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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Quantifying the Uncertainties in an Ensemble of Decadal Climate Predictions

Meaningful climate predictions should be accompanied by the corresponding uncertainty range. Common methods for estimating the uncertainty range are based on the spread of ensemble predictions. However, a simulation ensemble is not necessarily a proper sample of the real distribution of the climate, and therefore, the ensemble spread cannot be interpreted as the actual uncertainty. We propose a new method that links between the ensemble spread and the uncertainty without relying on any assumptions regarding the distribution of the ensemble predictions. The method is tested using CMIP5 1981-2010 decadal predictions and is shown to outperform other common methods.

Strobach, Ehud↗
NASA NTRS2009

Monitoring Top-of-Atmosphere Radiative Energy Imbalance for Climate Prediction

Large climate feedback uncertainties limit the prediction accuracy of the Earth s future climate with an increased CO2 atmosphere. One potential to reduce the feedback uncertainties using satellite observations of top-of-atmosphere (TOA) radiative energy imbalance is explored. Instead of solving the initial condition problem in previous energy balance analysis, current study focuses on the boundary condition problem with further considerations on climate system memory and deep ocean heat transport, which is more applicable for the climate. Along with surface temperature measurements of the present climate, the climate feedbacks are obtained based on the constraints of the TOA radiation imbalance. Comparing to the feedback factor of 3.3 W/sq m/K of the neutral climate system, the estimated feedback factor for the current climate system ranges from -1.3 to -1.0 W/sq m/K with an uncertainty of +/-0.26 W/sq m/K. That is, a positive climate feedback is found because of the measured TOA net radiative heating (0.85 W/sq m) to the climate system. The uncertainty is caused by the uncertainties in the climate memory length. The estimated time constant of the climate is large (70 to approx. 120 years), implying that the climate is not in an equilibrium state under the increasing CO2 forcing in the last century.

Lin, Bing↗
NASA NTRS2020

To What Extent Biomass Burning Aerosols Impact South America Seasonal Climate Predictions?

We applied the Goddard Earth Observing System for subseasonal to seasonal climate prediction to assess the impact of inclusion biomass burning (BB) aerosols over South America (SA) during the austral winter. We also evaluated the model sensitivity to the BB emissions prescription using no emissions, monthly climatological, and daily emissions. Each hindcast consisted of four members running from June to November of each year between 2000 and 2015. Our results indicated that interactive BB aerosols improve the seasonal climate prediction performance over SA. More realistic daily based emissions significantly further improve the performance in comparison with the climatological ones. Therefore, improvements in the BB emissions representation are urged to represent the aerosol impacts on seasonal climate prediction performance adequately.

Julliana L. M. Freire↗
NASA NTRS2011

Tropical and Subtropical Cloud Transitions in Weather and Climate Prediction Models: The GCSS/WGNE Pacific Cross-Section Intercomparison (GPCI)

A model evaluation approach is proposed in which weather and climate prediction models are analyzed along a Pacific Ocean cross section, from the stratocumulus regions off the coast of California, across the shallow convection dominated trade winds, to the deep convection regions of the ITCZ the Global Energy and Water Cycle Experiment Cloud System Study/Working Group on Numerical Experimentation (GCSS/ WGNE) Pacific Cross-Section Intercomparison (GPCI). The main goal of GPCI is to evaluate and help understand and improve the representation of tropical and subtropical cloud processes in weather and climate prediction models. In this paper, a detailed analysis of cloud regime transitions along the cross section from the subtropics to the tropics for the season June July August of 1998 is presented. This GPCI study confirms many of the typical weather and climate prediction model problems in the representation of clouds: underestimation of clouds in the stratocumulus regime by most models with the corresponding consequences in terms of shortwave radiation biases; overestimation of clouds by the 40-yr ECMWF Re-Analysis (ERA-40) in the deep tropics (in particular) with the corresponding impact in the outgoing longwave radiation; large spread between the different models in terms of cloud cover, liquid water path and shortwave radiation; significant differences between the models in terms of vertical cross sections of cloud properties (in particular), vertical velocity, and relative humidity. An alternative analysis of cloud cover mean statistics is proposed where sharp gradients in cloud cover along the GPCI transect are taken into account. This analysis shows that the negative cloud bias of some models and ERA-40 in the stratocumulus regions [as compared to the first International Satellite Cloud Climatology Project (ISCCP)] is associated not only with lower values of cloud cover in these regimes, but also with a stratocumulus-to-cumulus transition that occurs too early along the trade wind Lagrangian trajectory. Histograms of cloud cover along the cross section differ significantly between models. Some models exhibit a quasi-bimodal structure with cloud cover being either very large (close to 100%) or very small, while other models show a more continuous transition. The ISCCP observations suggest that reality is in-between these two extreme examples. These different patterns reflect the diverse nature of the cloud, boundary layer, and convection parameterizations in the participating weather and climate prediction models.

