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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Uncertainty in Model Predictions of Vibrio Vulnificus Response to Climate Variability and Change: A Chesapeake Bay Case Study

The effect that climate change and variability will have on waterborne bacteria is a topic of increasing concern for coastal ecosystems, including the Chesapeake Bay. Surface water temperature trends in the Bay indicate a warming pattern of roughly 0.3-0.4 C per decade over the past 30 years. It is unclear what impact future warming will have on pathogens currently found in the Bay, including Vibrio spp. Using historical environmental data, combined with three different statistical models of Vibrio vulnificus probability, we explore the relationship between environmental change and predicted Vibrio vulnificus presence in the upper Chesapeake Bay. We find that the predicted response of V. vulnificus probability to high temperatures in the Bay differs systematically between models of differing structure. As existing publicly available datasets are inadequate to determine which model structure is most appropriate, the impact of climatic change on the probability of V. vulnificus presence in the Chesapeake Bay remains uncertain. This result points to the challenge of characterizing climate sensitivity of ecological systems in which data are sparse and only statistical models of ecological sensitivity exist.

model predictions↗
NASA NTRSDate not supplied

Estimating Future Changes of Energy Demand for Heating and Cooling Buildings at NASA Centers GC23J-1198

With its unique and trusted earth observations, NASA is a critical source in informing decisions that will help achieve the U.S. goal of Net-Zero Greenhouse Gas (GHG) Emissions by 2050. NASA’s Prediction of Worldwide Energy Resource (POWER) project facilitates the use of NASA Earth Science data holdings within the energy, agricultural, and building heating/cooling design industries. POWER packages solar and meteorological data from several NASA projects in a user friendly GIS-enabled web services system (https://power.larc.nasa.gov). As part of the development of new data products to support the energy and building heating/cooling design communities, we estimate the changes in energy required to heat and cool buildings in the future climate at 14 different NASA site locations spread throughout the continental United States, as projected by CMIP6 climate models under different emissions scenarios. Bias-corrected downscaled time series of meteorological variables are taken from NASA Earth Exchange (NEX) Global Daily Downscaled Projections (GDDP-CMIP6) downscaled climate model data. The spread between the different model projections is accounted for by analyzing both the ensemble average of 22 CMIP6 models and 6 representative models with different climate sensitivities and different interannual variability. Changes in energy use are estimated in two ways. First, changes in the total annual heating and cooling degree days (HDD and CDD, respectively) are calculated relative to the current climate. This is done at all 14 sites. Second, the downscaled time series are used as inputs into RETScreen(R), a clean energy management decision tool, to give an estimate of heating/cooling energy use for a typical office building. We use this estimation method with model data at Langley Research Center. In the next 50 years, the annual total of HDD (CDD) is projected to decrease by 8-38% (increase by 5-28%) at all sites, with the increase in CDD typically a larger magnitude the decrease in HDD. For a typical small office building at Langley Research Center, the amount of energy needed to cool increases by 33-54% and the amount of energy to heat decreases by 29-40%. POWER is working to develop long term climate data services based on these results to include in future data products to provide to users.

