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All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Impacts of land use change and elevated CO2 on the interannual variations and seasonal cycles of gross primary productivity in China

Climate change, rising CO2 concentration, and land use and land cover change (LULCC) are primary driving forces for terrestrial gross primary productivity (GPP), but their impacts on the temporal changes in GPP are uncertain. In this study, the effects of the three main factors on the interannual variation (IAV) and seasonal cycle amplitude (SCA) of GPP in China were investigated using 12 terrestrial biosphere models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project. The simulated ensemble mean value of China's GPP between 1981 and 2010, driven by common climate forcing, LULCC and CO2 data, was found to be 7.4±1.8 Pg C/yr. In general, climate was the dominant control factor of the annual trends, IAV and seasonality of China's GPP. The overall rising CO2 led to enhanced plant photosynthesis, thus increasing annual mean and IAV of China's total GPP, especially in northeastern and southern China, where vegetation is dense. LULCC decreased the IAV of China's total GPP by ∼7 %, whereas rising CO2 induced an increase of 8 %. Compared to climate change and elevated CO2, LULCC showed less contributions to GPP's temporal variation, and its impact acted locally, mainly in southwestern China. Furthermore, this study also examined subregional contributions to the temporal changes in China's total GPP. Southern and southeastern China showed higher contributions to China's annual GPP, whereas southwestern and central parts of China explained larger fractions of the IAV in China's GPP.

land use change↗
NASA NTRS2013

Model Sensitivity to North Atlantic Freshwater Forcing at 8.2 Ka

We compared four simulations of the 8.2 ka event to assess climate model sensitivity and skill in responding to North Atlantic freshwater perturbations. All of the simulations used the same freshwater forcing, 2.5 Sv for one year, applied to either the Hudson Bay (northeastern Canada) or Labrador Sea (between Canada's Labrador coast and Greenland). This freshwater pulse induced a decadal-mean slowdown of 10-25%in the Atlantic Meridional Overturning Circulation (AMOC) of the models and caused a large-scale pattern of climate anomalies that matched proxy evidence for cooling in the Northern Hemisphere and a southward shift of the Intertropical Convergence Zone. The multi-model ensemble generated temperature anomalies that were just half as large as those from quantitative proxy reconstructions, however. Also, the duration of AMOC and climate anomalies in three of the simulations was only several decades, significantly shorter than the duration of approx.150 yr in the paleoclimate record. Possible reasons for these discrepancies include incorrect representation of the early Holocene climate and ocean state in the North Atlantic and uncertainties in the freshwater forcing estimates.

simulation↗
NASA NTRS2019

The Salience of Nonlinearities in the Boreal Winter Response to ENSO: Arctic Stratosphere and Europe

The Arctic stratospheric response to El Niño (EN) and La Niña (LN) is evaluated in a 41 member ensemble of the period 1980 to 2009 in the Goddard Earth Observing System Chemistry-Climate Model. We consider whether the responses to EN and LN are equal in magnitude and opposite in sign, whether the responses to moderate and extreme events are proportion-ate, and if the response depends on whether sea surface temperature anomalies (SSTs) peak in the Eastern Pacific (EP) or Central Pacific (CP). There is no indication of any nonlinearities between EN and LN, though in ~ 15% of the ensemble members the stratospheric sudden warming (SSW) frequencies for EN and LN are similar, suggesting that a similar SSW frequency for EN and LN, as has occurred over the past ~ 60 years, can occur by chance. The response to extreme EN events is not proportionate to the amplitude of the underlying SST anomalies in spring. EP EN events preferentially increase zonal wavenumber 1 and decrease zonal wavenumber 2 as compared to CP EN events, however the zonal-mean Arctic stratospheric and subpolar surface response is generally little different between EP EN and CP EN once one accounts for the relative weakness of CP events. These differences between EP and CP events and between moderate and extreme EN events only emerge if at least 25 events are composited, however, due to the small signal-to-noise ratio, and hence these differences may be of little practical benefit.