Teixeira, J.↗
NASA NTRS2010

The Impact of Ocean Observations in Seasonal Climate Prediction

The ocean provides the most significant memory for the climate system. Hence, a critical element in climate forecasting with coupled models is the initialization of the ocean with states from an ocean data assimilation system. Remotely-sensed ocean surface fields (e.g., sea surface topography, SST, winds) are now available for extensive periods and have been used to constrain ocean models to provide a record of climate variations. Since the ocean is virtually opaque to electromagnetic radiation, the assimilation of these satellite data is essential to extracting the maximum information content. More recently, the Argo drifters have provided unprecedented sampling of the subsurface temperature and salinity. Although the duration of this observation set has been too short to provide solid statistical evidence of its impact, there are indications that Argo improves the forecast skill of coupled systems. This presentation will address the impact these different observations have had on seasonal climate predictions with the GMAO's coupled model.

Rienecker, Michele↗
NASA NTRS2002

The Impact of Land Initialization and Assimilation on Climate Predictability and Prediction

Analysis will be presented which explores the impact of land conditions on monthly to seasonal climate simulations in a variety of atmospheric general circulation models (AGCMs). In one set of experiments, the Geophysical Fluid Dynamics Laboratory (GDFL) AGCM is used to explore the nature of soil-moisture predictability and associated climate predictability as an initial value problem. For another set of experiments, the Center for Ocean Land Atmosphere (COLA) and the Goddard Earth Observing System 2 (GEOS-2) AGCMs are used to investigate the impact of realistic snow initialization and assimilation in retrospective climate forecasts for the northern hemisphere spring (March-June).

Schlosser, C. Adam↗
NASA NTRS1995

Lidar-measured winds from space: A key component for weather and climate prediction

The deployment of a space-based Doppler lidar would provide information that is fundamental to advancing the understanding and prediction of weather and climate. This paper reviews the concepts of wind measurement by Doppler lidar, highlights the results of some observing system simulation experiments with lidar winds, and discusses the important advances in earth system science anticipated with lidar winds. Observing system simulation experiments, conducted using two different general circulation models, have shown (1) that there is a significant improvement in the forecast accuracy over the Southern Hemisphere and tropical oceans resulting from the assimilation of simulated satellite wind data, and (2) that wind data are significantly more effective than temperature or moisture data in controlling analysis error. Because accurate wind observations are currently almost entirely unavailable for the vast majority of tropical cyclones worldwide, lidar winds have the potential to substan- tially improve tropical cyclone forecasts. Similarly, to improve water vapor flux divergence calculations, a direct measure of the ageostrophic wind is needed since the present level of uncer- tainty cannot be reduced with better temperature and moisture soundings alone.

Baker, Wayman E.↗
NASA NTRS2021

Climate Predicts Geographic and Temporal Variation in Mosquito-Borne Disease Dynamics on Two Continents

Climate drives population dynamics through multiple mechanisms, which can lead to seemingly context-dependent effects of climate on natural populations. For climate-sensitive diseases such as dengue, chikungunya, and Zika, climate appears to have opposing effects in different contexts. Here we show that a model, parameterized with laboratory measured climate-driven mosquito physiology, captures three key epidemic characteristics across ecologically and culturally distinct settings in Ecuador and Kenya: the number, timing, and duration of outbreaks. The model generates a range of disease dynamics consistent with observed Aedes aegypti abundances and laboratory-confirmed arboviral incidence with variable accuracy (28–85% for vectors, 44–88%for incidence). The model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and homes with piped water and made of cement. Models with limited calibration that robustly capture climate-virus relationships can help guide intervention efforts and climate change disease projections.

Climate-change ecology↗
NASA NTRS1995

Workshop on Satellite and In situ Observations for Climate Prediction

Participants in this workshop, which convened in Venice, Italy, 6-8 May 1993, met to consider the current state of climate monitoring programs and instrumentation for the purpose of climatological prediction on short-term (seasonal to interannual) timescales. Data quality and coverage requirements for definition of oceanographic heat and momentum fluxes, scales of inter- and intra-annual variability, and land-ocean-atmosphere exchange processes were examined. Advantages and disadvantages of earth-based and spaceborne monitoring systems were considered, as were the structures for future monitoring networks, research programs, and modeling studies.