Bradley M. Hegyi↗
NASA NTRS2012

GPM Mission Overview and U.S. Science Status

The Global Precipitation Measurement (GPM) Mission is an international satellite mission to unify and advance precipitation measurements from a constellation of research and operational sensors to provide "next-generation" precipitation products [1-2]. Water is fundamental to life on Earth. Knowing where and how much rain and snow falls globally is vital to understanding how weather and climate impact both our environment and Earth's water and energy cycles, including effects on agriculture, fresh water availability, and responses to natural disasters. Since rainfall and snowfall vary greatly from place to place and over time, satellites can provide more uniform observations of rain and snow around the globe than ground instruments, especially in areas where surface measurements are difficult. Relative to current global rainfall products, GPM data products will be characterized by: (l) more accurate instantaneous precipitation measurements (especially for light rain and cold-season solid precipitation), (2) more frequent sampling by an expanded constellation of domestic and international microwave radiometers including operational humidity sounders, (3) intercalibrated microwave brightness temperatures from constellation radiometers within a unified framework, and (4) physical-based precipitation retrievals from constellation radiometers using a common a priori cloud/hydrometeor database derived from GPM Core sensor measurements. The cornerstone of the GPM mission is the deployment of a Core Observatory in a unique 65 non-Sun-synchronous orbit to serve as a physics observatory and a reference standard to unify precipitation measurements by a constellation of dedicated and operational passive microwave sensors. The design of the GPM Core Observatory is an advancement of the Tropical Rainfall Measuring Mission (TRMM)'s highly successful rain-sensing package. The Core Observatory will carry a Ku/Ka-band Dual-frequency Precipitation Radar (DPR) and a multichannel (l0-183 GHz) GPM Microwave Radiometer (GMI). Since light rain and falling snow account for a significant fraction of precipitation occurrence in middle and high latitudes, the GPM instruments extend the capabilities of the TRMM sensors to detect falling snow, measure light rain, and provide, for the first time, quantitative estimates of microphysical properties of precipitation particles. The combined use of DPR and GMI measurements will place greater constraints on possible solutions to radiometer retrievals to improve the accuracy and consistency of precipitation retrievals from all constellation radiometers. The GMI uses 13 different microwave channels to observe energy from the different types of precipitation through clouds for estimating everything from heavy to light rain and for detecting falling snow. As the satellite passes over Earth, the GMI constantly scans a region 885 kilometers across. The Ball Aerospace and Technology Corporation built the GMI under contract with NASA Goddard Space Flight Center. The DPR provides three-dimensional information about precipitation particles derived from reflected energy by these particles at different heights within the cloud system. The two frequencies of the DPR also allow the radar to infer the sizes of precipitation particles and offer insights into a storm's physical characteristics. The Ka-band frequen~y scans across a region of 125 kilometers and is nested within the wider scan of the Ku-band frequency of 245 kilometers. The Japan Aerospace and Exploration Agency (JAXA) and Japan's National Institute of Information and Communications Technology (NICT) built the DPR. The Core Observatory satellite will fly at an altitude of 253 miles (407 kilometers) in a non-Sun-synchronous orbit that covers the Earth from 65 S to 65 N - from about the Antarctic Circle to the Arctic Circle. The GPM Core Observatory is being developed and tested at NASA Goddard Space Flight Center. Once complete, a Japanese H-lIA rocket will carry thPM Core Observatory into orbit from Tanegashima Island, Japan in 2014. The GPM constellation is envisioned to comprise 8 or more microwave sensors provided by partners, including both conical imagers and cross-track sounders. GPM is currently a partnership between NASA and the Japan Aerospace Exploration Agency (JAXA). Additional partnerships are under development to include microwave radiometers on the French-Indian Megha-Tropiques satellite and U.S. Defense Meteorological Satellite Program (DMSP) satellites, as well as humidity sounders or precipitation sensors on operational satellites such as the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP), NOAA-NASA Joint Polar Satellite System (JPSS) satellites, European MetOp satellites, and DMSP follow-on sensors. In addition, data from Chinese and Russian microwave radiometers may be available through international cooperation under the auspices of the Committee on Earth Observation Satellites (CEOS) and Group on Earth Observations (GEO). GPM's next-generation global precipitation data will lead to scientific advances and societal benefits in the following areas: (1) Improved knowledge of the Earth's water cycle and its link to climate change (2) New insights into precipitation microphysics, storm structures and large-scale atmospheric processes (3) Better understanding of climate sensitivity and feedback processes (4) Extended capabilities in monitoring and predicting hurricanes and other extreme weather events (5) Improved forecasting capabilities for natural hazards, including floods, droughts and landslides. (6) Enhanced numerical prediction skills for weather and climate (7) Better agricultural crop forecasting and monitoring of freshwater resources. An overview of the GPM mission concept and science activities in the United States, together with an update on international collaborations in radiometer intercalibration and ground validation, will be presented.

