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NASA NTRS2022

Investigating Stable Oxygen and Carbon Isotopic Variability in Speleothem Records Over the Last Millennium Using Multiple Isotope-Enabled Climate Models

The incorporation of water isotopologues into the hydrology of general circulation models (GCMs) facilitates the comparison between modeled and measured proxy data in paleoclimate archives. However, the variability and drivers of measured and modeled water isotopologues, as well as the diversity of their representation in different models, are not well constrained. Improving our understanding of this variability in past and present climates will help to better constrain future climate change projections and decrease their range of uncertainty. Speleothems are a precisely datable terrestrial paleoclimate archives and provide well-preserved (semi-)continuous multivariate isotope time series in the lower latitudes and mid-latitudes and are therefore well suited to assess climate and isotope variability on decadal and longer timescales. However, the relationships of speleothem oxygen and carbon isotopes to climate variables are influenced by site-specific parameters, and their comparison to GCMs is not always straightforward. Here we compare speleothem oxygen and carbon isotopic signatures from the Speleothem Isotopes Synthesis and Analysis database version 2 (SISALv2) to the output of five different water-isotope-enabled GCMs (ECHAM5-wiso, GISS-E2-R, iCESM, iHadCM3, and isoGSM) over the last millennium (850–1850 CE). We systematically evaluate differences and commonalities between the standardized model simulation outputs. The goal is to distinguish climatic drivers of variability for modeled isotopes and compare them to those of measured isotopes. We find strong regional differences in the oxygen isotope signatures between models that can partly be attributed to differences in modeled surface temperature. At low latitudes, precipitation amount is the dominant driver for stable water isotope variability; however, at cave locations the agreement between modeled temperature variability is higher than for precipitation variability. While modeled isotopic signatures at cave locations exhibited extreme events coinciding with changes in volcanic and solar forcing, such fingerprints are not apparent in the speleothem isotopes. This may be attributed to the lower temporal resolution of speleothem records compared to the events that are to be detected. Using spectral analysis, we can show that all models underestimate decadal and longer variability compared to speleothems (albeit to varying extents). We found that no model excels in all analyzed comparisons, although some perform better than the others in either mean or variability. Therefore, we advise a multi-model approach whenever comparing proxy data to modeled data. Considering karst and cave internal processes, e.g., through isotope-enabled karst models, may alter the variability in speleothem isotopes and play an important role in determining the most appropriate model. By exploring new ways of analyzing the relationship between the oxygen and carbon isotopes, their variability, and co-variability across timescales, we provide methods that may serve as a baseline for future studies with different models using, e.g., different isotopes, different climate archives, or different time periods.

isotope-enabled simulations↗

NASA NTRS2017

Principal Modes of Precipitation Variability from Preliminary Series of IMERG Data

The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission, IMERG, is the unified U.S. algorithm that provides merged Microwave Infrared (IR) satellite precipitation product for the U.S. GPM team. Even though IMERG record is still very short, 2014-2016, it is tempting to test if it captures ENSO and NAO signals as compared to the popular, still on-going, TRMM Multi-satellite Precipitation Analysis, TMPA. El Nino Southern Oscillation (ENSO) is the most significant mode of interannual variability of tropical ocean atmosphere. North Atlantic Oscillation (NAO) impact is on monthly scales and is mostly an atmospheric mode in the North Atlantic. There exist well-defined, multivariate, indexes that represent ENSO and NAO conditions and phase.

climate variability↗

NASA NTRS2006

Is Solar Variability Reflected in the Nile River?

We investigate the possibility that solar variability influences North African climate by using annual records of the water level of the Nile collected in 622-1470 A.D. The time series of these records are nonstationary, in that the amplitudes and frequencies of the quasi-periodic variations are time-dependent. We apply the Empirical Mode Decomposition technique especially designed to deal with such time series. We identify two characteristic timescales in the records that may be linked to solar variability: a period of about 88 years and one exceeding 200 years. We show that these timescales are present in the number of auroras reported per decade in the Northern Hemisphere at the same time. The 11-year cycle is seen in the Nile's high-water level variations, but it is damped in the low-water anomalies. We suggest a possible physical link between solar variability and the low-frequency variations of the Nile water level. This link involves the influence of solar variability on the atmospheric Northern Annual Mode and on its North Atlantic Ocean and Indian Ocean patterns that affect the rainfall over the sources of the Nile in eastern equatorial Africa.

solar variability↗

NASA NTRS2011

Intraseasonal Variations in Tropical Energy Balance: Relevance to Climate Sensitivity?