Acker, James G.↗
NASA NTRS2011

An Update on Experimental Climate Prediction and Analysis Products Being Developed at NASA's Global Modeling and Assimilation Office

The Global Modeling and Assimilation Office at NASA's Goddard Space Flight Center is developing a number of experimental prediction and analysis products suitable for research and applications. The prediction products include a large suite of subseasonal and seasonal hindcasts and forecasts (as a contribution to the US National MME), a suite of decadal (10-year) hindcasts (as a contribution to the IPCC decadal prediction project), and a series of large ensemble and high resolution simulations of selected extreme events, including the 2010 Russian and 2011 US heat waves. The analysis products include an experimental atlas of climate (in particular drought) and weather extremes. This talk will provide an update on those activities, and discuss recent efforts by WCRP to leverage off these and similar efforts at other institutions throughout the world to develop an experimental global drought early warning system.

Schubert, Siegfried↗
NASA NTRS2009

Improving Weather and Climate Prediction with the AIRS on Aqua

The Atmospheric Infrared Sounder (AIRS) on the EOS Aqua Spacecraft was launched on May 4, 2002. Early in the mission, the AIRS instrument demonstrated its value to the weather forecasting community with better than 6 hours of improvement on the 5 day forecast. Now with over six years of consistent and stable data from AIRS, scientists are able to examine processes governing weather and climate and look at seasonal and interannual trends from the AIRS data with high statistical confidence. Naturally, long-term climate trends require a longer data set, but indications are that the Aqua spacecraft and the AIRS instrument should last beyond 2016. This paper briefly describes the AIRS products, reviews past science and weather accomplishments from AIRS data product users and highlights recent findings in these areas.

Temperature↗
NASA NTRS1977

Integration schemes for climate prediction

To assure the conservation of total mass, momentum, energy, vorticity, etc., a method of modifying any given algorithm is presented. The modified scheme has at least the same order of accuracy as the given scheme.

Isaacson, E.↗
NASA NTRS2002

Climate Prediction Sees Future Despite Chaos: Researchers Outside NASA use NCCS Resources for Studies

The air on this mostly sunny January day is crisp and the wind is blustery. The morning's National Weather Service 6-hour forecast had accurately predicted these conditions for the Baltimore-Washington area and the 2-3 day extended outlook was almost perfect. The previous week, the National Center for Environmental Prediction's (NCEP) 6-10 day temperature and precipitation outlook for the general trends for the' region was correct as well. However, no forecast could have predicted specific details about this day. It is 28.5 F in the sunshine bright enough for dark sunglasses, and windy enough to blow off a hat. Such details are impossible to foresee with any accuracy and are outside the scope of routine weather prediction. Equally difficult is accurately forecasting weather beyond about 2 weeks.

Source record↗
NASA NTRS2011

GEOS-5 System Developments for Decadal Climate Prediction at NASA/GMAO

The GEOS-5 AOGCM is being used to conduct the CMIP5 decadal prediction suite. The model is comprised of the GEOS-5 AGCM coupled to MOM4 and CICE, with aerosol distributions from GOCART. The decadal predictions are initialized from the GEOS Ocean-Ice reanalysis that is coupled to MERRA, GMAO's atmospheric reanalysis. Perturbations are generated using a bred vector approach. The integrations are still underway. The system and preliminary results will be presented.

Rienecker, Michele↗
NASA NTRS2022

Use of Machine Learning to Reduce Uncertainties in Particle Number Concentration and Aerosol Indirect Radiative Forcing Predicted by Climate Models

The radiative forcing of anthropogenic aerosols associated with aerosol–cloud interactions (RF(sub aci)) remains the largest source of uncertainty in climate prediction. The calculation of particle number concentration (PNC), one of the critical parameters affecting RF(sub aci), is generally simplified in climate models. Here we employ outputs from long-term (30-years) simulations of a global size-resolved (sectional) aerosol microphysics model and a machine-learning tool to develop a Random Forest Regression Model (RFRM) for PNC. We have implemented the PNC RFRM in GISS-ModelE2.1 with a mass-based One-Moment Aerosol module, which is one of CMIP6 models. Compared to the default setting, the GISS-ModelE2.1 simulation based on RFRM reduces the changes of cloud droplet number concentration associated with anthropogenic emissions, and decreases the RF(sub aci) from −1.46 W⋅m(exp −2) to −1.11 W⋅m(exp −2). This work highlights a promising approach based on machine learning to reduce uncertainties of climate models in predicting PNC and RF(sub aci) without compromising their computing efficiency.

Radiative forcing↗