Sea surface temperature anomalies (SSTs) peak in t↗
NASA NTRS2022

Development and Evaluation of Ensemble Consensus Precipitation Estimates over High Mountain Asia

Precipitation estimates are highly uncertain in complex regions such as High-Mountain Asia (HMA), where ground measurements are very difficult to obtain, and atmospheric dynamics poorly understood. Though gridded products derived from satellite-based observations and/or reanalysis can provide temporally and spatially distributed estimates of precipitation, there are significant inconsistencies in these products. As such, to date, there is little agreement in the community on the best and most accurate gridded precipitation product in HMA, which is likely area dependent because of HMA’s strong heterogeneities and complex orography. Targeting these gaps, this article presents the development of a consensus ensemble precipitation product using three gridded precipitation datasets (the Integrated Multi-satellitE Retrieals for Global Precipitation Measurement IMERG, the Climate Hazards group Infrared Precipitation with Stations CHIRPS, and the ECMWF Reanalysis ERA5) with a localized probability matched mean (LPM) approach. We evaluate the performance of the LPM estimate along with a simple ensemble mean (EM) estimate to overcome the differences and disparities of the three selected constituent products on long-term averages and trends in HMA. Our analysis demonstrates that LPM reduces the high biases embedded in the ensemble members and provides more realistic spatial patterns compared to EM. LPM is also a good alternative for merging data products with different spatio-temporal resolutions. By filtering disparities among the individual ensemble members, LPM overcomes the problem of a certain product performing well only in a particular area and provides a consensus estimate with plausible temporal trends.

Fadji Z Maina↗
NASA NTRS1999

Potential Predictability of the Monsoon Subclimate Systems

While El Nino/Southern Oscillation (ENSO) phenomenon can be predicted with some success using coupled oceanic-atmospheric models, the skill of predicting the tropical monsoons is low regardless of the methods applied. The low skill of monsoon prediction may be either because the monsoons are not defined appropriately or because they are not influenced significantly by boundary forcing. The latter characterizes the importance of internal dynamics in monsoon variability and leads to many eminent chaotic features of the monsoons. In this study, we analyze results from nine AMIP-type ensemble experiments with the NASA/GEOS-2 general circulation model to assess the potential predictability of the tropical climate system. We will focus on the variability and predictability of tropical monsoon rainfall on seasonal-to-interannual time scales. It is known that the tropical climate is more predictable than its extratropical counterpart. However, predictability is different from one climate subsystem to another within the tropics. It is important to understand the differences among these subsystems in order to increase our skill of seasonal-to-interannual prediction. We assess potential predictability by comparing the magnitude of internal and forced variances as defined by Harzallah and Sadourny (1995). The internal variance measures the spread among the various ensemble members. The forced part of rainfall variance is determined by the magnitude of the ensemble mean rainfall anomaly and by the degree of consistency of the results from the various experiments.

Yang, Song↗
NASA NTRS2017

An Intercomparison of Changes Associated with Earth's Lower Tropospheric Temperature Using Traditional and AMIP-Style Reanalyses

Reanalyses have become an integral tool for evaluating regional and global climate variations, and an important component of this is modifications to the energy budget. Reductions in Arctic Sea ice extent has induced an albedo feedback, causing the Arctic to warm more rapidly than anywhere else in the world, referred to as "Arctic Amplification." This has been demonstrated by observations and numerous reanalyses, including the Modern Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). However, the Arctic Amplification signal is non-existent in a ten member ensemble of the MERRA-2 Atmospheric Model Intercomparison Project (M2AMIP) simulations, using the same prescribed climate forcing, including Sea Surface Temperature (SST) and ice. An evaluation of the temperature tendency within the lower troposphere due to radiation, moisture, and dynamics as well as the surface energy budget in MERRA-2 and M2AMIP will demonstrate that despite identical prescribed SSTs and sea ice in both versions, enhanced warming in the Arctic in MERRA-2 is in response to the analysis increment tendency due to temperature observations. Furthermore, the role of boundary conditions, model biases and changes in observing systems on the Arctic Amplification signal will be assessed. Literature on the topic of Arctic Amplification demonstrates that the enhanced warming begins in the mid-1990s. Anomalously warm Arctic SST in the early time period of MERRA-2 can mute the trend in Arctic lower troposphere temperature without the constraint of observations in M2AMIP. Additionally, MERRA-2 uses three distinct datasets of SST and sea ice concentration, which also plays a role.

