Aggregation -- 2.1 General Evolution of Aggregation -- 2.2 Identifying Metrics -- 2.3 Time Scale -- 2.4 Length Scale -- 2.5 Impacts -- 3 Mechanisms of Self-Aggregation -- 3.1 Surface Fluxes -- 3.1.1 Sensitivity to SST -- 3.2 Longwave Radiation -- 3.2.1 Sensitivity to SST -- 3.3 Shortwave Radiation -- 3.3.1 Sensitivity to SST -- 3.4 Advective Processes -- 3.5 Moisture Feedbacks -- 3.6 Triggering Versus Maintenance -- 4 Importance of Self-Aggregation -- 5 Conclusions -- 5.1 What Aspects of Self-Aggregation do Modeling Studies Agree on? -- 5.2 What Remains Uncertain? -- 5.3 What Could be Explored More? -- 5.4 Synthesis -- References -- 2 Observing Convective Aggregation -- 1 Introduction -- 1.1 Importance of Aggregation -- 1.2 Literature Review: Observational Studies of Convective Organization -- 2 Observational Perspectives on Processes Important for Idealized Convective Aggregation -- 2.1 Metrics to Quantify Feedbacks -- 2.2 Initiation Processes -- 2.2.1 Longwave Radiation -- 2.2.2 Surface Fluxes -- 2.2.3 Shortwave Radiation -- 2.2.4 Moisture-Convection Feedbacks -- 2.3 Sensitivity to SST -- 2.4 Maintenance Processes -- 3 Comparing the Idealized World to the Natural World -- 3.1 Time Scales of Self-Aggregation -- 3.2 Mean Wind and Wind Shear -- 3.3 Humidity Profiles -- 3.4 Equatorial Wave Dynamics -- 3.5 Ocean Interaction and Feedback -- 4 Observational Perspectives on Aggregation in a Warming Climate -- 5 Future Observational Aspirations -- 5.1 Evolution of Convective Organization Using Satellite Data -- 5.2 Spaceborne Cloud Radar Approaches. , 5.3 Feasibility of a Ground-Based Observational Network -- 6 Conclusions -- References -- 3 An Observational View of Relationships Between Moisture Aggregation, Cloud, and Radiative Heating Profiles -- 1 Introduction -- 2 Characterizing Aggregation in Clouds and Their Environment -- 2.1 Observations from the A-Train -- 2.2 Characterizing Aggregation in the Water Vapor Field -- 3 Relationships Among Clouds, Humidity, and Aggregation -- 3.1 Cloudiness Depends on Sea Surface Temperature and Aggregation State -- 3.2 Cloudiness, Radiative Heating, and Convective Intensity -- 3.3 Does Vapor Aggregation Imply Convective Aggregation? -- 4 Summary and Discussion -- References -- 4 Correction to: An Observational View of Relationships Between Moisture Aggregation, Cloud, and Radiative Heating Profiles -- 5 Implications of Warm Rain in Shallow Cumulus and Congestus Clouds for Large-Scale Circulations -- 1 Introduction -- 2 Warm Rain in Large-Scale Circulations -- 2.1 A Two-Column RCE Model -- 2.2 Circulating Equilibria in the Two-Column System -- 2.3 Shallow Cumulus -- 2.4 Congestus -- 3 From a Conceptual Model to Nature -- 3.1 Large-Eddy Simulations -- 3.2 Observations -- 4 Concluding Thoughts -- References -- 6 A Survey of Precipitation-Induced Atmospheric Cold Pools over Oceans and Their Interactions with the Larger-Scale Environment -- 1 Introduction -- 2 Cold Pools from Boundary Layers not Exceeding 2 km Altitude -- 3 Cold Pools from Convection Reaching the Mid-Troposphere -- 4 Cold Pools from Deep Tropical Convection -- 5 Remaining Questions -- 5.1 The Relationship of Trade Wind Cold Pools to Cloud Cover -- 5.2 Thermodynamic Secondary Initiation Processes -- References -- 7 Low-Cloud Feedbacks from Cloud-Controlling Factors: A Review -- 1 Seeking Observational Constraints on Low-Cloud Feedbacks -- 2 Cloud-Controlling Factors -- 3 Low-Cloud Feedbacks. , 4 Implications for Climate Sensitivity -- 5 Sources of Uncertainty -- 5.1 Fundamental Issues -- 5.1.1 F1. Are Cloud Sensitivities Time-scale Invariant? -- 5.1.2 F2. Are Clouds Responding to the Controlling Factors? -- 5.1.3 F3. Uncertainty in the Climate Change Prediction of Cloud-Controlling -- Factors -- 5.1.4 F4. Time-Dependency of Cloud-Controlling Factors During a Climate Change -- 5.2 Implementation Issues -- 5.2.1 I1. Imperfect Observations of Clouds and Their Controlling Factors -- 5.2.2 I2. Limited Duration of the Observational Record -- 5.2.3 I3. Limited Spatial Sampling of the Observations -- 5.2.4 I4. Imprecise Statistical Modeling -- 5.2.5 I5. Incomplete Set of Cloud-Controlling Factors -- 6 Summary and Final Remarks -- References -- 8 Mechanisms and Model Diversity of Trade-Wind Shallow Cumulus Cloud Feedbacks: A Review -- 1 Introduction -- 2 Interpreting Model Differences in Trade-Wind Cloud Responses to Warming in General Circulation Models -- 2.1 Boundary-Layer Moisture Budget -- 2.2 The Role of Shallow Convective Mixing -- 3 A Mass Budget Perspective on Cloud-Base Cloud Fraction -- 4 High-Resolution Simulation of Shallow Cumulus Cloud Changes and Mechanisms -- 4.1 Trade-Wind Shallow Cumulus Cloud Response to Warming in LES -- 4.1.1 The Role of Precipitation -- 4.1.2 The Role of Organization -- 4.2 Robustness and Uncertainties of LES Studies -- 5 Connecting LES and GCM Interpretations of Shallow Cumulus Cloud Feedback Mechanisms -- 6 Observational Support for Trade-Wind Shallow Cumulus Cloud Feedbacks -- 7 Synthesis -- References -- 9 Importance Profiles for Water Vapor -- 1 Introduction -- 2 Simplest: The Profile of -- Mass Errors from Vapor Measurement Errors -- 3 Radiative Kernels: Sensitivity of OLR to Humidity -- 4 Importance of Vapor for Deep Convection -- 5 Importance Functions for Passive Remote Sensing. , 6 Summary and Conclusions -- References -- 10 Structure and Dynamical Influence of Water Vapor in the Lower Tropical Troposphere -- 1 Introduction -- 2 Data and Context -- 2.1 Airborne Measurements and the Barbados Cloud Observatory -- 2.1.1 Dropsonde Humidity Measurements During the NARVAL Campaigns -- 2.1.2 WALES -- 2.2 SAPHIR and Megha-Tropiques -- 2.3 Infrared Atmospheric Sounding Interferometer (IASI) -- 3 How Lower-Tropospheric Humidity Influences Clouds, Convection and Circulation -- 3.1 Humidity in the Planetary Boundary Layer -- 3.2 Column Water Vapor (Thermodynamic Effects) -- 3.3 Column Water Vapor (radiative effects) -- 3.4 Elevated Moist Layers -- 4 Remotely Sensed Humidity Variations During NARVAL-1 and NARVAL-2 -- 4.1 General Structure of Humidity Retrievals from SAPHIR -- 4.2 Evaluation of SAPHIR Retrievals by WALES -- 4.3 Comparison with IASI and the Added Value of Retrieving Isotopologues -- 5 A Hypothesis for the Preponderance of Melting Level Convection -- 6 Conclusions -- References -- 11 The Representation of Tropospheric Water Vapor Over Low-Latitude Oceans in (Re-)analysis: Errors, Impacts, and the Ability to Exploit Current and Prospective Observations -- 1 Tropospheric Water Vapor Over Low-Latitude Oceans -- 2 Integrating Observations in Space and Time: Data Assimilation and (Re-)analysis -- 2.1 Producing Meteorological (Re-)analyses -- 2.1.1 Global Forecast Models -- 2.1.2 Data Assimilation Systems -- 2.1.3 Challenges in Assimilating Moisture -- 2.2 Analysis and Reanalysis -- 3 What Measurements Inform Current Estimates? -- 3.1 Microwave and Infrared Sounding -- 3.1.1 Principles of Measurement -- 3.1.2 Why Both Microwave and Infrared Observations are Useful -- 3.1.3 Prospects -- 3.2 Estimates of Precipitable Water from Microwave Observations -- 3.3 GNSS Radio Occultation: A Global Refractometer. , 3.3.1 Principles of Measurement -- 3.3.2 Vertical Resolution, Accuracy, and Limitations -- 3.3.3 Prospects -- 4 Errors in Water Vapor Distributions and the Resulting Impacts -- 4.1 Assessing Errors in the Analyzed Distribution of Water Vapor -- 4.1.1 Assessment in Subsiding Regions -- 4.1.2 Assessment in Convecting Regions -- 4.2 Assessing Impacts -- 5 Characterizing Water Vapor in a More Richly Observed World -- 5.1 Limited Observations and Model Error -- 5.2 Exploiting Richer Observations -- References -- 12 Airborne Lidar Observations of Water Vapor Variability in Tropical Shallow Convective Environment -- 1 Introduction -- 2 The DLR Airborne Water Vapor Lidar -- 3 The Meteosat Images -- 4 Results -- 4.1 Overview -- 4.2 Strong Heterogeneity in the Cloud Layer -- 4.3 Dry Regions in Both Cloud and Sub-cloud Layers -- 4.4 Transport of Moisture Through the Cloud Layer by Shallow Convection -- 4.5 Mean, Variance and Skewness Profiles of Water Vapor -- 5 Conclusions and Outlook -- References -- 13 Emerging Technologies and Synergies for Airborne and Space-Based Measurements of Water Vapor Profiles -- 1 Introduction -- 2 Differential Absorption Lidar -- 2.1 Measurement Capabilities -- 2.2 Technology Readiness -- 3 Differential Absorption Radar -- 3.1 Measurement Capabilities -- 3.2 Technology Readiness -- 4 Microwave Occultation -- 4.1 Measurement Approach -- 4.2 Measurement Capabilities -- 4.3 Technology Readiness -- 5 Hyperspectral Microwave -- 5.1 Measurement Capabilities -- 5.2 Technology Readiness -- 6 Synergies of Observing Systems -- 6.1 Value of Ground-Based Profiling -- 6.2 Examples of Synergetic Applications -- 6.2.1 Combining Lidar and Microwave Radiometer -- 6.2.2 Combining Satellite and Ground-Based Observations -- 6.3 Future Perspectives -- 7 Summary and Conclusions -- References. , 14 Observational Constraints on Cloud Feedbacks: The Role of Active Satellite Sensors.
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  • 2
    Publication Date: 2022-05-27
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Quinn, P. K., Thompson, E. J., Coffman, D. J., Baidar, S., Bariteau, L., Bates, T. S., Bigorre, S., Brewer, A., de Boer, G., de Szoeke, S. P., Drushka, K., Foltz, G. R., Intrieri, J., Iyer, S., Fairall, C. W., Gaston, C. J., Jansen, F., Johnson, J. E., Krueger, O. O., Marchbanks, R. D., Moran, K. P., Noone, D., Pezoa, S., Pincus, R., Plueddemann, A. J., Poehlker, M. L., Poeschl, U., Melendez, E. Q., Royer, H. M., Szczodrak, M., Thomson, J., Upchurch, L. M., Zhang, C., Zhang, D., & Zuidema, P. Measurements from the RV Ronald H. Brown and related platforms as part of the Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC). Earth System Science Data, 13(4), (2021): 1759-1790, https://doi.org/10.5194/essd-13-1759-2021.
