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DOE OSTI2021

Are 100 Ensemble Members Enough to Capture the Remote Atmospheric Response to +2°C Arctic Sea Ice Loss?

This study presents results from the Polar Amplification Multimodel Intercomparison Project (PAMIP) single-year time-slice experiments that aim to isolate the atmospheric response to Arctic sea ice loss at global warming levels of +2°C. Using two general circulation models (GCMs), the ensemble size is increased up to 300 ensemble members, beyond the recommended 100 members. After partitioning the response in groups of 100 ensemble members, the reproducibility of the results is evaluated, with a focus on the response of the midlatitude jet streams in the North Atlantic and North Pacific. Both atmosphere-only and coupled ocean–atmosphere PAMIP experiments are analyzed. Substantial differences in the midlatitude response are found among the different experiment subsets, suggesting that 100-member ensembles are still significantly influenced by internal variability, which can mislead conclusions. Despite an overall stronger response, the coupled ocean–atmosphere runs exhibit greater spread due to additional ENSO-related internal variability when the ocean is interactive. The lack of consistency in the response is true for anomalies that are statistically significant according to Student’s t and false discovery rate tests. This is problematic for the multimodel assessment of the response, as some of the spread may be attributed to different model sensitivities whereas it is due to internal variability. Here, we propose a method to overcome this consistency issue that allows for more robust conclusions when only 100 ensemble members are used.

58 GEOSCIENCES↗

DOE OSTI2021

Assessing dynamic vegetation model parameter uncertainty across Alaskan arctic tundra plant communities

Abstract As the Arctic region moves into uncharted territory under a warming climate, it is important to refine the terrestrial biosphere models (TBMs) that help us understand and predict change. One fundamental uncertainty in TBMs relates to model parameters, configuration variables internal to the model whose value can be estimated from data. We incorporate a version of the Terrestrial Ecosystem Model (TEM) developed for arctic ecosystems into the Predictive Ecosystem Analyzer (PEcAn) framework. PEcAn treats model parameters as probability distributions, estimates parameters based on a synthesis of available field data, and then quantifies both model sensitivity and uncertainty to a given parameter or suite of parameters. We examined how variation in 21 parameters in the equation for gross primary production influenced model sensitivity and uncertainty in terms of two carbon fluxes (net primary productivity and heterotrophic respiration) and two carbon (C) pools (vegetation C and soil C). We set up different parameterizations of TEM across a range of tundra types (tussock tundra, heath tundra, wet sedge tundra, and shrub tundra) in northern Alaska, along a latitudinal transect extending from the coastal plain near Utqiaġvik to the southern foothills of the Brooks Range, to the Seward Peninsula. TEM was most sensitive to parameters related to the temperature regulation of photosynthesis. Model uncertainty was mostly due to parameters related to leaf area, temperature regulation of photosynthesis, and the stomatal responses to ambient light conditions. Our analysis also showed that sensitivity and uncertainty to a given parameter varied spatially. At some sites, model sensitivity and uncertainty tended to be connected to a wider range of parameters, underlining the importance of assessing tundra community processes across environmental gradients or geographic locations. Generally, across sites, the flux of net primary productivity (NPP) and pool of vegetation C had about equal uncertainty, while heterotrophic respiration had higher uncertainty than the pool of soil C. Our study illustrates the complexity inherent in evaluating parameter uncertainty across highly heterogeneous arctic tundra plant communities. It also provides a framework for iteratively testing how newly collected field data related to key parameters may result in more effective forecasting of Arctic change.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1983

Capabilities and limitations of existing MST radars: Poker Flat

Designed as a prototype system to continuously monitor the atmosphere up to approximately 100 km, the Poker Flat MST radar began operating in 1979 at a relatively low sensitivity. In almost continuous operation since then, the system is steadily increasing in sensitivity to its ultimate design characteristics. Current and final parameters are listed. The advantages of its modular design, which uses 64 transmitting modules distributed through the 200 mx 200 m antenna array include: easy maintenance, beam switching using very low power switching, air cooled transmitting tubes, lower feedline costs, and no moving parts. Continuous, uninterrupted operation ( 4 years) and less man-made interference because of the remote location) are other assets. Most disadvantages are related to its not-yet-finished status, climate, moose excursions, and operating expenses.

