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  • 1
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    Non‐Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models (2022)
    Details
    Publication Date: 2022-12-06
    Description: Deep learning can accurately represent sub‐grid‐scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack interpretability due to large internal dimensionality, resulting in reduced trustworthiness in these methods. Here, we use Variational Encoder Decoder structures (VED), a non‐linear dimensionality reduction technique, to learn and understand convective processes in an aquaplanet superparameterized climate model simulation, where deep convective processes are simulated explicitly. We show that similar to previous deep learning studies based on feed‐forward neural nets, the VED is capable of learning and accurately reproducing convective processes. In contrast to past work, we show this can be achieved by compressing the original information into only five latent nodes. As a result, the VED can be used to understand convective processes and delineate modes of convection through the exploration of its latent dimensions. A close investigation of the latent space enables the identification of different convective regimes: (a) stable conditions are clearly distinguished from deep convection with low outgoing longwave radiation and strong precipitation; (b) high optically thin cirrus‐like clouds are separated from low optically thick cumulus clouds; and (c) shallow convective processes are associated with large‐scale moisture content and surface diabatic heating. Our results demonstrate that VEDs can accurately represent convective processes in climate models, while enabling interpretability and better understanding of sub‐grid‐scale physical processes, paving the way to increasingly interpretable machine learning parameterizations with promising generative properties.
    Description: Plain Language Summary: Deep neural nets are hard to interpret due to their hundred thousand or million trainable parameters without further postprocessing. We demonstrate in this paper the usefulness of a network type that is designed to drastically reduce this high dimensional information in a lower‐dimensional space to enhance the interpretability of predictions compared to regular deep neural nets. Our approach is, on the one hand, able to reproduce small‐scale cloud related processes in the atmosphere learned from a physical model that simulates these processes skillfully. On the other hand, our network allows us to identify key features of different cloud types in the lower‐dimensional space. Additionally, the lower‐order manifold separates tropical samples from polar ones with a remarkable skill. Overall, our approach has the potential to boost our understanding of various complex processes in Earth System science.
    Description: Key Points: A Variational Encoder Decoder (VED) can predict sub‐grid‐scale thermodynamics from the coarse‐scale climate state. The VED's latent space can distinguish convective regimes, including shallow/deep/no convection. The VED's latent space reveals the main sources of convective predictability at different latitudes.
    Description: EC ERC HORIZON EUROPE European Research Council http://dx.doi.org/10.13039/100019180
    Description: Columbia sub‐award 1
    Description: Advanced Research Projects Agency - Energy http://dx.doi.org/10.13039/100006133
    Description: Deutsches Klimarechenzentrum http://dx.doi.org/10.13039/100018730
    Description: National Science Foundation Science and Technology Center Learning the Earth with Artificial intelligence and Physics
    Keywords: ddc:551.5 ; machine learning ; generative deep learning ; convection ; parameterization ; explainable artificial intelligence ; dimensionality reduction
    Language: English
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  • 2
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    Deep Learning Based Cloud Cover Parameterization for ICON (2022)
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    Publication Date: 2023-12-05
    Description: A promising approach to improve cloud parameterizations within climate models and thus climate projections is to use deep learning in combination with training data from storm‐resolving model (SRM) simulations. The ICOsahedral Non‐hydrostatic (ICON) modeling framework permits simulations ranging from numerical weather prediction to climate projections, making it an ideal target to develop neural network (NN) based parameterizations for sub‐grid scale processes. Within the ICON framework, we train NN based cloud cover parameterizations with coarse‐grained data based on realistic regional and global ICON SRM simulations. We set up three different types of NNs that differ in the degree of vertical locality they assume for diagnosing cloud cover from coarse‐grained atmospheric state variables. The NNs accurately estimate sub‐grid scale cloud cover from coarse‐grained data that has similar geographical characteristics as their training data. Additionally, globally trained NNs can reproduce sub‐grid scale cloud cover of the regional SRM simulation. Using the game‐theory based interpretability library SHapley Additive exPlanations, we identify an overemphasis on specific humidity and cloud ice as the reason why our column‐based NN cannot perfectly generalize from the global to the regional coarse‐grained SRM data. The interpretability tool also helps visualize similarities and differences in feature importance between regionally and globally trained column‐based NNs, and reveals a local relationship between their cloud cover predictions and the thermodynamic environment. Our results show the potential of deep learning to derive accurate yet interpretable cloud cover parameterizations from global SRMs, and suggest that neighborhood‐based models may be a good compromise between accuracy and generalizability.