Arcti↗
NASA NTRS2021

Reduced El NiñO Variability in the Mid-Pliocene According to the Pliomip2 Ensemble

The mid-Pliocene warm period (3.264–3.025 Ma) is the most recent geological period during which atmospheric CO 2 levels were similar to recent historical values (∼400 ppm). Several proxy reconstructions for the mid-Pliocene show highly reduced zonal sea surface temperature (SST) gradients in the tropical Pacific Ocean, indicating an El Niño-like mean state. However, past modelling studies do not show these highly reduced gradients. Efforts to understand mid-Pliocene climate dynamics have led to the Pliocene Model Intercomparison Project (PlioMIP). Results from the first phase (PlioMIP1) showed clear El Niño variability (albeit significantly reduced) and did not show the greatly reduced time-mean zonal SST gradient suggested by some of the proxies. In this work, we study El Niño–Southern Oscillation (ENSO) variability in the PlioMIP2 ensemble, which consists of additional global coupled climate models and updated boundary conditions compared to PlioMIP1. We quantify ENSO amplitude, period, spatial structure and “flavour”, as well as the tropical Pacific annual mean state in mid-Pliocene and pre-industrial simulations. Results show a reduced ENSO amplitude in the model-ensemble mean (−24 %) with respect to the pre-industrial, with 15 out of 17 individual models showing such a reduction. Furthermore, the spectral power of this variability considerably decreases in the 3–4-year band. The spatial structure of the dominant empirical orthogonal function shows no particular change in the patterns of tropical Pacific variability in the model-ensemble mean, compared to the pre-industrial. Although the time-mean zonal SST gradient in the equatorial Pacific decreases for 14 out of 17 models (0.2 ∘C reduction in the ensemble mean), there does not seem to be a correlation with the decrease in ENSO amplitude. The models showing the most “El Niño-like” mean state changes show a similar ENSO amplitude to that in the pre-industrial reference, while models showing more “La Niña-like” mean state changes generally show a large reduction in ENSO variability. The PlioMIP2 results show a reasonable agreement with both time-mean proxies indicating a reduced zonal SST gradient and reconstructions indicating a reduced, or similar, ENSO variability.

mid-Pliocene warm period↗
NASA NTRS2012

Global Modeling and Assimilation Office Annual Report and Research Highlights 2011-2012

Over the last year, the Global Modeling and Assimilation Office (GMAO) has continued to advance our GEOS-5-based systems, updating products for both weather and climate applications. We contributed hindcasts and forecasts to the National Multi-Model Ensemble (NMME) of seasonal forecasts and the suite of decadal predictions to the Coupled Model Intercomparison Project (CMIP5).