Balsley, B. B.↗

DOE OSTI2022

A generalist–specialist trade-off between switchgrass cytotypes impacts climate adaptation and geographic range

Polyploidy results from whole-genome duplication and is a unique form of heritable variation with pronounced evolutionary implications. Different ploidy levels, or cytotypes, can exist within a single species, and such systems provide an opportunity to assess how ploidy variation alters phenotypic novelty, adaptability, and fitness, which can, in turn, drive the development of unique ecological niches that promote the coexistence of multiple cytotypes. Switchgrass, Panicum virgatum, is a widespread, perennial C4 grass in North America with multiple naturally occurring cytotypes, primarily tetraploids (4×) and octoploids (8×). Using a combination of genomic, quantitative genetic, landscape, and niche modeling approaches, we detect divergent levels of genetic admixture, evidence of niche differentiation, and differential environmental sensitivity between switchgrass cytotypes. Taken together, these findings support a generalist (8×)–specialist (4×) trade-off. Our results indicate that the 8× represent a unique combination of genetic variation that has allowed the expansion of switchgrass’ ecological niche and thus putatively represents a valuable breeding resource.

09 BIOMASS FUELS↗

DOE OSTI2020

A Multiyear Gridded Data Ensemble of Surface Biogenic Carbon Fluxes for North America: Evaluation and Analysis of Results

Accurate and fine-scale estimates of biogenic carbon fluxes are critical for measuring and monitoring the biosphere's responses and feedback to the climate system. Currently available data products from flux towers and model-intercomparison projects struggle to adequately represent spatiotemporal dynamics of surface biogenic carbon fluxes, and to quantify their uncertainties, which also are crucial to atmospheric inversion systems. To address these gaps, we introduce a new perturbed-parameter model ensemble with the CASA model to estimate surface biogenic carbon fluxes at monthly and 3-hourly scales for North America at ~500-m and 5-km resolutions. We first use the Extended Fourier Amplitude Sensitivity Testing to choose the three most sensitive parameters to be perturbed, maximum light use efficiency (E max ), optimal temperature of photosynthesis (T opt ), and temperature response of respiration (Q 10 ). The initial range for each parameter is broadly sampled for the L1 ensemble, but then we pruned E max with site-level primary productivity to derive an L2 ensemble with narrower uncertainty ranges. Ensembles are strongly correlated with site-level results at both monthly and 3-hourly scales, and the spread across L1/L2 ensemble members encompasses the range of AmeriFlux observations. Monthly variability in the L2 ensemble mean is 85% of the observed variability. The L2 ensemble outperforms diverse data products with the highest Taylor skill scores at diurnal to annual scales. Finally, the ensemble's seasonality agrees well with other models for most biome types and in high and middle latitudes, but inconsistencies are found in subtropical and tropical ecoregions and for annual totals over North America.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

PastoralScape: An Environment-Driven Model of Vaccination Decision Making Within Pastoralist Groups in East Africa

Economic and cultural resilience among pastoralists in East Africa is threatened by the interconnected forces of climate change, contagious diseases spread and evolving national and international trade. A key factor in the resilience of livestock that communities depend on is human decision making regarding vaccination against prevalent diseases such as Rift Valley fever and Contagious Bovine Pleuropneumonia. This paper describes an agent-based model that couples models of disease propagation, animal health, human decision making, and external GIS data sources capturing measures of foraging condition. We describe the design of the sub-models, their coupling, and demonstrate the sensitivity of the model to parameters that relate to controllable factors such as government and NGO information sources that can influence human decision making patterns. This model is intended to form the basis upon which richer economic and human factor models can be built.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1999