    Description: Plain Language Summary: Climate models, such as the ICOsahedral Non‐hydrostatic climate model, operate on low‐resolution grids, making it computationally feasible to use them for climate projections. However, physical processes –especially those associated with clouds– that happen on a sub‐grid scale (inside a grid box) cannot be resolved, yet they are critical for the climate. In this study, we train neural networks that return the cloudy fraction of a grid box knowing only low‐resolution grid‐box averaged variables (such as temperature, pressure, etc.) as the climate model sees them. We find that the neural networks can reproduce the sub‐grid scale cloud fraction on data sets similar to the one they were trained on. The networks trained on global data also prove to be applicable on regional data coming from a model simulation with an entirely different setup. Since neural networks are often described as black boxes that are therefore difficult to trust, we peek inside the black box to reveal what input features the neural networks have learned to focus on and in what respect the networks differ. Overall, the neural networks prove to be accurate methods of reproducing sub‐grid scale cloudiness and could improve climate model projections when implemented in a climate model.
    Description: Key Points: Neural networks can accurately learn sub‐grid scale cloud cover from realistic regional and global storm‐resolving simulations. Three neural network types account for different degrees of vertical locality and differentiate between cloud volume and cloud area fraction. Using a game theory based library we find that the neural networks tend to learn local mappings and are able to explain model errors.
    Description: EC ERC HORIZON EUROPE European Research Council
    Description: Partnership for Advanced Computing in Europe (PRACE)
    Description: NSF Science and Technology Center, Center for Learning the Earth with Artificial Intelligence and Physics (LEAP)
    Description: Deutsches Klimarechenzentrum
    Description: Columbia sub‐award 1
    Description: https://github.com/agrundner24/iconml_clc
    Description: https://doi.org/10.5281/zenodo.5788873
    Description: https://code.mpimet.mpg.de/projects/iconpublic
    Keywords: ddc:551.5 ; cloud cover ; parameterization ; machine learning ; neural network ; explainable AI ; SHAP
    Language: English
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  • 3
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    Detecting Extreme Temperature Events Using Gaussian Mixture Models (2023)
    Details
    Publication Date: 2024-02-12
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Extreme temperature events have traditionally been detected assuming a unimodal distribution of temperature data. We found that surface temperature data can be described more accurately with a multimodal rather than a unimodal distribution. Here, we applied Gaussian Mixture Models (GMM) to daily near‐surface maximum air temperature data from the historical and future Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations for 46 land regions defined by the Intergovernmental Panel on Climate Change. Using the multimodal distribution, we found that temperature extremes, defined based on daily data in the warmest mode of the GMM distributions, are getting more frequent in all regions. Globally, a 10‐year extreme temperature event relative to 1985–2014 conditions will occur 13.6 times more frequently in the future under 3.0°C of global warming levels (GWL). The frequency increase can be even higher in tropical regions, such that 10‐year extreme temperature events will occur almost twice a week. Additionally, we analyzed the change in future temperature distributions under different GWL and found that the hot temperatures are increasing faster than cold temperatures in low latitudes, while the cold temperatures are increasing faster than the hot temperatures in high latitudes. The smallest changes in temperature distribution can be found in tropical regions, where the annual temperature range is small. Our method captures the differences in geographical regions and shows that the frequency of extreme events will be even higher than reported in previous studies.〈/p〉