Rienecker, Michele M.↗
NASA NTRSDate not supplied

Inferred Sea Level Prediction in the NASA GMAO Seasonal Forecasting System

Reliable predictions of sea level anomalies on seasonal timescales with lead times of 1 to 9 months may have relevance to stakeholders – for example, in the advance deployment of resources for coastal flood mitigation. Routine prediction and analysis may also highlight physical processes associated with sea level change and modeling capabilities on seasonal and other timescales. These forecasts may represent interannual changes in the seasonal slope of the ocean surface, teleconnection effects such as the El Niño/Southern Oscillation phenomenon, and variations in seasonal hydrology including precipitation and coastal runoff. Coupled atmosphere/ocean models are routinely used in the seasonal prediction of temperature anomalies, precipitation anomalies, sea ice cover, and climate indices such as the Niño3.4 predictions under the North American Multi-Model Ensemble (NMME) protocol. Within the limits of their configuration, these complex Earth-system models have a potential for depicting regional changes in oceanic column properties, including the sea surface height. Seasonal prediction models generally have no representation of long-term mass contributions from melting land ice, or changes in vertical land motion; their output may be more specifically characterized as predictions of the ocean dynamic sea level. In practice however, the sea surface height prognostic variable is substantially compromised by the forecast model response to initial conditions. Imbalances between the initial, observed hydrologic cycle and the forecast model state produce abrupt adjustments in the model sea surface height. As a result, most seasonal prediction systems employ a constraint on the globally-averaged sea surface height that is applied at each time step. This essentially renders the prognostic sea surface height variable as unserviceable. Several approaches have previously been used to retrieve sea level information from seasonal forecasts beyond the use of the sea surface height variable. Here, we extend a method of relating other prognostic values, including ocean circulation and climate indices, to observed sea level variations. We use the merged altimetry record of the NASA MEaSUREs Gridded Sea Surface Height Anomalies data set and monthly revised local reference gauge observations from the National Oceanography Centre Permanent Service for Mean Sea Level (PSMSL) to evaluate derived prognostic variables from the NASA Global Modeling and Assimilation Office subseasonal-to-seasonal system version 2.1 (GMAO S2S v2.1). We focus on results for the midlatitudes with particular emphasis on US gauge locations. As shown in previous studies, prognostic ENSO-related indices in boreal winter are well correlated with gauge observations for the US west coast, but also for other locations in the southeastern US. Other forecast climate indices such as the North Atlantic Oscillation have relations to sea level that are limited both seasonally and spatially. As expected, surface atmospheric pressure (e.g., inverse barometer effect) is found to be particularly well correlated with observed sea level. We provide a characterization of forecast skill for seasonal sea level with this method.

Richard I Cullather↗
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 NTRS2013

A Single-column Model Ensemble Approach Applied to the TWP-ICE Experiment

Single-column models (SCM) are useful test beds for investigating the parameterization schemes of numerical weather prediction and climate models. The usefulness of SCM simulations are limited, however, by the accuracy of the best estimate large-scale observations prescribed. Errors estimating the observations will result in uncertainty in modeled simulations. One method to address the modeled uncertainty is to simulate an ensemble where the ensemble members span observational uncertainty. This study first derives an ensemble of large-scale data for the Tropical Warm Pool International Cloud Experiment (TWP-ICE) based on an estimate of a possible source of error in the best estimate product. These data are then used to carry out simulations with 11 SCM and two cloud-resolving models (CRM). Best estimate simulations are also performed. All models show that moisture-related variables are close to observations and there are limited differences between the best estimate and ensemble mean values. The models, however, show different sensitivities to changes in the forcing particularly when weakly forced. The ensemble simulations highlight important differences in the surface evaporation term of the moisture budget between the SCM and CRM. Differences are also apparent between the models in the ensemble mean vertical structure of cloud variables, while for each model, cloud properties are relatively insensitive to forcing. The ensemble is further used to investigate cloud variables and precipitation and identifies differences between CRM and SCM particularly for relationships involving ice. This study highlights the additional analysis that can be performed using ensemble simulations and hence enables a more complete model investigation compared to using the more traditional single best estimate simulation only.