    Description: The Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) took place from 7 January to 11 July 2020 in the tropical North Atlantic between the eastern edge of Barbados and 51∘ W, the longitude of the Northwest Tropical Atlantic Station (NTAS) mooring. Measurements were made to gather information on shallow atmospheric convection, the effects of aerosols and clouds on the ocean surface energy budget, and mesoscale oceanic processes. Multiple platforms were deployed during ATOMIC including the NOAA RV Ronald H. Brown (RHB) (7 January to 13 February) and WP-3D Orion (P-3) aircraft (17 January to 10 February), the University of Colorado's Robust Autonomous Aerial Vehicle-Endurant Nimble (RAAVEN) uncrewed aerial system (UAS) (24 January to 15 February), NOAA- and NASA-sponsored Saildrones (12 January to 11 July), and Surface Velocity Program Salinity (SVPS) surface ocean drifters (23 January to 29 April). The RV Ronald H. Brown conducted in situ and remote sensing measurements of oceanic and atmospheric properties with an emphasis on mesoscale oceanic–atmospheric coupling and aerosol–cloud interactions. In addition, the ship served as a launching pad for Wave Gliders, Surface Wave Instrument Floats with Tracking (SWIFTs), and radiosondes. Details of measurements made from the RV Ronald H. Brown, ship-deployed assets, and other platforms closely coordinated with the ship during ATOMIC are provided here. These platforms include Saildrone 1064 and the RAAVEN UAS as well as the Barbados Cloud Observatory (BCO) and Barbados Atmospheric Chemistry Observatory (BACO). Inter-platform comparisons are presented to assess consistency in the data sets. Data sets from the RV Ronald H. Brown and deployed assets have been quality controlled and are publicly available at NOAA's National Centers for Environmental Information (NCEI) data archive (https://www.ncei.noaa.gov/archive/accession/ATOMIC-2020, last access: 2 April 2021). Point-of-contact information and links to individual data sets with digital object identifiers (DOIs) are provided herein.
    Description: NOAA's Climate Variability and Predictability Program provided funding under NOAA CVP NA19OAR4310379, GC19-301, and GC19-305. The Joint Institute for the Study of the Atmosphere and Ocean (JISAO) supported this study under NOAA cooperative agreement NA15OAR4320063. Additional support was provided by the NOAA's Uncrewed Aircraft Systems (UAS) Program Office, NOAA's Physical Sciences Laboratory, and NOAA AOML's Physical Oceanography Division. The NTAS project is funded by the NOAA's Global Ocean Monitoring and Observing Program (CPO FundRef number 100007298), through the Cooperative Institute for the North Atlantic Region (CINAR) under cooperative agreement NA14OAR4320158.
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 3
    Publication Date: 2022-05-27
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Stevens, B., Bony, S., Farrell, D., Ament, F., Blyth, A., Fairall, C., Karstensen, J., Quinn, P. K., Speich, S., Acquistapace, C., Aemisegger, F., Albright, A. L., Bellenger, H., Bodenschatz, E., Caesar, K.