    Description: Plain Language Summary: Extreme temperature events are unusual weather conditions with exceptionally low or high temperatures. Traditionally, the temperature range was determined by assuming a single distribution, which describes the frequency of temperatures at a given climate using their mean and variability. This single distribution was then used to detect extreme weather events. In this study, we found that temperature data from reanalyses and climate models can be more accurately described using a mixture of multiple Gaussian distributions. We used the information from this mixture of Gaussians to determine the cold and hot extremes of the distributions. We analyzed their change in a future climate and found that hot temperature extremes are getting more frequent in all analyzed regions at a rate that is even higher than found in previous studies. For example, a global 10‐year event will occur 13.6 times more frequently under 3.0°C of global warming. Furthermore, our results show that the temperatures of hot days will increase faster than the temperature of cold days in equatorial regions, while the opposite will occur in polar regions. Extreme hot temperatures will be the new normal in highly populated regions such as the Mediterranean basin.〈/p〉
    Description: Key Points: 〈list list-type="bullet"〉 〈list-item〉 〈p xml:lang="en"〉Extreme temperature events are detected with Gaussian Mixture Models to follow a multimodal rather than a unimodal distribution〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉10‐year temperature extremes will occur 13.6 times more frequently under 3.0°C future warming〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉Colder days are getting warmer faster than hotter days in high latitudes, whereas it is the opposite for many regions in low latitudes〈/p〉〈/list-item〉 〈/list〉 〈/p〉
    Description: European Research Council http://dx.doi.org/10.13039/501100000781
    Description: https://github.com/EyringMLClimateGroup/pacal23jgr_GaussianMixtureModels_Extremes
    Description: https://doi.org/10.5281/zenodo.3401363
    Keywords: ddc:551.5 ; extreme events ; Gaussian mixture models ; daily maximum temperatures ; return periods ; bimodal distributions ; multimodal distributions
    Language: English
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  • 4
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    Do Emergent Constraints on Carbon Cycle Feedbacks Hold in CMIP6? (2022)
    Details
    Publication Date: 2024-02-21
    Description: Emergent constraints on carbon cycle feedbacks in response to warming and increasing atmospheric CO〈sub〉2 〈/sub〉 concentration have previously been identified in Earth system models participating in the Coupled Model Intercomparison Project (CMIP) Phase 5. Here, we examine whether two of these emergent constraints also hold for CMIP6. The spread of the sensitivity of tropical land carbon uptake to tropical warming in an idealized simulation with a 1% per year increase of atmospheric CO〈sub〉2 〈/sub〉 shows only a slight decrease in CMIP6 (−52 ± 35 GtC/K) compared to CMIP5 (−49 ± 40 GtC/K). For both model generations, the observed interannual variability in the growth rate of atmospheric CO〈sub〉2 〈/sub〉 yields a consistent emergent constraint on the sensitivity of tropical land carbon uptake with a constrained range of −37 ± 14 GtC/K for the combined ensemble (i.e., a reduction of ∼30% in the best estimate and 60% in the uncertainty range relative to the multimodel mean of the combined ensemble). A further emergent constraint is based on a relationship between CO〈sub〉2 〈/sub〉 fertilization and the historical increase in the CO〈sub〉2 〈/sub〉 seasonal cycle amplitude in high latitudes. However, this emergent constraint is not evident in CMIP6. This is in part because the historical increase in the amplitude of the CO〈sub〉2 〈/sub〉 seasonal cycle is more accurately simulated in CMIP6, such that the models are all now close to the observational constraint.
    Description: Plain Language Summary: The statistical model of so‐called emergent constraints help to better understand the sensitivity of Earth system processes in a changing climate. Here, we analyze the robustness of two previously found emergent constraints on carbon cycle feedbacks, using models from the Coupled Model Intercomparison Project (CMIP) of Phases 5 and 6. First the decrease of carbon storage in the tropics due to increasing near‐surface air temperatures, which is found to be robust on the choise of model ensemble. Giving a constraint estimate of −52 ± 35 GtC/K for CMIP6 models, being within the range of uncertainty for the previously estimated result for CMIP5. Second, the increase of carbon storage in high latitudes due to CO〈sub〉2 〈/sub〉 fertilization effect, which is found to be not evident among CMIP6 models. This is in part because the historical increase in the amplitude of the CO〈sub〉2 〈/sub〉 seasonal cycle is more accurately simulated in CMIP6, such that the models are all now close to the observational constraint.
    Description: Key Points: An emergent constraint on the sensitivity of tropical land carbon to global warming, originally derived from Coupled Model Intercomparison Project Phase 5 (CMIP5), also holds for CMIP6. The combined CMIP5 + CMIP6 ensemble gives an emergent constraint on the sensitivity of tropical land carbon to global warming of −37 ± 14 GtC/K. An emergent constraint on the fertilization feedback due to rising CO〈sub〉2 〈/sub〉 levels, previously derived, is not evident in CMIP6.