Using Spaceborne Ku-Band Scatterometer for Global Snow Cover Monitoring

We demonstrate for the first time the utility of spaceborne Ku-band scatterometer for global snow cover monitoring. Satellite radar data were collected over the globe by the NASA Scatterometer (NSCAT) operated at 14 GHz on board the Japanese ADEOS spacecraft from September 1996 to June 1997, spanning the 1997 seasonal snow season. First, we present backscatter signature of dry and wet snow to facilitate the interpretation of NSCAT backscatter evolution over snow cover regions. Surface field experiments indicated that dry snow backscatter at Ku band is approximately 40 times stronger than that at C band. Thus, Ku-band scatterometer measurements are sensitive to snow cover, which is typically transparent to C-band scatterometer returns. Furthermore, Ku-band backscatter does not saturate for most of natural snow depths as compared to radar responses at 19 GHz and 37 GHz or higher frequencies which have more limited penetration depths into snow. Ku-band backscatter is also sensitive to wetness in snow, which is appropriate to detect early snow melt conditions. Using the snow backscatter characteristics, we investigate NSCAT backscatter evolution over global snow cover regions throughout the 1997 snow season. The results reveal detail delineations between different regional snow areas. We show the correlation of these delineations with the boundaries of different global snow classes defined by the U.S. Army Cold Regions Research and Engineering Laboratory snow classification system. Using in-situ snow depth data from the U.S. National Climatic Data Center, we show that Ku-band backscatter corresponds very well to the trend of snow melt while snow mapping products (U.S. Climate Prediction Center gridded snow charts) from visible sensors does not reflect the fast snow melt trend. To illustrate the practical application of global snow monitoring with spaceborne Ku-band scatterometer, we present NSCAT backscatter response corresponding to the snow event leading to the 1997 Flood of the Century over the U.S. Northern plains and the Canadian prarie region, which caused loss of lives and several billion dollars in flood related damages and cleanup costs. Finally, we show that the fixed incidence configuration of Seawinds scatterometers, to be launched on QuickSCAT and on ADEOS-2 in the near future, is better for snow monitoring and then discuss the use of future high-resolution scatterometers for global snow mapping.

Nghiem, S. V.↗

NASA NTRS2014

Millimeter- and Submillimeter-Wave Remote Sensing Using Small Satellites

Cloud ice properties and processes play fundamental roles in atmospheric radiation and precipitation. Limited knowledge and poor representation of clouds in global climate models have led to large uncertainties about cloud feedback processes under climate change. Ice clouds have been used as a tuning parameter in the models to force agreement with observations of the radiation budget at the top of the atmosphere, and precipitation at the bottom. The lack of ice cloud measurements has left the cloud processes at intermediate altitudes unconstrained. Millimeter (mm) and submillimeter (submm)-wave radiometry is widely recognized for its potential to fill the cloud measurement gap in the middle and upper troposphere. Analyses have shown that channels from 183900 GHz offer good sensitivity to ice cloud scattering and can provide ice water path (IWP) products to an accuracy of 25 by simultaneously retrieving ice particle size (Dme) and IWP. Therefore, it is highly desirable to develop a cost-effective, compact mm/submm-wave instrument for cloud observations that can be deployed on future small satellites.This paper presents a conceptual study for a mm/submm-wave instrument for multispectral measurements of ice clouds. It discusses previous work at these frequencies by NASA Goddard Space Flight Center (GSFC) and the current instrument study, as well as receiver architectures and their anticipated performance. And finally, it describes a microsatellite prototype intended for use with this mm/submm-wave instrument.

small satellites↗

DOE OSTI2025

Coupled Lake‐Atmosphere‐Land Physics Uncertainties in a Great Lakes Regional Climate Model