-A., Chewitt-Lucas, R., de Boer, G., Delanoë, J., Denby, L., Ewald, F., Fildier, B., Forde, M., George, G., Gross, S., Hagen, M., Hausold, A., Heywood, K. J., Hirsch, L., Jacob, M., Jansen, F., Kinne, S., Klocke, D., Kölling, T., Konow, H., Lothon, M., Mohr, W., Naumann, A. K., Nuijens, L., Olivier, L., Pincus, R., Pöhlker, M., Reverdin, G., Roberts, G., Schnitt, S., Schulz, H., Siebesma, A. P., Stephan, C. C., Sullivan, P., Touzé-Peiffer, L., Vial, J., Vogel, R., Zuidema, P., Alexander, N., Alves, L., Arixi, S., Asmath, H., Bagheri, G., Baier, K., Bailey, A., Baranowski, D., Baron, A., Barrau, S., Barrett, P. A., Batier, F., Behrendt, A., Bendinger, A., Beucher, F., Bigorre, S., Blades, E., Blossey, P., Bock, O., Böing, S., Bosser, P., Bourras, D., Bouruet-Aubertot, P., Bower, K., Branellec, P., Branger, H., Brennek, M., Brewer, A., Brilouet , P.-E., Brügmann, B., Buehler, S. A., Burke, E., Burton, R., Calmer, R., Canonici, J.-C., Carton, X., Cato Jr., G., Charles, J. A., Chazette, P., Chen, Y., Chilinski, M. T., Choularton, T., Chuang, P., Clarke, S., Coe, H., Cornet, C., Coutris, P., Couvreux, F., Crewell, S., Cronin, T., Cui, Z., Cuypers, Y., Daley, A., Damerell, G. M., Dauhut, T., Deneke, H., Desbios, J.-P., Dörner, S., Donner, S., Douet, V., Drushka, K., Dütsch, M., Ehrlich, A., Emanuel, K., Emmanouilidis, A., Etienne, J.-C., Etienne-Leblanc, S., Faure, G., Feingold, G., Ferrero, L., Fix, A., Flamant, C., Flatau, P. J., Foltz, G. R., Forster, L., Furtuna, I., Gadian, A., Galewsky, J., Gallagher, M., Gallimore, P., Gaston, C., Gentemann, C., Geyskens, N., Giez, A., Gollop, J., Gouirand, I., Gourbeyre, C., de Graaf, D., de Groot, G. E., Grosz, R., Güttler, J., Gutleben, M., Hall, K., Harris, G., Helfer, K. C., Henze, D., Herbert, C., Holanda, B., Ibanez-Landeta, A., Intrieri, J., Iyer, S., Julien, F., Kalesse, H., Kazil, J., Kellman, A., Kidane, A. T., Kirchner, U., Klingebiel, M., Körner, M., Kremper, L. A., Kretzschmar, J., Krüger, O., Kumala, W., Kurz, A., L'Hégaret, P., Labaste, M., Lachlan-Cope, T., Laing, A., Landschützer, P., Lang, T., Lange, D., Lange, I., Laplace, C., Lavik, G., Laxenaire, R., Le Bihan, C., Leandro, M., Lefevre, N., Lena, M., Lenschow, D., Li, Q., Lloyd, G., Los, S., Losi, N., Lovell, O., Luneau, C., Makuch, P., Malinowski, S., Manta, G., Marinou, E., Marsden, N., Masson, S., Maury, N., Mayer, B., Mayers-Als, M., Mazel, C., McGeary, W., McWilliams, J. C., Mech, M., Mehlmann, M., Meroni, A. N., Mieslinger, T., Minikin, A., Minnett, P., Möller, G., Morfa Avalos, Y., Muller, C., Musat, I., Napoli, A., Neuberger, A., Noisel, C., Noone, D., Nordsiek, F., Nowak, J. L., Oswald, L., Parker, D. J., Peck, C., Person, R., Philippi, M., Plueddemann, A., Pöhlker, C., Pörtge, V., Pöschl, U., Pologne, L., Posyniak, M., Prange, M., Quiñones Meléndez, E., Radtke, J., Ramage, K., Reimann, J., Renault, L., Reus, K., Reyes, A., Ribbe, J., Ringel, M., Ritschel, M., Rocha, C. B., Rochetin, N., Röttenbacher, J., Rollo, C., Royer, H., Sadoulet, P., Saffin, L., Sandiford, S., Sandu, I., Schäfer, M., Schemann, V., Schirmacher, I., Schlenczek, O., Schmidt, J., Schröder, M., Schwarzenboeck, A., Sealy, A., Senff, C. J., Serikov, I., Shohan, S., Siddle, E., Smirnov, A., Späth, F., Spooner, B., Stolla, M. K., Szkółka, W., de Szoeke, S. P., Tarot, S., Tetoni, E., Thompson, E., Thomson, J., Tomassini, L., Totems, J., Ubele, A. A., Villiger, L., von Arx, J., Wagner, T., Walther, A., Webber, B., Wendisch, M., Whitehall, S., Wiltshire, A., Wing, A. A., Wirth, M., Wiskandt, J., Wolf, K., Worbes, L., Wright, E., Wulfmeyer, V., Young, S., Zhang, C., Zhang, D., Ziemen, F., Zinner, T., and Zöger, M.: EUREC4A. Earth System Science Data, 13(8), (2021): 4067–4119, https://doi.org/10.5194/essd-13-4067-2021.