Intraseasonal variability of deep convection represents a fundamental mode of organization for tropical convection. While most studies of intraseasonal oscillations (ISOs) have focused on the spatial propagation and dynamics of convectively coupled circulations, here we examine the projection of ISOs on the tropically-averaged heat and moisture budget. One unresolved question concerns the degree to which observable variations in the "fast" processes (e.g. convection, radiative / turbulent fluxes) can inform our understanding of feedback mechanisms operable in the context of climate change. Our analysis use daily data from satellite observations, the Modern Era analysis for Research and Applications (MERRA), and other model integrations to address these questions: (i) How are tropospheric temperature variations related to that tropical deep convection and the associated ice cloud fractional amount (ICF), ice water path (IWP), and properties of warmer liquid clouds? (ii) What role does moisture transport play vis-a-vis ocean latent heat flux in enabling the evolution of deep convection to mediate PBL - free atmospheric temperature equilibration? (iii) What affect do convectively generated upper-tropospheric clouds have on the TOA radiation budget? Our methodology is similar to that of Spencer et al., (2007 GRL ) whereby a composite time series of various quantities over 60+ ISO events is built using tropical mean tropospheric temperature signal as a reference to which the variables are related at various lag times (from -30 to +30 days). The area of interest encompasses the global oceans between 20oN/S. The increase of convective precipitation cannot be sustained by evaporation within the domain, implying strong moisture transports into the tropical ocean area. The decrease in net TOA radiation that develops after the peak in deep convective rainfall, is part of the response that constitutes a "discharge" / "recharge" mechanism that facilitates tropical heat balance maintenance on these time scales. However, water vapor and hydrologic scaling relationships for this mode of variability cast doubt on the utility of ISO variations as proxies for climate sensitivity response to external radiatively forced (e.g. greenhouse gas-induced) climate change.

Robertson, Franklin R.↗

NASA NTRS1990

Basic Mechanisms of Solar Variability

In order to simulate the behavior of a changing Sun in a realistic way, researchers have used a perturbation analysis (the results are summarized in Endal et al. 1985). In this approach, they use a standard solar model and then vary several of the model parameters to mimic the sudden or gradual change of some physical property within the Sun. The evolution following the perturbation is followed in a physically self-consistent way, that is, hydrostatic and thermal processes occur in their normal timescales. This treatment has allowed us to determine the sensitivity of the various global parameters to physical processes affecting the solar interior, and it has guided us in defining the least complex global solar model which can address the question of climatically significant variability. Not surprisingly, magnetic fields play the crucial role. The current status of the model, what researchers have already learned from it, and the future prospects are discussed.

Sofia, Sabatino↗

NASA NTRS1995

Modeling Climate Change in the Absence of Climate Change Data. Editorial Comment

Practitioners of climate change prediction base many of their future climate scenarios on General Circulation Models (GCM's), each model with differing assumptions and parameter requirements. For representing the atmosphere, GCM's typically contain equations for calculating motion of particles, thermodynamics and radiation, and continuity of water vapor. Hydrology and heat balance are usually included for continents, and sea ice and heat balance are included for oceans. The current issue of this journal contains a paper by Van Blarcum et al. (1995) that predicts runoff from nine high-latitude rivers under a doubled CO2 atmosphere. The paper is important since river flow is an indicator variable for climate change. The authors show that precipitation will increase under the imposed perturbations and that owing to higher temperatures earlier in the year that cause the snow pack to melt sooner, runoff will also increase. They base their simulations on output from a GCM coupled with an interesting water routing scheme they have devised. Climate change models have been linked to other models to predict deforestation.

Skiles, J. W.↗

NASA NTRS2015

Relative Contributions of Mean-State Shifts and ENSO-Driven Variability to Precipitation Changes in a Warming Climate

The El Nino-Southern Oscillation (ENSO) is an important driver of regional hydroclimate variability through far-reaching teleconnections. This study uses simulations performed with Coupled General Circulation Models (CGCMs) to investigate how regional precipitation in the 21st century may be affected by changes in both ENSO-driven precipitation variability and slowly-evolving mean rainfall. First, a dominant, time-invariant pattern of canonical ENSO variability (cENSO) is identified in observed SST data. Next, the fidelity with which 33 state-of-the-art CGCMs represent the spatial structure and temporal variability of this pattern (as well as its associated precipitation responses) is evaluated in simulations of 20th century climate change. Possible changes in both the temporal variability of this pattern and its associated precipitation teleconnections are investigated in 21st century climate projections. Models with better representation of the observed structure of the cENSO pattern produce winter rainfall teleconnection patterns that are in better accord with 20th century observations and more stationary during the 21st century. Finally, the model-predicted 21st century rainfall response to cENSO is decomposed into the sum of three terms: 1) the 21st century change in the mean state of precipitation; 2) the historical precipitation response to the cENSO pattern; and 3) a future enhancement in the rainfall response to cENSO, which amplifies rainfall extremes. By examining the three terms jointly, this conceptual framework allows the identification of regions likely to experience future rainfall anomalies that are without precedent in the current climate.