Abstract This study develops a surrogate‐based method to assess the uncertainty within a convective permitting integrated modeling system of the Great Lakes region, arising from interacting physics parameterizations across the lake, atmosphere, and land surface. Perturbed physics ensembles of the model during the 2018 summer are used to train a neural network surrogate model to predict lake surface temperature (LST) and near‐surface air temperature (T2m). Average physics uncertainties are determined to be 1.5C for LST and T2m over land, and 1.9C for T2m over lake, but these have significant spatiotemporal variations. We find that atmospheric physics parameterizations alone are the dominant sources of uncertainty (45%–53%), while lake and land parameterizations account for 33% and 38% of the uncertainty of LST and T2m over land respectively. Interactions of atmosphere physics parameterizations with those of the land and lake contribute to an additional 13%–17% of the total variance. LST and T2m over the lake are more uncertain in the deeper northern lakes, particularly during the rapid warming phase that occurs in late spring/early summer. The LST uncertainty increases with sensitivity to the lake model's surface wind stress scheme. T2m over land is more uncertain over forested areas in the north, where it is most sensitive to the land surface model, than the more agricultural land in the south, where it is most sensitive to the atmospheric planetary boundary and surface layer scheme. Uncertainty also increases in the southwest during multiday temperature declines with higher sensitivity to the land surface model.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Robust global detection of forced changes in mean and extreme precipitation despite observational disagreement on the magnitude of change

Detection and attribution (D&A) of forced precipitation change are challenging due to internal variability, limited spatial, and temporal coverage of observational records and model uncertainty. These factors result in a low signal-to-noise ratio of potential regional and even global trends. Here, we use a statistical method – ridge regression – to create physically interpretable fingerprints for the detection of forced changes in mean and extreme precipitation with a high signal-to-noise ratio. The fingerprints are constructed using Coupled Model Intercomparison Project phase 6 (CMIP6) multi-model output masked to match coverage of three gridded precipitation observational datasets – GHCNDEX, HadEX3, and GPCC – and are then applied to these observational datasets to assess the degree of forced change detectable in the real-world climate in the period 1951–2020. We show that the signature of forced change is detected in all three observational datasets for global metrics of mean and extreme precipitation. Forced changes are still detectable from changes in the spatial patterns of precipitation even if the global mean trend is removed from the data. This shows the detection of forced change in mean and extreme precipitation beyond a global mean trend is robust and increases confidence in the detection method's power as well as in climate models' ability to capture the relevant processes that contribute to large-scale patterns of change. We also find, however, that detectability depends on the observational dataset used. Not only coverage differences but also observational uncertainty contribute to dataset disagreement, exemplified by the times of emergence of forced change from internal variability ranging from 1998 to 2004 among datasets. Furthermore, different choices for the period over which the forced trend is computed result in different levels of agreement between observations and model projections. These sensitivities may explain apparent contradictions in recent studies on whether models under- or overestimate the observed forced increase in mean and extreme precipitation. Lastly, the detection fingerprints are found to rely primarily on the signal in the extratropical Northern Hemisphere, which is at least partly due to observational coverage but potentially also due to the presence of a more robust signal in the Northern Hemisphere in general.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Thermal modeling of three lakes within the continuous permafrost zone in Alaska using the LAKE 2.0 model