    Description: Horizon 2020 Framework Programme http://dx.doi.org/10.13039/100010661
    Description: ERC
    Description: https://doi.org/10.5281/zenodo.6900341
    Description: https://doi.org/10.5281/zenodo.3387139
    Description: https://github.com/ESMValGroup
    Description: https://docs.esmvaltool.org/
    Keywords: ddc:551 ; carbon cycle ; emergent constraint ; CMIP5 ; CMIP6 ; fertilization effect ; temperature warming
    Language: English
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  • 5
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    Global Premature Mortality Due to Anthropogenic Outdoor Air Pollution and the Contribution of Past Climate Change (2013)
    In:  Other Sources
    Details
    Publication Date: 2019-07-13
    Description: Increased concentrations of ozone and fine particulate matter (PM2.5) since preindustrial times reflect increased emissions, but also contributions of past climate change. Here we use modeled concentrations from an ensemble of chemistryclimate models to estimate the global burden of anthropogenic outdoor air pollution on present-day premature human mortality, and the component of that burden attributable to past climate change. Using simulated concentrations for 2000 and 1850 and concentrationresponse functions (CRFs), we estimate that, at present, 470000 (95% confidence interval, 140000 to 900000) premature respiratory deaths are associated globally and annually with anthropogenic ozone, and 2.1 (1.3 to 3.0) million deaths with anthropogenic PM2.5-related cardiopulmonary diseases (93%) and lung cancer (7%). These estimates are smaller than ones from previous studies because we use modeled 1850 air pollution rather than a counterfactual low concentration, and because of different emissions. Uncertainty in CRFs contributes more to overall uncertainty than the spread of model results. Mortality attributed to the effects of past climate change on air quality is considerably smaller than the global burden: 1500 (20000 to 27000) deaths yr (exp -1) due to ozone and 2200 (350000 to 140000) due to PM2.5. The small multi-model means are coincidental, as there are larger ranges of results for individual models, reflected in the large uncertainties, with some models suggesting that past climate change has reduced air pollution mortality.
    Keywords: Meteorology and Climatology; Environment Pollution
    Type: GSFC-E-DAA-TN11367 , Environmental Research Letters (ISSN 1748-9326) (e-ISSN 1748-9326); 8; 3; 034005
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  • 6
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    Taking Climate Model Evaluation to the Next Level (2019)
    In:  Other Sources
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    Publication Date: 2019-07-13
    Description: Earth system models are complex and represent a large number of processes, resulting in a persistent spread across climate projections for a given future scenario. Owing to different model performances against observations and the lack of independence among models, there is now evidence that giving equal weight to each available model projection is suboptimal. This Perspective discusses newly developed tools that facilitate a more rapid and comprehensive evaluation of model simulations with observations, process-based emergent constraints that are a promising way to focus evaluation on the observations most relevant to climate projections, and advanced methods for model weighting. These approaches are needed to distil the most credible information on regional climate changes, impacts, and risks for stakeholders and policy-makers.
    Keywords: Meteorology and Climatology
    Type: GSFC-E-DAA-TN65080 , Nature Climate Change (ISSN 1758-678X) (e-ISSN 1758-6798); 9; 102-110
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  • 7
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    Global Air Quality and Climate (2012)
    In:  CASI
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    Publication Date: 2019-07-13
    Description: Emissions of air pollutants and their precursors determine regional air quality and can alter climate. Climate change can perturb the long-range transport, chemical processing, and local meteorology that influence air pollution. We review the implications of projected changes in methane (CH4), ozone precursors (O3), and aerosols for climate (expressed in terms of the radiative forcing metric or changes in global surface temperature) and hemispheric-to-continental scale air quality. Reducing the O3 precursor CH4 would slow near-term warming by decreasing both CH4 and tropospheric O3. Uncertainty remains as to the net climate forcing from anthropogenic nitrogen oxide (NOx) emissions, which increase tropospheric O3 (warming) but also increase aerosols and decrease CH4 (both cooling). Anthropogenic emissions of carbon monoxide (CO) and non-CH4 volatile organic compounds (NMVOC) warm by increasing both O3 and CH4. Radiative impacts from secondary organic aerosols (SOA) are poorly understood. Black carbon emission controls, by reducing the absorption of sunlight in the atmosphere and on snow and ice, have the potential to slow near-term warming, but uncertainties in coincident emissions of reflective (cooling) aerosols and poorly constrained cloud indirect effects confound robust estimates of net climate impacts. Reducing sulfate and nitrate aerosols would improve air quality and lessen interference with the hydrologic cycle, but lead to warming. A holistic and balanced view is thus needed to assess how air pollution controls influence climate; a first step towards this goal involves estimating net climate impacts from individual emission sectors. Modeling and observational analyses suggest a warming climate degrades air quality (increasing surface O3 and particulate matter) in many populated regions, including during pollution episodes. Prior Intergovernmental Panel on Climate Change (IPCC) scenarios (SRES) allowed unconstrained growth, whereas the Representative Concentration Pathway (RCP) scenarios assume uniformly an aggressive reduction, of air pollutant emissions. New estimates from the current generation of chemistry-climate models with RCP emissions thus project improved air quality over the next century relative to those using the IPCC SRES scenarios. These two sets of projections likely bracket possible futures. We find that uncertainty in emission-driven changes in air quality is generally greater than uncertainty in climate-driven changes. Confidence in air quality projections is limited by the reliability of anthropogenic emission trajectories and the uncertainties in regional climate responses, feedbacks with the terrestrial biosphere, and oxidation pathways affecting O3 and SOA.