Celine J W Bonfils↗

NASA NTRS2014

Reply to Comment by Laprise on 'the Added Value to Global Model Projections of Climate Change by Dynamical Downscaling: a Case Study over the Continental U.S. Using the GISS-ModelE2 and WRF Models'

In his comment, Laprise raises several points that we agree merit consideration. His primary critique is that our study [Racherla et al., 2012] tested the ability of the WRF regional climate model to reproduce historical temperature and precipitation change relative to the driving global climate model (GCM) using only a single simulation rather than an ensemble. He asserts that the observed changes are smaller than the internal variability in the climate system (i.e., not statistically significant) and that thus a single simulation should not necessarily be able to capture the observations. Laprise points out that the statistical signal is reduced for a multi-decadal trend such as the one we analyzed in comparison with mean climatology and cites two studies showing that for particular climate parameters it can take any years for a signal to be discerned over internal variability. He states that The results of theexperiment as designed were strongly influenced by the presence of internal variability and sampling errors,which masked the rather small climate changes that may have occurred as a consequence of changes inforcing during the period considered. While Laprise discusses statistics in general terms at some length, for the actual climate trends examined in our study, he offers no evidence that the forced signal was smallcompared with internal variability. The two studies he cites [de Ela et al., 2013; Maraun, 2013] do not provide convincing evidence as they concern climate variables averaged over different times and areas. One in fact examines extreme precipitation events, which by definition are rare and thus have a lower significance level. We accept the general point that it is important to consider internal variability, and as noted in our paper we agree that an ensemble of simulations is in principle an optimal, though computationally expensive, approach. While we did not present the statistical significance of the observations in our original paper, we have now evaluated those for the regional temperature trends used in our study to evaluate the added value of WRF and thus can analyze data as to the magnitude of the trends with respect to internal variability.

climate↗

NASA NTRS2024

PRODEM: An Annual Series of Summer DEMs (2019 through 2022) of the Marginal Areas of the Greenland Ice Sheet

Surface topography across the marginal zone of the Greenland Ice Sheet is constantly evolving in response to changing weather, season, climate, and ice dynamics. However, current digital elevation models (DEMs) for the ice sheet are usually based on data from a multi-year period, thus obscuring these changes over time. Here we present four 500 m resolution summer DEMs (PRODEMs) of the Greenland Ice Sheet marginal zone for 2019 through 2022. The PRODEMs cover the marginal zone from the ice edge to 50 km inland, hence capturing all Greenland outlet glaciers. Each PRODEM is based on data fusion of CryoSat-2 radar altimetry and ICESat-2 laser altimetry using regionally varying kriging of elevation anomalies relative to ArcticDEM. The PRODEMs are validated using leave-one-out cross-validation, and PRODEM19 is further validated against an external data set, showcasing their ability to correctly represent surface elevations within the associated spatially varying prediction uncertainties. We observe a general lowering of surface elevations during the 4-year PRODEM period, but the spatial pattern of change is highly complex and with annual changes superimposed. The PRODEMs enable detailed studies of the marginal ice sheet elevation changes. With their high spatio-temporal resolution, the PRODEMs will be of value to a wide range of researchers and users studying ice sheet dynamics and monitoring how the ice sheet responds to changing environmental conditions. PRODEMs from summer 2019 through 2022 are available at https://doi.org/10.22008/FK2/52WWHG (Winstrup, 2024), and we plan to annually update the product henceforth.