Abstract. Lakes in the Arctic are important reservoirs of heat with much lower albedo in summer and greater absorption of solar radiation than surrounding tundra vegetation. In the winter, lakes that do not freeze to their bed have a mean annual bed temperature >0 ∘C in an otherwise frozen landscape. Under climate warming scenarios, we expect Arctic lakes to accelerate thawing of underlying permafrost due to warming water temperatures in the summer and winter. Previous studies of Arctic lakes have focused on ice cover and thickness, the ice decay process, catchment hydrology, lake water balance, and eddy covariance measurements, but little work has been done in the Arctic to model lake heat balance. We applied the LAKE 2.0 model to simulate water temperatures in three Arctic lakes in northern Alaska over several years and tested the sensitivity of the model to several perturbations of input meteorological variables (precipitation, shortwave radiation, and air temperature) and several model parameters (water vertical resolution, sediment vertical resolution, depth of soil column, and temporal resolution). The LAKE 2.0 model is a one-dimensional model that explicitly solves vertical profiles of water state variables on a grid. We used a combination of meteorological data from local and remote weather stations, as well as data derived from remote sensing, to drive the model. We validated modeled water temperatures with data of observed lake water temperatures at several depths over several years for each lake. Our validation of the LAKE 2.0 model is a necessary step toward modeling changes in Arctic lake ice regimes, lake heat balance, and thermal interactions with permafrost. The sensitivity analysis shows us that lake water temperature is not highly sensitive to small changes in air temperature or precipitation, while changes in shortwave radiation and large changes in precipitation produced larger effects. Snow depth and lake ice strongly affect water temperatures during the frozen season, which dominates the annual thermal regime of Arctic lakes. These findings suggest that reductions in lake ice thickness and duration could lead to more heat storage by lakes and enhanced permafrost degradation.

Clark, Jason A. (ORCID:0000000248105261)↗

NASA NTRS1993

Polarimetric thermal emission from periodic water surfaces

Experimental results and theoretical calculations are presented to study the polarimetric emission from water surfaces with directional features. For our ground-based Ku-band radiometer measurements, a water pool was constructed on the roof of a building in the Jet Propulsion Laboratory, and a fiberglass surface with periodic corrugations in one direction was impressed on the top of the water surface to create a stationary water surface underneath it. It is observed that the measured Stokes parameters of corrugated fiberglass-covered water surfaces are functions of azimuth angles and agree very well with the theoretical calculations. The theory, after being verified by the experimental data, was then used to calculate the Stokes parameters of periodic surfaces without fiberglass surface layer and with rms height of the order of wind-generated water ripples. The magnitudes of the azimuthal variation of the calculated emissivities at horizontal and vertical polarizations corresponding to the first two Stokes parameters are found to be comparable to the values measured by airborne radiometers and SSM/I. In addition, the third Stokes parameter not shown in the literature is seen to have approximately twice the magnitude of the azimuth variation of either T(sub h) or T(sub v), which may make it more sensitive to the row direction, while less susceptive to noises because the atmospheric and system noises tend to be unpolarized and are expected to be cancelled out when the third Stokes parameter is derived as the difference of two or three power measurements, as indicated by another experiment carried out at a swimming pool with complicated surroundings. The results indicate that passive polarimetry is a potential technology in the remote sensing of ocean wind vector which is a crucial component in the understanding of global climate change. Issues related to the application of microwave passive polarimetry to ocean wind are also discussed.

Yueh, S. H.↗

NASA NTRS2004

Tropical Diabatic Heating and the Role of Convective Processes as Represented in Several Contemporary Climate Models

One of the most fundamental properties of the global heat balance is the net heat input into the tropical atmosphere that helps drive the planetary atmospheric circulation. Although broadly understood in terms of its gross structure and balance of source / sink terms, incorporation of the relevant processes in predictive models is still rather poor. The work reported here examines the tropical radiative and water cycle behavior as produced by four contemporary climate models. Among these are the NSIPP-2 (NASA Seasonal to Interannual Prediction Project) which uses the RAS convective parameterization; the FVCCM, a code using finite volume numerics and the CCM3.6 physics; FVCCM-MCRAS again having the finite volume numerics, but MCRAS convective parameterization and a different radiation treatment; and, finally, the NCEP GSM which uses the RAS. Using multi-decadal integrations with specified SSTs we examine the statistics of radiative / convective processes and associated energy transports, and then estimate model energy flux sensitivities to SST changes. In particular the behavior of the convective parameterizations is investigated. Additional model integrations are performed specifically to assess the importance representing convective inhibition in regulating convective cloud-top structure and moisture detrainment as well as controlling surface energy fluxes. To evaluate the results of these experiments, a number of satellite retrievals are used: TRMM retrievals of vertical reflectivity structure, rainfall rate, and inferred diabatic heating are analyzed to show both seasonal and interannual variations in vertical structure of latent heat release. Top-of-atmosphere radiative fluxes from ERBS and CERES are used to examine shortwave and longwave cloud forcing and to deduce required seasonal energy transports. Retrievals of cloud properties from ISCCP and water vapor variations from SSM/T-2 are also used to understand behavior of the humidity fields. These observations are supplemented with output form the DOE Reanalysis-2.