Hou, Arthur Y.↗
NASA NTRS2020

Recent Amplified Global Gross Primary Productivity Due to Temperature Increase is Offset by Reduced Productivity Due to Water Constraints

Satellite remote sensing observationsshow an increased greenness trend over land in recent decades. Whilegreennessobservationscanindicateincreased productivity, estimation of total annual productivity is highly dependent on vegetation response to climate and environmental conditions. Models have been struggling to determinehow much carbon is taken up by plants as a result of increased atmospheric CO2fertilization. Current remote sensing light use efficiency (LUE) models contain considerable uncertainty due to the lack of spatial and temporal variability in maximum LUE parameter and climate sensitivity defined for global plantfunctional types (PFTs). We usedthe optimum LUE (LUEopt) previously derived fromtheglobal FLUXNET network to improve estimation of global gross primary productivity (GPP)for the period 1982–2016. Our results indicateincreasing GPP in northern latitudesowing to reduced cold temperature constraintson plant growth, thereby suggesting increasing negative carbon-climate feedbackin high latitudes. In the tropics, by contrast, our results indicate an emerging positive climate feedback,mainly due to increasing atmospheric vapor pressure deficit(VPD).Further pervasive VPD increase is likely to continue to reduce global GPPand amplify carbon emissions.

Climate change↗
NASA NTRS1990

Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models

The present study provides an intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. This intercomparison uses sea surface temperature change as a surrogate for climate change. The interpretation of cloud-climate interactions is given special attention. A roughly threefold variation in one measure of global climate sensitivity is found among the 19 models. The important conclusion is that most of this variation is attributable to differences in the models' depiction of cloud feedback, a result that emphasizes the need for improvements in the treatment of clouds in these models if they are ultimately to be used as reliable climate predictors. It is further emphazied that cloud feedback is the consequence of all interacting physical and dynamical processes in a general circulation model. The result of these processes is to produce changes in temperature, moisture distribution, and clouds which are integrated into the radiative response termed cloud feedback.

Cess, R. D.↗
NASA NTRS2017

The PMIP4 Contribution to CMIP6-Part 4: Scientific Objectives and Experimental Design of the PMIP4-CMIP6 Last Glacial Maximum Experiments and PMIP4 Sensitivity Experiments

The Last Glacial Maximum (LGM, 21,000 years ago) is one of the suite of paleoclimate simulations included in the current phase of the Coupled Model Intercomparison Project (CMIP6). It is an interval when insolation was similar to the present, but global ice volume was at a maximum, eustatic sea level was at or close to a minimum, greenhouse gas concentrations were lower, atmospheric aerosol loadings were higher than today, and vegetation and land-surface characteristics were different from today. The LGM has been a focus for the Paleoclimate Modelling Intercomparison Project (PMIP) since its inception, and thus many of the problems that might be associated with simulating such a radically different climate are well documented. The LGM state provides an ideal case study for evaluating climate model performance because the changes in forcing and temperature between the LGM and pre-industrial are of the same order of magnitude as those projected for the end of the 21st century. Thus, the CMIP6 LGM experiment could provide additional information that can be used to constrain estimates of climate sensitivity. The design of the Tier 1 LGM experiment (lgm) includes an assessment of uncertainties in boundary conditions, in particular through the use of different reconstructions of the ice sheets and of the change in dust forcing. Additional (Tier 2) sensitivity experiments have been designed to quantify feedbacks associated with land-surface changes and aerosol loadings, and to isolate the role of individual forcings. Model analysis and evaluation will capitalize on the relative abundance of paleoenvironmental observations and quantitative climate reconstructions already available for the LGM.

Kageyama, Masa↗
NASA NTRS2013

Improving Decision-Making Activities for Meningitis and Malaria

Public health professionals are increasingly concerned about the potential impact that climate variability and change can have on infectious disease. The International Research Institute for Climate and Society (IRI) is developing new products to increase the public health community's capacity to understand, use and demand the appropriate climate data and climate information to mitigate the public health impacts of climate on infectious disease, in particular meningitis and malaria. In this paper, we present the new and improved products that have been developed for: (i) estimating dust aerosol for forecasting risks of meningitis and (ii) for monitoring temperature and rainfall and integrating them into a vectorial capacity model for forecasting risks of malaria epidemics. We also present how the products have been integrated into a knowledge system (IRI Data Library Map Room, SERVIR) to support the use of climate and environmental information in climate-sensitive health decision-making.