numerical weather forecasting↗
NASA NTRS2016

A Model-Model and Data-Model Comparison for the Early Eocene Hydrological Cycle

A range of proxy observations have recently provided constraints on how Earth's hydrological cycle responded to early Eocene climatic changes. However, comparisons of proxy data to general circulation model (GCM) simulated hydrology are limited and inter-model variability remains poorly characterised. In this work, we undertake an intercomparison of GCM-derived precipitation and P - E distributions within the extended EoMIP ensemble (Eocene Modelling Intercomparison Project; Lunt et al., 2012), which includes previously published early Eocene simulations performed using five GCMs differing in boundary conditions, model structure, and precipitation-relevant parameterisation schemes. We show that an intensified hydrological cycle, manifested in enhanced global precipitation and evaporation rates, is simulated for all Eocene simulations relative to the preindustrial conditions. This is primarily due to elevated atmospheric paleo-CO2, resulting in elevated temperatures, although the effects of differences in paleogeography and ice sheets are also important in some models. For a given CO2 level, globally averaged precipitation rates vary widely between models, largely arising from different simulated surface air temperatures. Models with a similar global sensitivity of precipitation rate to temperature (dP=dT ) display different regional precipitation responses for a given temperature change. Regions that are particularly sensitive to model choice include the South Pacific, tropical Africa, and the Peri-Tethys, which may represent targets for future proxy acquisition. A comparison of early and middle Eocene leaf-fossil-derived precipitation estimates with the GCM output illustrates that GCMs generally underestimate precipitation rates at high latitudes, although a possible seasonal bias of the proxies cannot be excluded. Models which warm these regions, either via elevated CO2 or by varying poorly constrained model parameter values, are most successful in simulating a match with geologic data. Further data from low-latitude regions and better constraints on early Eocene CO2 are now required to discriminate between these model simulations given the large error bars on paleoprecipitation estimates. Given the clear differences between simulated precipitation distributions within the ensemble, our results suggest that paleohydrological data offer an independent means by which to evaluate model skill for warm climates.

Boundary conditions↗
NASA NTRS2018

File Specification for M2AMIP Products: Version Number - 1.0

The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) is an atmospheric reanalysis computed with the Goddard Earth Observing (EOS) System, Version 5.12.4 (GEOS) data assimilation system (Gelaro et al., 2017). To supplement the reanalysis, the GEOS General Circulation Model (GCM) used in MERRA-2 has been used to generate a 10-member ensemble of simulations, configured following the convention of the Atmospheric Model Intercomparison Project (AMIP; Gates et al., 1992). Each ensemble member was initialized using meteorological fields from a different date in November 1979. The AMIP simulations used the sea-surface temperature (SST) and sea-ice boundary conditions that were used in MERRA-2 (Bosilovich et al., 2016). This 10-member ensemble of AMIP simulations, denoted M2AMIP, is available for download in a group of self-describing files, which are documented in this office note. All data collections are provided on the same horizontal grid as MERRA-2. This grid has 576 points in the longitudinal direction and 361 points in the latitudinal direction, corresponding to a resolution of 0.625 degrees by 0.5 degrees. Although data collections are available at this grid, all fields are computed on a cubed-sphere grid with an approximate resolution of 50 km by 50 km and are then spatially interpolated to the latitude-longitude grid. There are no changes in the vertical grids used: variables are provided on either the native vertical grid of 72 model layers, or interpolated to 42 standard pressure levels. Unlike MERRA, no data collections are available at the vertical layer edges. More details on the grid are provided in Section 4. MERRA-2 introduced observation-based precipitation forcing for the land surface parameterization and the corresponding variable PRECTOTCORR in the MERRA-2 FLX (surface turbulent fluxes and related quantities) and LFO (land-surface forcing) collections (see Section 6; Reichle et al., 2017). While this variable is still available for M2AMIP, there was no observation-based forcing, making the value identical to the model derived precipitation, PRECTOT. Similarly, without data assimilation, the values for the analysis increments, D*DTANA, in the tendency and vertically integrated file collections are zero. The M2AMIP data are available for download online through the NASA Center for Climate Simulation (NCCS) DataPortal (https://portal.nccs.nasa.gov/datashare/gmao_m2amip/). Data are arranged in subdirectories based on ensemble member, followed by year and month. Control files that are compatible with the Grid Analysis and Display System (GrADS) are available in the ctl_daily and ctl_monthly directories for the hourly, three hourly, and monthly mean data. Control files for the monthly mean diurnal cycle can be found in the ctl_diurnal subdirectory within the directory for each individual ensemble member.