Digital Elevation Models↗

NASA NTRS2022

Impact of the Arctic Oscillation from March on summertime sea ice

Current understanding of the cold season Arctic oscillation (AO) impact on the summertime sea ice is revisited in this study by analyzing the role from each month. Earlier studies examined the prolonged AO impact using a smooth average over 1–2 seasons (e.g. December–March, December–April, March–May), ignoring large month-to-month AO variability. This study finds that the March AO is most influential on the summertime sea ice loss. First, the March AO is most highly negative-correlated with the AO in summer. Secondly, surface energy budget, sea level pressure, and low-tropospheric circulation exhibit that their time-lagged responses to the positive (negative) phase of the March AO grow with time, transitioning to the patterns associated with the negative (positive) phase of the AO that induces sea ice decrease (increase) in summer. Time evolution of the surface energy budget explains the growth of the sea ice concentration anomaly in summer, and a warming-to-cooling transition in October. The regional difference in sea ice anomaly distribution can be also explained by circulation and surface energy budget patterns. The sea ice concentration along the pan-Arctic including the Laptev, East Siberian, Chukchi, and Beaufort Sea decreases (increases) in summer in response to the positive (negative) phase of the March AO, while the sea ice to the northeast of Greenland increases (decreases). This sea ice response is better represented by the March AO than by the seasonally averaged winter AO, suggesting that the March AO can play more significant role. This study also finds that the sea ice decrease in response to the positive AO is distinctively smaller in the 20th century than in the 21st century, along with the opposite sea ice response over the Canada Basin due to circulation difference between the two periods.

Arctic Oscillation↗

NASA NTRS1984

Carbon monoxide in the earth's atmosphere - Increasing trend

The results of an analysis of more than 60,000 atmospheric measurements of carbon monoxide taken over 3-1/2 years at Cape Meares, Oregon (45 deg N, 125 deg W), indicate that the background concentration of this gas is increasing. The rate of increase, although uncertain, is about 6 percent per year on average. Human activities are the likely cause of a substantial portion of this observed increase; however, because of the short atmospheric lifetime of carbon monoxide and the relatively few years of observations, fluctuations of sources and sinks related to the natural variability of climate may have affected the observed trend. Increased carbon monoxide may deplete tropospheric hydroxyl radicals, slowing down the removal of dozens of man-made and anthropogenic trace gases and thus indirectly affecting the earth's climate and possibly the stratospheric ozone layer.

Khalil, M. A. K.↗

NASA NTRS1996

Precipitation Recycling

The water cycle regulates and reflects natural variability in climate at the regional and global scales. Large-scale human activities that involve changes in land cover, such as tropical deforestation, are likely to modify climate through changes in the water cycle. In order to understand, and hopefully be able to predict, the extent of these potential global and regional changes, we need first to understand how the water cycle works. In the past, most of the research in hydrology focused on the land branch of the water cycle, with little attention given to the atmospheric branch. The study of precipitation recycling which is defined as the contribution of local evaporation to local precipitation, aims at understanding hydrologic processes in the atmospheric branch of the water cycle. Simply stated, any study on precipitation recycling is about how the atmospheric branch of the water cycle works, namely, what happens to water vapor molecules after they evaporate from the surface, and where will they precipitate?

Eltahir, Elfatih A. B.↗

NASA NTRS1999

Improved Stratospheric Temperature Retrievals for Climate Reanalysis

The Data Assimilation Office (DAO) is embarking on plans to generate a twenty year reanalysis data set of climatic atmospheric variables. One of the focus points will be in the evaluation of the dynamics of the stratosphere. The Stratospheric Sounding Unit (SSU), flown as part of the TIROS Operational Vertical Sounder (TOVS), is one of the primary stratospheric temperature sensors flown consistently throughout the reanalysis period. Seven unique sensors made the measurements over time, with individual instrument characteristics that need to be addressed. The stratospheric temperatures being assimilated across satellite platforms will profoundly impact the reanalysis dynamical fields. To attempt to quantify aspects of instrument and retrieval bias we are carefully collecting and analyzing all available information on the sensors, their instrument anomalies, forward model errors and retrieval biases. For the retrieval of stratospheric temperatures, we adapted the minimum variance approach of Jazwinski (1970) and Rodgers (1976) and applied it to the SSU soundings. In our algorithm, the state vector contains an initial guess of temperature from a model six hour forecast provided by the Goddard EOS Data Assimilation System (GEOS/DAS). This is combined with an a priori covariance matrix, a forward model parameterization, and specifications of instrument noise characteristics. A quasi-Newtonian iteration is used to obtain convergence of the retrieved state to the measurement vector. This algorithm also enables us to analyze and address the systematic errors associated with the unique characteristics of the cell pressures on the individual SSU instruments and the resolving power of the instruments to vertical gradients in the stratosphere. The preliminary results of the improved retrievals and their assimilation as well as baseline calculations of bias and rms error between the NESDIS operational product and col-located ground measurements will be presented.