parasitic diseases↗
NASA NTRS1994

Sensitivity of Regional Climate to Deforestation in the Amazon Basin

The deforestation results in several adverse effect on the natural environment. The focus of this paper is on the effects of deforestation on land-surface processes and regional climate of the Amazon basin. In general, the effect of deforestation on climate are likely to depend on the scale of the defrosted area. In this study, we are interested in the effects due to deforestation of areas with a scale of about 250 km. Hence, a meso-scale climate model is used in performing numerical experiments on the sensitivity of regional climate to deforestation of areas with that size. It is found that deforestation results in less net surface radiation, less evaporation, less rainfall, and warmer surface temperature. The magnitude of the of the change in temperature is of the order 0.5 C, the magnitudes of the changes in the other variables are of the order of IO%. In order to verify some of he results of the numerical experiments, the model simulations of net surface radiation are compared to recent observations of net radiation over cleared and undisturbed forest in the Amazon. The results of the model and the observations agree in the following conclusion: the difference in net surface radiation between cleared and undisturbed forest is, almost, equally partioned between net solar radiation and net long-wave radiation. This finding contributes to our understanding of the basic physics in the deforestation problem.

Eltahir, Elfatih A. B.↗
NASA NTRSDate not supplied

Dynamical Sensitivity in Response to a Wide Range of Abrupt CO 2 Forcings

An improved understanding of dynamical variability is important for projecting future changes in extratropical weather and the interaction between the extratropical troposphere and the tropics, stratosphere, cryosphere and ocean. Despite their simplicity, the abrupt 2x- and 4xCO 2 forcing simulations from the Coupled Model Intercomparison Project (CMIP) Phase 6 DECK experiments enable a mechanistic look into the forcing and feedback response characteristics of models that can be unambiguously attributed to an increase in carbon dioxide concentrations. Thus, while typically used to evaluate the climate sensitivity in models, with a primary focus on global surface temperature change, here we focus on several measures of extratropical variability, including projected changes in the storm tracks and in stratospheric polar vortex variability. Results are primarily based on simulations produced using low- and high-top versions of the NASA Goddard Institute for Space Studies Model (ModelE) but results are also presented from the larger CMIP6 multi-model ensemble. In addition to the 2x- and 4xCO 2 simulations, we also explore the linearity of the response of extratropical dynamical variability in ModelE to varying levels of CO 2 spanning the range 1/8-8xCO 2 . In particular, we show that the expansion of the jet streams varies nonlinearly with increasing CO 2 , especially in the Northern Hemisphere, and that this can be interpreted in terms of variations in ocean heat transport. The impact of composition feedbacks on changes in variability is also discussed.

dynamical variability↗
NASA NTRS2023

Comparison of Clouds and Cloud Feedback between AMIP5 and AMIP6

We examine the changes in clouds and cloud feedback between Phase 5 (AMIP5) and Phase 6 (AMIP6) of the Atmospheric Model Intercomparison Project. Each model is perturbed by uniformly increasing the sea surface temperature by 4 K. The simulated cloud fraction, the perturbed states and cloud radiative kernels are used to derive cloud feedback in the shortwave (SW), longwave (LW) and their sum (Net). Compared to AMIP5, the cloud fraction in AMIP6 increases by 9.1%, while the perturbation leads to a 0.25% decrease. The Net cloud feedback at the top of the atmosphere (TOA) is almost double (174%). Statistical tests support that this change is mainly due to an increase in the surface SW cloud feedback caused by optically thick, middle and low clouds. The contribution of the atmospheric Net component (12%) stems from the increase in the atmospheric LW cloud feedback, likely to play a role in weakening (strengthening) the northward (southward) meridional atmospheric energy transport, while the opposite is true for the surface LW and Net cloud feedback in the meridional oceanic energy transport. The substantial increase in cloud feedback at the TOA primarily contributes to the higher climate sensitivity. The cloud feedback spread in AMIP6 is comparable to that in AMIP5.