M2AMIP↗
NASA NTRS2002

Goddard Cumulus Ensemble (GCE) Model: Application for Understanding Precipitation Processes

One of the most promising methods to test the representation of cloud processes used in climate models is to use observations together with Cloud Resolving Models (CRMs). The CRMs use more sophisticated and realistic representations of cloud microphysical processes, and they can reasonably well resolve the time evolution, structure, and life cycles of clouds and cloud systems (size about 2-200 km). The CRMs also allow explicit interaction between out-going longwave (cooling) and incoming solar (heating) radiation with clouds. Observations can provide the initial conditions and validation for CRM results. The Goddard Cumulus Ensemble (GCE) Model, a cloud-resolving model, has been developed and improved at NASA/Goddard Space Flight Center over the past two decades. Dr. Joanne Simpson played a central role in GCE modeling developments and applications. She was the lead author or co-author on more than forty GCE modeling papers. In this paper, a brief discussion and review of the application of the GCE model to (1) cloud interactions and mergers, (2) convective and stratiform interaction, (3) mechanisms of cloud-radiation interaction, (4) latent heating profiles and TRMM, and (5) responses of cloud systems to large-scale processes are provided. Comparisons between the GCE model's results, other cloud-resolving model results and observations are also examined.

Tao, Wei-Kuo↗
NASA NTRS2021

Evaluating the Large-Scale Hydrological Cycle Response within the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2) Ensemble

The mid-Pliocene (∼3 Ma) is one of the most recent warm periods with high CO2 concentrations in the atmosphere and resulting high temperatures, and it is often cited as an analog for near-term future climate change. Here, we apply a moisture budget analysis to investigate the response of the large-scale hydrological cycle at low latitudes within a 13-model ensemble from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2). The results show that increased atmospheric moisture content within the mid-Pliocene ensemble (due to the thermodynamic effect) results in wetter conditions over the deep tropics, i.e., the Pacific intertropical convergence zone (ITCZ) and the Maritime Continent, and drier conditions over the subtropics. Note that the dynamic effect plays a more important role than the thermodynamic effect in regional precipitation minus evaporation (PmE) changes (i.e., northward ITCZ shift and wetter northern Indian Ocean). The thermodynamic effect is offset to some extent by a dynamic effect involving a northward shift of the Hadley circulation that dries the deep tropics and moistens the subtropics in the Northern Hemisphere (i.e., the subtropical Pacific). From the perspective of Earth's energy budget, the enhanced southward cross-equatorial atmospheric transport (0.22 PW), induced by the hemispheric asymmetries of the atmospheric energy, favors an approximately 1∘ northward shift of the ITCZ. The shift of the ITCZ reorganizes atmospheric circulation, favoring a northward shift of the Hadley circulation. In addition, the Walker circulation consistently shifts westward within PlioMIP2 models, leading to wetter conditions over the northern Indian Ocean. The PlioMIP2 ensemble highlights that an imbalance of interhemispheric atmospheric energy during the mid-Pliocene could have led to changes in the dynamic effect, offsetting the thermodynamic effect and, hence, altering mid-Pliocene hydroclimate.

mid-Pliocene↗
NASA NTRS2020

Impacts of 1.5 °C and 2.0 °C global warming above pre-industrial on potential winter wheat production of China