Rokke, L.↗

NASA NTRS2001

Satellite Ocean Color: Present Status, Future Challenges

We are midway into our 5th consecutive year of nearly continuous, high quality ocean color observations from space. The Ocean Color and Temperature Scanner/Polarization and Directionality of the Earth's Reflectances (OCTS/POLDER: Nov. 1996 - Jun. 1997), the Sea-viewing Wide Field-of-view Sensor (SeaWiFS: Sep. 1997 - present), and now the Moderate Resolution Imaging Spectrometer (MODIS: Sep. 2000 - present) have and are providing unprecedented views of chlorophyll dynamics on global scales. Global synoptic views of ocean chlorophyll were once a fantasy for ocean color scientists. It took nearly the entire 8-year lifetime of limited Coastal Zone Color Scanner (CZCS) observations to compile seasonal climatologies. Now SeaWIFS produces comparably complete fields in about 8 days. For the first time, scientists may observe spatial and temporal variability never before seen in a synoptic context. Even more exciting, we are beginning to plausibly ask questions of interannual variability. We stand at the beginning of long-time time series of ocean color, from which we may begin to ask questions of interdecadal variability and climate change. These are the scientific questions being addressed by users of the 18-year Advanced Very High Resolution Radiometer time series with respect to terrestrial processes and ocean temperatures. The nearly 5-year time series of ocean color observations now being constructed, with possibilities of continued observations, can put us at comparable standing with our terrestrial and physical oceanographic colleagues, and enable us to understand how ocean biological processes contribute to, and are affected by global climate change.

Gregg, Watson W.↗

NASA NTRS2002

Information Theoretic Approaches to Rapid Discovery of Relationships in Large Climate Data Sets

Mutual information as the asymptotic Bayesian measure of independence is an excellent starting point for investigating the existence of possible relationships among climate-relevant variables in large data sets, As mutual information is a nonlinear function of of its arguments, it is not beholden to the assumption of a linear relationship between the variables in question and can reveal features missed in linear correlation analyses. However, as mutual information is symmetric in its arguments, it only has the ability to reveal the probability that two variables are related. it provides no information as to how they are related; specifically, causal interactions or a relation based on a common cause cannot be detected. For this reason we also investigate the utility of a related quantity called the transfer entropy. The transfer entropy can be written as a difference between mutual informations and has the capability to reveal whether and how the variables are causally related. The application of these information theoretic measures is rested on some familiar examples using data from the International Satellite Cloud Climatology Project (ISCCP) to identify relation between global cloud cover and other variables, including equatorial pacific sea surface temperature (SST), over seasonal and El Nino Southern Oscillation (ENSO) cycles.

Knuth, Kevin H.↗

NASA NTRS2003

Water Ice Albedo Variations on the Martian Northern Polar Cap

The Viking Orbiters determined that the surface of Mars northern residual cap is water ice. Many researchers have related observed atmospheric water vapor abundances to seasonal exchange between reservoirs such as the polar caps, but the extent to which the exchange between the surface and the atmosphere remains uncertain. Early studies of the ice coverage and albedo of the northern residual Martian polar cap using Mariner 9 and Viking images reported that there were substantial internannual differences in ice deposition on the polar cap, a result which suggested a highly variable Martian climate. However, some of the data used in these studies were obtained at differing values of heliocentric solar longitude (L(sub s)). Reevaluation of this dataset indicated that the residual cap undergoes seasonal brightening throughout the summer, and indicated that this process repeats from year to year. In this study we continue to compare Mariner 9 and Viking Orbiter imaging observations and thermal data of the north residual polar cap to data acquired with Mars Global Surveyor s Mars Orbiter Camera (MOC) instrument. In the current study, our goal is to examine all released data from MGS MOC in the northern summer season, along with applicable TES data in order to better understand the albedo variations in the northern summer and their implications on water transport. To date, work has focused primarily on the MOC dataset. In 1999, data acquisition of the northern polar regions began at L(sub s) = 107, although there was little north polar data acquired from L(sub s)= 107 to L(sub s) = 109. We examined a total of 409 images from L(sub s) = 107 to L(sub s)=148. We have also examined data from 2000 from L(sub s)= 93 to L(sub s)= 110; additional progress is ongoing. Here we present a progress report of our observations, and continue to determine their implications for the Martian water cycle.

Hale, A. S.↗

NASA NTRS2003

Albedo Variations on the Martian Northern Polar Cap as Seen by MGS

Hale, A. S.↗

NASA NTRS2002

Influence of solar variability on stratospheric dynamics

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solar variability climate↗