Robertson, Franklin R.↗

NASA NTRS2012

Performance of McRAS-AC in the GEOS-5 AGCM: Part 1, Aerosol-Activated Cloud Microphysics, Precipitation, Radiative Effects, and Circulation

A revised version of the Microphysics of clouds with Relaxed Arakawa-Schubert and Aerosol-Cloud interaction (McRAS-AC), including, among others, the Barahona and Nenes ice nucleation parameterization, is implemented in the GEOS-5 AGCM. Various fields from a 10-year long integration of the AGCM with McRAS-AC were compared with their counterparts from an integration of the baseline GEOS-5 AGCM, and with satellite data as observations. Generally using McRAS-AC reduced biases in cloud fields and cloud radiative effects are much better over most of the regions of the Earth. Two weaknesses are identified in the McRAS-AC runs, namely, too few cloud particles around 40S-60S, and too high cloud water path during northern hemisphere summer over the Gulf Stream and North Pacific. Sensitivity analyses showed that these biases potentially originated from biases in the aerosol input. The first bias is largely eliminated in a sensitivity test using 50% smaller aerosol particles, while the second bias is much reduced when interactive aerosol chemistry was turned on. The main drawback of McRAS-AC is dearth of low-level marine stratus clouds, probably due to lack of dry-convection, not yet implemented into the cloud scheme. Despite these biases, McRAS-AC does simulate realistic clouds and their optical properties that can improve with better aerosol-input and thereby has the potential to be a valuable tool for climate modeling research because of its aerosol indirect effect simulation capabilities involving prediction of cloud particle number concentration and effective particle size for both convective and stratiform clouds is quite realistic.

Sud, Y. C.↗

NASA NTRS2022

Simulation of a Future SLR Satellite to Improve Low-Degree Gravity Estimates

The Gravity Recovery and Climate Experiment (GRACE; 2002-2017) and GRACE Follow-On (2018-present) have observed Earth's monthly mass change with unprecedented spatial resolution. These missions have long relied on satellite laser ranging (SLR) measurements to replace the C 2,0 coefficient, which GRACE recovers poorly. Recent work has also shown the need for SLR-determined C 3,0 when GRACE operates with a single accelerometer. However, it was not until the 2012 launch of the Laser Relativity Satellite that the SLR data gained the sensitivity to recover C 3,0 accurately. These low-degree gravity coefficients represent large-scale mass transport and small changes in their values have implications for ice sheet, ocean mass, and water storage estimates. To fully exploit SLR's utility for time-variable gravity (TVG), future satellite orbits should be selected to maximize their sensitivity to the gravity field. In this work, we present results from a simulation study of a hypothetical SLR satellite in which we generate 1 year of data to satellites placed across varying inclinations. We also simulate seven current SLR satellites to show realistic improvements from the new satellite. When compared to the known truth input, a low-inclination satellite ( <~ 45°) most improves the low-degree gravity terms, especially the even zonals which show a significant decorrelation. From this, we investigate recovery of the annual variability in the simulated signal and find recovery of the sine component improves by up to 41%. This has important implications when considering future SLR satellites in the context of TVG.