    Keywords: Meteorology and Climatology
    Type: GSFC-E-DAA-TN8931 , Chemical Society Reviews; 41; 19; 6663-6683
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  • 8
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    2016 International Land Model Benchmarking (ILAMB) Workshop Report (2016)
    In:  CASI
    Details
    Publication Date: 2019-07-13
    Description: As earth system models (ESMs) become increasingly complex, there is a growing need for comprehensive and multi-faceted evaluation of model projections. To advance understanding of terrestrial biogeochemical processes and their interactions with hydrology and climate under conditions of increasing atmospheric carbon dioxide, new analysis methods are required that use observations to constrain model predictions, inform model development, and identify needed measurements and field experiments. Better representations of biogeochemistryclimate feedbacks and ecosystem processes in these models are essential for reducing the acknowledged substantial uncertainties in 21st century climate change projections.
    Keywords: Meteorology and Climatology
    Type: DOE/SC-0186 , GSFC-E-DAA-TN43734 , 2016 International Land Model Benchmarking (ILAMB) Workshop; May 16, 2016 - May 18, 2016; Washington, DC; United States
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  • 9
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    Overview of IGAC/SPARC Chemistry-Climate Model Initiative (CCMI) Community Simulations in Support of Upcoming Ozone and Climate Assessments (2013)
    In:  SPARC Newsletter, 40 (Januar). pp. 48-66.
    Details
    Publication Date: 2016-06-20
    Type: Article , NonPeerReviewed
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  • 10
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    Improving Antarctic Total Ozone Projections by a Process-Oriented Multiple Diagnostic Ensemble Regression (2013)
    AMS (American Meteorological Society)
    In:  Journal of the Atmospheric Sciences, 70 (12). pp. 3959-3976.
    Details
    Publication Date: 2018-04-16
    Description: Accurate projections of stratospheric ozone are required because ozone changes affect exposure to ultraviolet radiation and tropospheric climate. Unweighted multimodel ensemble-mean (uMMM) projections from chemistry–climate models (CCMs) are commonly used to project ozone in the twenty-first century, when ozone-depleting substances are expected to decline and greenhouse gases are expected to rise. Here, the authors address the question of whether Antarctic total column ozone projections in October given by the uMMM of CCM simulations can be improved by using a process-oriented multiple diagnostic ensemble regression (MDER) method. This method is based on the correlation between simulated future ozone and selected key processes relevant for stratospheric ozone under present-day conditions. The regression model is built using an algorithm that selects those process-oriented diagnostics that explain a significant fraction of the spread in the projected ozone among the CCMs. The regression model with observed diagnostics is then used to predict future ozone and associated uncertainty. The precision of the authors’ method is tested in a pseudoreality; that is, the prediction is validated against an independent CCM projection used to replace unavailable future observations. The tests show that MDER has higher precision than uMMM, suggesting an improvement in the estimate of future Antarctic ozone. The authors’ method projects that Antarctic total ozone will return to 1980 values at around 2055 with the 95% prediction interval ranging from 2035 to 2080. This reduces the range of return dates across the ensemble of CCMs by about a decade and suggests that the earliest simulated return dates are unlikely.
    Type: Article , PeerReviewed
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