    Description: The science guiding the EUREC4A campaign and its measurements is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, EUREC4A marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or the life cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso- (200 km) and larger (500 km) scales, roughly 400 h of flight time by four heavily instrumented research aircraft; four global-class research vessels; an advanced ground-based cloud observatory; scores of autonomous observing platforms operating in the upper ocean (nearly 10 000 profiles), lower atmosphere (continuous profiling), and along the air–sea interface; a network of water stable isotopologue measurements; targeted tasking of satellite remote sensing; and modeling with a new generation of weather and climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that EUREC4A explored – from North Brazil Current rings to turbulence-induced clustering of cloud droplets and its influence on warm-rain formation – are presented along with an overview of EUREC4A's outreach activities, environmental impact, and guidelines for scientific practice. Track data for all platforms are standardized and accessible at https://doi.org/10.25326/165 (Stevens, 2021), and a film documenting the campaign is provided as a video supplement.
    Description: This research has been supported by the people and government of Barbados; the Max Planck Society and its supporting members; the German Research Foundation (DFG) and the German Federal Ministry of Education and Research (grant nos. GPF18-1_69 and GPF18-2_50); the European Research Council (ERC) advanced grant EUREC4A (grant agreement no. 694768) under the European Union’s Horizon 2020 research and innovation program (H2020), with additional support from CNES (the French National Centre for Space Studies) through the EECLAT proposal, Météo-France, the CONSTRAIN H2020 project (grant agreement no. 820829), and the French AERIS Research Infrastructure; the Natural Environment Research Council (NE/S015868/1, NE/S015752/1, and NE/S015779/1); ERC under the European Union’s H2020 program (COMPASS, advanced grant agreement no. 74110); the French national program LEFE INSU, by IFREMER, the French research fleet, CNES, the French research infrastructures AERIS and ODATIS, IPSL, the Chaire Chanel program of the Geosciences Department at ENS, and the European Union's Horizon 2020 research and innovation program under grant agreement no. 817578 TRIATLAS; NOAA’s Climate Variability and Prediction Program within the Climate Program Office (grant nos. GC19-305 and GC19-301); NOAA cooperative agreement NA15OAR4320063; NOAA's Climate Program Office and base funds to NOAA/AOML's Physical Oceanography Division; Swiss National Science Foundation grant no. 188731; the UAS Program Office, Climate Program Office, and Physical Sciences Laboratory and by the US National Science Foundation (NSF) through grant AGS-1938108; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2037 “CLICCS – Climate, Climatic Change, and Society” – project no. 390683824; and Poland’s National Science Centre grant no. UMO-2018/30/M/ST10/00674 and Foundation for Polish Science grant no. POIR.04.04.00-00-3FD6/17-02.
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 4
    Publication Date: 2023-01-03
    Description: A new release of the Max Planck Institute for Meteorology Earth System Model version 1.2 (MPI-ESM1.2) is presented. The development focused on correcting errors in and improving the physical processes representation, as well as improving the computational performance, versatility, and overall user friendliness. In addition to new radiation and aerosol parameterizations of the atmosphere, several relatively large, but partly compensating, coding errors in the model's cloud, convection, and turbulence parameterizations were corrected. The representation of land processes was refined by introducing a multilayer soil hydrology scheme, extending the land biogeochemistry to include the nitrogen cycle, replacing the soil and litter decomposition model and improving the representation of wildfires. The ocean biogeochemistry now represents cyanobacteria prognostically in order to capture the response of nitrogen fixation to changing climate conditions and further includes improved detritus settling and numerous other refinements. As something new, in addition to limiting drift and minimizing certain biases, the instrumental record warming was explicitly taken into account during the tuning process. To this end, a very high climate sensitivity of around 7 K caused by low-level clouds in the tropics as found in an intermediate model version was addressed, as it was not deemed possible to match observed warming otherwise. As a result, the model has a climate sensitivity to a doubling of CO2 over preindustrial conditions of 2.77 K, maintaining the previously identified highly nonlinear global mean response to increasing CO2 forcing, which nonetheless can be represented by a simple two-layer model.