Clouds↗
NASA NTRS2021

The Response of the Ozone Layer Under Abrupt 4xCO2 in CMIP6

Previous studies indicate a possible role of stratospheric ozone chemistry feedbacks in the climate response to 4xCO2, either via a reduction in equilibrium climate sensitivity (ECS) (Nowack et al.,2015), or via changes in the tropospheric circulation (Chiodo and Polvani, 2017). However, these effects are subject to uncertainty. Part of the uncertainty may stem from the dependency of the feedback on the pattern of the ozone response, as the radiative efficiency of ozone largely depends on its vertical distribution (Lacis et al., 1990). Here, an analysis is presented of the ozone layer response to 4xCO2 in chemistry–climate models (CCMs) which participated to CMIP intercomparisons. In a previous study using CMIP5 models, it has been shown that under 4xCO2, ozone decreases in the tropical lower stratosphere, and increases over the high latitudes and throughout the upper stratosphere. It was also found that a substantial portion of the spread in the tropical column ozone is tied to inter-model spread in tropical upwelling, which is in turn tied to ECS (Chiodo et al., 2018). Here, we revisit this connection using 4xCO2 data from CMIP6, thereby exploiting the larger number of CCMs than in CMIP5. In addition, we explore the linearity of the ozone response, by complementing the analysis with simulations using lower CO 2 forcing levels (2xCO2). We show that the pattern of the ozone response in is similar to CMIP5. In some models (e.g. WACCM), we find larger ozone responses in CMIP6 than in CMIP5, partly because of the larger ECS and thus larger upwelling response in the tropical pipe. In this presentation, we will discuss the relationship between radiative forcing, transport and ozone, as well as further implications for CMIP6 models.

Ozone layer↗
NASA NTRS2023

Heat Stress to Jeopardize Crop Production in the US Corn Belt Based on Downscaled CMIP5 Projections

CONTEXT Global food security faces increasing challenges from the changing climate. Changes of agricultural output from some of the most productive regions such as the US Corn Belt can largely affect the world's food market. Developing predictive understanding of the agricultural risk of climate change and potential mitigation strategies is critical for the global food security. OBJECTIVE The objective of this study is to assess the responses of maize and soybean yield to projected climate changes in the Corn Belt, identify the shifting environment stressors on crop yield, and tackle potential climate adaptation strategies. METHODS We drive a process-based model, the Decision Support System for Agrotechnology Transfer, with high-resolution statistically downscaled and bias-corrected historical and future climates from ten CMIP5 models in the MACA-2 database. RESULTS AND CONCLUSIONS The multi-model ensemble mean suggests a 12% decrease of maize yield by mid-century and 40% by late century, with a high degree of model consensus in the direction of changes; for individual models, the projected decrease of maize yield by late century ranges from <5% to over 80%, with the worst crop outcome corresponding to the most sensitive climate models. Soybean yield is projected to increase by midcentury with a high degree of model consensus, but such consensus is lost by late century as some projections shift to significant decreases. Crop yield in the Corn Belt is currently limited by water stress, but is projected to be increasingly limited by heat stress as well after the midcentury. The mounting heat stress will drive the most productive zone for maize to shift from central to northern part of the Corn Belt, but the projected increase in the northern states cannot fully compensate for the decrease in the south, causing the total production to decrease if agricultural practice stays the same. Earlier planting can alleviate only a small fraction of the heat-induced crop loss in a warmer climate. Climate change will (at least partially) offset the yield boost caused by agricultural technology and intensification. SIGNIFICANCE This study advances our predictive understanding of crop yield responses to climate change, and suggests that a multitude of strategies will be needed to address the climate change challenges for the U.S. agriculture.

Crop yield↗
NASA NTRS2021

Non-Monotonic Response of the Climate System to Abrupt CO2 Forcing

We explore the climate system response to abrupt CO2 forcing, spanning the range 1 × to 8×CO2, with two state-of-the-art coupled atmosphere-ocean-sea-ice-land models: the NASA Goddard Institute for Space Studies Model E2.1-G (GISS-E2.1-G) and the Community Earth System Model (CESM-LE). We find that the effective climate sensitivity is a non-monotonic function of CO2 in both models, reaching a minimum at 3×CO2 for GISS-E2.1-G, and 4×CO2 for CESM-LE. A similar non-monotonic response is found in Northern Hemisphere surface temperature, sea-ice, precipitation, the latitude of zero precipitation minus-evaporation, and the strength of the Hadley cell. Interestingly, the Atlantic meridional overturning circulation collapses when non-monotonicity appears and does not recover for larger CO2 forcings. Analyzing the climate response over the same CO2 range with slab ocean versions of the same models, we demonstrate that the climate system's non-monotonic response is linked to ocean dynamics.