Satellite laser ranging↗

DOE OSTI2024

RuralAI in Tomato Farming: Integrated Sensor System, Distributed Computing, and Hierarchical Federated Learning for Crop Health Monitoring

Precision horticulture is evolving due to scalable sensor deployment and machine learning (ML) integration. These advancements boost the operational efficiency of individual farms, balancing the benefits of analytics with autonomy requirements. However, given concerns that affect wide geographic regions (e.g., climate change), there is a need to apply models that span farms. Federated learning (FL) has emerged as a potential solution. FL enables decentralized ML across different farms without sharing private data. Traditional FL assumes simple two-tier network topologies and, thus, falls short of operating on more complex networks found in real-world agricultural scenarios. Networks vary across crops and farms and encompass various sensor data modes, extending across jurisdictions. New hierarchical FL (HFL) approaches are needed for more efficient and context-sensitive model sharing, accommodating regulations across multiple jurisdictions. Here, we present the RuralAI architecture deployment for tomato crop monitoring, featuring sensor field units for soil, crop, and weather data collection. HFL with personalization is used to offer localized and adaptive insights. Model management, aggregation, and transfers are facilitated via a flexible approach, enabling seamless communication between local devices, edge nodes, and the cloud.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1999

Determining the Relationship between the Total and Window Channel Nighttime Radiances for the CERES Instrument

Observing Earth s radiant energy budget from space is critical to improving our understanding of Earth s climate system. The Earth Radiation Budget Experiment (ERBE) was the first initiative to provide simultaneous observations of Earth s radiant energy with identical instruments flying aboard separate satellites. The design of the ERBE instrument was based upon three complementary broadband radiometers which measured the shortwave (< 5 mm), longwave (> 5 mm), and total regions of the spectrum. Since any two of the ERBE radiometers could be used to simulate the third, a three channel intercomparison, based on redundancy, was available to uncover any changes in the relative sensitivities of the individual radiometers. Such a three channel intercomparison thus provided confidence in the application of the ERBE measurements over the lifetime of the instrument while mitigating the concern over instrument degradation.

Kratz, David P.↗

NASA NTRSDate not supplied

Regional and Model-Specific Response Types in A Global Gridded Crop Model Ensemble

Crop models are often employed to project crop yields under changing conditions such as global warming and associated management change for adaptation. Multi-model ensembles are promoted to enhance the robustness of projections, but questions remain on what causes often large differences between projections of individual models. Global Gridded Crop Models (GGCMs) are especially exposed to this question when applied for assessing climate change impacts, adaptation, environmental impacts of agricultural production, because their results are used in downstream analyses, such as in integrated assessment or economic modeling for projecting future land-use change. Even though global gridded crop models are often based on detailed field-scale models or have implemented similar modeling principles in other ecosystem models, global-scale models are subject to substantial uncertainties from both model structure and parametrization as well as from calibration and input data quality. AgMIP’s Global Gridded Crop Model Intercomparison (GGCMI) has thus set out to intercompare GGCMs in order to evaluate model performance, describe model uncertainties, identify inconsistencies within the ensemble and underlying reasons, and to ultimately improve models and modeling capacities. In phase 2 of the GGCMI activities, 12 modeling groups followed a modeling protocol that asked for up to 1404 31-year global simulations at 0.5 arc-degree spatial resolution to assess models’ sensitivities to changes in carbon dioxide (C; 4 different levels) temperature (T; 7 different offset levels), water supply (W; 9 levels), and nitrogen (N; 3 levels), the so-called CTWN experiment (Franke et al. 2020; http://dx.doi.org/10.5194/gmd-13-2315-2020). We here present analyses of model response types using impact response surfaces along the C, T, W, and N dimensions, respectively and collectively. Doing so, we can understand differences in simulated responses per driver rather than aggregated changes in yields. We find that models’ sensitivities to the individual driver dimensions are substantially different and often more different across models than across regions. A cluster analysis finds regional and model-specific patterns. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response type clusters across models suggests that models need to undergo further scrutiny. We suggest establishing standards in model process evaluation not only against historical dynamics but also against dedicated experiments across the CTWN dimensions.

crop models↗