    Type: Article , PeerReviewed
    Format: text
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  • 5
    Publication Date: 2023-01-04
    Description: The effective radiative forcing, which includes the instantaneous forcing plus adjustments from the atmosphere and surface, has emerged as the key metric of evaluating human and natural influence on the climate. We evaluate effective radiative forcing and adjustments in 17 contemporary climate models that are participating in the Coupled Model Intercomparison Project (CMIP6) and have contributed to the Radiative Forcing Model Intercomparison Project (RFMIP). Present-day (2014) global-mean anthropogenic forcing relative to pre-industrial (1850) levels from climate models stands at 2.00 (±0.23) W m−2, comprised of 1.81 (±0.09) W m−2 from CO2, 1.08 (± 0.21) W m−2 from other well-mixed greenhouse gases, −1.01 (± 0.23) W m−2 from aerosols and −0.09 (±0.13) W m−2 from land use change. Quoted uncertainties are 1 standard deviation across model best estimates, and 90 % confidence in the reported forcings, due to internal variability, is typically within 0.1 W m−2. The majority of the remaining 0.21 W m−2 is likely to be from ozone. In most cases, the largest contributors to the spread in effective radiative forcing (ERF) is from the instantaneous radiative forcing (IRF) and from cloud responses, particularly aerosol–cloud interactions to aerosol forcing. As determined in previous studies, cancellation of tropospheric and surface adjustments means that the stratospherically adjusted radiative forcing is approximately equal to ERF for greenhouse gas forcing but not for aerosols, and consequentially, not for the anthropogenic total. The spread of aerosol forcing ranges from −0.63 to −1.37 W m−2, exhibiting a less negative mean and narrower range compared to 10 CMIP5 models. The spread in 4×CO2 forcing has also narrowed in CMIP6 compared to 13 CMIP5 models. Aerosol forcing is uncorrelated with climate sensitivity. Therefore, there is no evidence to suggest that the increasing spread in climate sensitivity in CMIP6 models, particularly related to high-sensitivity models, is a consequence of a stronger negative present-day aerosol forcing and little evidence that modelling groups are systematically tuning climate sensitivity or aerosol forcing to recreate observed historical warming.
    Type: Article , PeerReviewed
    Format: text
    Format: text
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  • 6
    Publication Date: 2024-05-17
    Description: The climate science community aims to improve our understanding of climate change due to anthropogenic influences on atmospheric composition and the Earth's surface. Yet not all climate interactions are fully understood and diversity in climate model experiments persists as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article synthesizes current challenges and emphasizes opportunities for advancing our understanding of climate change and model diversity. The perspective of this article is based on expert views from three multi-model intercomparison projects (MIPs) – the Precipitation Driver Response MIP (PDRMIP), the Aerosol and Chemistry MIP (AerChemMIP), and the Radiative Forcing MIP (RFMIP). While there are many shared interests and specialisms across the MIPs, they have their own scientific foci and specific approaches. The partial overlap between the MIPs proved useful for advancing the understanding of the perturbation-response paradigm through multi-model ensembles of Earth System Models of varying complexity. It specifically facilitated contributions to the research field through sharing knowledge on best practices for the design of model diagnostics and experimental strategies across MIP boundaries, e.g., for estimating effective radiative forcing. We discuss the challenges of gaining insights from highly complex models that have specific biases and provide guidance from our lessons learned. Promising ideas to overcome some long-standing challenges in the near future are kilometer-scale experiments to better simulate circulation-dependent processes where it is possible, and machine learning approaches for faster and better sub-grid scale parameterizations where they are needed. Both would improve our ability to adopt a smart experimental design with an optimal tradeoff between resolution, complexity and simulation length. Future experiments can be evaluated and improved with sophisticated methods that leverage multiple observational datasets, and thereby, help to advance the understanding of climate change and its impacts.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
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  • 7
    Electronic Resource
    Electronic Resource
    [s.l.] : Nature Publishing Group
    Nature 372 (1994), S. 250-252 
    ISSN: 1476-4687
    Source: Nature Archives 1869 - 2009
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Notes: [Auszug] Aerosols affect cloud properties by acting as cloud condensa-tion nuclei (CCN) upon which cloud droplets form. An increase in droplet number concentration TV (cirT3) at constant liquid-water mixing ratio q\ (kg water per kg air) results in a decrease in the average droplet radius and an increase in ...
    Type of Medium: Electronic Resource
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