Non-monotonic response↗
NASA NTRS2016

Improving Our Fundamental Understanding of the Role of Aerosol Cloud Interactions in the Climate System

The effect of an increase in atmospheric aerosol concentrations on the distribution and radiative properties of Earth's clouds is the most uncertain component of the overall global radiative forcing from preindustrial time. General circulation models (GCMs) are the tool for predicting future climate, but the treatment of aerosols, clouds, and aerosol−cloud radiative effects carries large uncertainties that directly affect GCM predictions, such as climate sensitivity. Predictions are hampered by the large range of scales of interaction between various components that need to be captured. Observation systems (remote sensing, in situ) are increasingly being used to constrain predictions, but significant challenges exist, to some extent because of the large range of scales and the fact that the various measuring systems tend to address different scales. Fine-scale models represent clouds, aerosols, and aerosol−cloud interactions with high fidelity but do not include interactions with the larger scale and are therefore limited from a climatic point of view. We suggest strategies for improving estimates of aerosol−cloud relationships in climate models, for new remote sensing and in situ measurements, and for quantifying and reducing model uncertainty.

general circulation models↗
NASA NTRS2015

Sensitivity to Factors Underlying the Hiatus

Recent trends in global mean surface air temperature fall outside the 90 range predicted by models using the CMIP5 forcings and scenarios; this recent period of muted warming is dubbed the hiatus. The hiatus has attracted broad attention in both the popular press and the scientific literature, primarily because of its perceived implications for understanding long-term trends. Many hypotheses have been offered to explain the warming slowdown during the hiatus, and comprehensive studies of this period across multiple variables and spatial scales will likely improve our understanding of the physical mechanisms driving global temperature change and variability.We argue, however, that decadal temperature trends by themselves are unlikely to constrain future trajectories of global mean temperature and that the hiatus does not significantly revise our understanding of overall climate sensitivity. Instead, we demonstrate that, because of the poorly constrained nature of the hiatus, model-observation disagreements over this period may be resolvable via uncertainties in the observations, modeled internal variability, forcing estimates, or (more likely) some combination of all three factors. We define the hiatus interval as 1998-2012, endpoints judiciously chosen to minimize observed warming by including the large 1998 El Nio event and excluding 2014, an exceptionally warm year. Such choices are fundamentally subjective and cannot be considered random, so any probabilistic statements regarding the likelihood of this occurring need to be made carefully. Using this definition, the observed global temperature trend estimates from four datasets fall outside the 5-95 interval predicted by the CMIP5 models. Here we explore some of the plausible explanations for this discrepancy, and show that no unique explanation is likely to fully account for the hiatus.

trends↗
NASA NTRS2006

Satellite Observations of the Effect of Natural and Anthropogenic Aerosols on Clouds

Our knowledge of atmospheric aerosols (smoke, pollution, dust or sea salt particles, small enough to be suspended in the air), their evolution, composition, variability in space and time and interaction with clouds and precipitation is still lacking despite decades of research. Understanding the global aerosol system is critical to quantifying anthropogenic climate change, to determine climate sensitivity from observations and to understand the hydrological cycle. While a single instrument was used to demonstrate 50 years ago that the global CO2 levels are rising, posing threat of global warming, we need an array of satellites and field measurements coupled with chemical transport models to understand the global aerosol system. This complexity of the aerosol problem results from their short lifetime (1 week) and variable chemical composition. A new generation of satellites provides exciting opportunities to measure the global distribution of aerosols, distinguishing natural from anthropogenic aerosol and measuring their interaction with clouds and climate.

Kaufman, Yoram J.↗
NASA NTRS2018

The Importance of Understanding the Last Glacial Maximum for Climate Change

The last glacial maximum (LGM) at approximately 23–18k (k—thousand calendar years) provides an important contrast to our present and pre-industrial climate in a warming world. Global observational datasets of LGM land and sea surface conditions have been synthesized and present some interesting challenges both for providing another scenario for understanding climate change and for climate sensitivity. These challenges are ongoing, as data increase and modeling improves. By definition, the LGM is defined as the time during the last glacial interval in which maximum ice was sequestered in ice sheets as visible in the marine isotopic records. Maximum cooling is visible from pollen and macrofossil records 14C dated to this interval, and ice sheets and alpine glaciers are roughly at their maximum extent throughout the globe. The ice cores extracted from Greenland and Antarctica have given us high-resolution records of greenhouse gases, dust, and isotopes of hydrogen and oxygen which reveal the progression out of the LGM at 18k as climate warmed.

Peteet, Dorothy↗