Keeping global temperatures below 2.0 °C above pre-industrial condition and pursuing efforts toward the more ambitious 1.5 °C goal in the late 21st century was the main target from the Paris Agreement in 2015. Here we assessed the likely challenges for the China’s winter wheat production under 1.5 °C and 2.0 °C increase of global temperature, with four wheat crop models (CERES-Wheat, Nwheat, WheatGrow, and APSIM-Wheat) and the latest climate projections from the Half a degree Additional warming, Prognosis and Projected Impacts project (HAPPI). Instead of using average “winter type” wheat cultivar, and same management and soil inputs for whole region, location-specific winter wheat cultivars with local agronomic information were calibrated for each of the representative wheat growing area of China, allowing a better spatial agronomic representation of the whole wheat planting area. The mean growing season temperature (GST) during the winter wheat vegetative stage was projected to increase by 0.6 to 1.4 °C for the 1.5 °C scenario, and 0.9 to 1.8 °C for the 2.0 °C scenario, while during the reproductive stage was decreased between 0 and 0.9 °C for the 1.5 °C scenario and -0.3 and 1.1 °C for the 2.0 °C scenario. Growing season duration (GSD) for the whole period was shortened by 6 to 15 days for the 1.5 °C scenario and 8 to 18 days for the 2.0 °C scenario, as a result of higher GST under global warming. Increase in GST and decrease in GSD was more obvious in the Southwest Subregion (SWS) than subregions in the north. The shortening GSD for the whole wheat growth period was mostly from the shortening vegetative period, as no appreciable difference in number of days from anthesis to maturity was found for the whole regions. Although there is variability among models, the indication is that wheat yields were projected to increase in the North Subregion (NS), the Huang-Huai Subregion (HHS), and the Middle-lower Researches of Yangzi River Subregion (MYS), but to decrease in the SWS under two warming scenarios. The effects of elevated CO2 concentration were mostly beneficial and tended to offset the negative impacts of increasing temperature at both global warming scenarios, with a rate of 7-14% yield increase per 100-ppm, except for locations with GST of baseline higher than 11 °C. Aggregating to regional wheat production, the total winter wheat production of China was projected to increase by 2.8% (1.6% to 3.0%, 25th percentile to 75th percentile) and 8.3% (7.0% to 9.6%, 25th percentile to 75th percentile) under 1.5 °C and 2.0 °C scenarios, and most of increase was observed in the north subregions due to the largest wheat planting area. Our results will lay the foundation for developing adaptation strategies to future climate change to ensure China and global wheat supply and food security.

Winter wheat↗
NASA NTRS2010

Application of Ensemble Detection and Analysis to Modeling Uncertainty in Non Stationary Process

Characterization of non stationary and nonlinear processes is a challenge in many engineering and scientific disciplines. Climate change modeling and projection, retrieving information from Doppler measurements of hydrometeors, and modeling calibration architectures and algorithms in microwave radiometers are example applications that can benefit from improvements in the modeling and analysis of non stationary processes. Analyses of measured signals have traditionally been limited to a single measurement series. Ensemble Detection is a technique whereby mixing calibrated noise produces an ensemble measurement set. The collection of ensemble data sets enables new methods for analyzing random signals and offers powerful new approaches to studying and analyzing non stationary processes. Derived information contained in the dynamic stochastic moments of a process will enable many novel applications.

Racette, Paul↗
NASA NTRS2013

A Canonical Repsonse of Precipitation Characteristics to Global Warming from CMIP5 Models

In this study, we find from analyses of projections of 14 CMIP5 models a robust, canonical global response in rainfall characteristics to a warming climate. Under a scenario of 1% increase per year of CO2 emission, the model ensemble projects globally more heavy precipitation (+7+/-2.4%/K1), less moderate precipitation (-2.5+/-0.6%/K), more light precipitation (+1.8+/-1.3%/K1), and increased length of dry (no-rain) periods (+4.7+/-2.1%/K). Regionally, a majority of the models project a consistent response with more heavy precipitation over climatologically wet regions of the deep tropics, especially the equatorial Pacific Ocean and the Asian monsoon regions, and more dry periods over the land areas of the subtropics and the tropical marginal convective zones. Our results suggest that increased CO2 emissions induce a global adjustment in circulation and moisture availability manifested in basic changes in global precipitation characteristics, including increasing risks of severe floods and droughts in preferred geographic locations worldwide.

CMIP5↗