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NASA NTRS2015

Introduction The Role of the Agricultural Model Intercomparison and Improvement Project

Climate impacts on agriculture are of increasing concern in both the scientific and policy communities because of the need to ensure food security for a growing population. A special challenge is posed by the changes in the frequency and intensity of heat-waves, droughts, and episodic rainstorms already underway in many parts of the world. Changes in production are directly linked to such variations in temperature and precipitation during the growing season, and often to offseason changes in weather affecting soil-water storage and availability to crops. This is not an isolated problem but one of both global and regional importance, because of impacts on the livelihoods of smallholder farmers as well as consequences for the world food trade system. This two-part set the Agricultural Model Intercomparison and Improvement Project (AgMIP): Integrated Crop and Economic Assessments is the first to be entirely devoted to AgMIP (www.agmip.org). AgMIP is a major international research program focused on climate change and agriculture. The goal of the two parts is to advance the field by providing detailed information on new simulation techniques and assessments being conducted by this program. It presents information about new methods of global and regional integrated assessment, results from agricultural regions, and adaptation strategies for maintaining food security under changing climate conditions.

security↗
DOE OSTI2024

A protocol for model intercomparison of impacts of marine cloud brightening climate intervention

A modeling protocol (defined by a series of climate model simulations with specified model output) is introduced. Studies using these simulations are designed to improve the understanding of climate impacts using a strategy for climate intervention (CI) known as marine cloud brightening (MCB) in specific regions; therefore, the protocol is called MCB-REG (where REG stands for region). The model simulations are not intended to assess consequences of a realistic MCB deployment intended to achieve specific climate targets but instead to expose responses to interventions in six regions with pervasive cloud systems that are often considered candidates for such a deployment. A calibration step involving simulations with fixed sea surface temperatures (SSTs) is first used to identify a common forcing, and then coupled simulations with forcing in individual regions and combinations of regions are used to examine climate impacts. Synthetic estimates constructed by superposing responses from simulations with forcing in individual regions are considered a means of approximating the climate impacts produced when MCB interventions are introduced in multiple regions. A few results comparing simulations from three modern climate models (CESM2, E3SMv2, and UKESM1) are used to illustrate the similarities and differences between model behavior and the utility of estimates of MCB climate responses that were synthesized by summing responses introduced in individual regions. Cloud responses to aerosol injections differ substantially between models (CESM2 clouds appear much more susceptible to aerosol emissions than the other models), but patterns in precipitation and surface temperature responses were similar when forcing is imposed with similar amplitudes in the same regions. A previously identified La Niña-like response to forcing introduced in the Southeast Pacific is evident in this study, but the amplitude of the response was shown to markedly differ across the three models. Other common response patterns were also found and are discussed. Forcing in the Southeast Atlantic consistently (across all three models) produces weaker global cooling than that in other regions, and the Southeast Pacific and South Pacific show the strongest cooling. This indicates that the efficiency of a given intervention depends on not only the susceptibility of the clouds to aerosol perturbations, but also the strength of the underlying radiative feedbacks and ocean responses operating within each region. These responses were generally robust across models, but more studies and an examination of responses with ensembles would be beneficial.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

Single-column Model Intercomparison for a Stably Stratified Atmospheric Boundary Layer

The parameterization of the stably stratified atmospheric boundary layer is a difficult issue, which has a large impact on the medium-range weather forecasts and on climate integrations. A non-strongly stratified arctic case is simulated by nineteen single-column turbulence schemes. The statistics from the Large-eddy simulation (LES) intercomparison made for the same case by eight different models are used as a guiding reference. The single-column parameterizations include research schemes and operational schemes from major forecast and climate research centres. First order schemes, a large number of turbulence kinetic energy closures, and other proposals have submitted results. There is a large spread in the results; in general, the operational schemes mix more efficiently than the research ones, and the TKE and other higher order closures give results closer to the LES statistics. The sensitivities of the schemes to the parameters of their turbulence closures are partially explored.

Cuxart, J.↗
DOE OSTI2021

Long-term single-column model intercomparison of diurnal cycle of precipitation over midlatitude and tropical land

General Circulation Models (GCMs) have for decades exhibited difficulties in modelling the diurnal cycle of precipitation (DCP). This issue can be related to inappropriate representation of the processes controlling sub-diurnal phenomena like convection. In this study, 11 single-column versions of GCMs are used to investigate the interactions between convection and environmental conditions, processes that control nocturnal convections, and the transition from shallow to deep convection on a diurnal time-scale. Long-term simulations are performed over two continental land sites: the Southern Great Plains (SGP) in the USA for 12 summer months from 2004 to 2015 and the Manacapuru site at the central Amazon (MAO) in Brazil for two full years from 2014 to 2015. The analysis is done on two regimes: afternoon convective regime and nocturnal precipitation regime. Most models produce afternoon precipitation too early, likely due to the missing transition of shallow-to-deep convection in these models. At SGP, the unified convection schemes better simulate the onset time of precipitation. At MAO, models produce the heating peak in a much lower level compared with observation, indicating too shallow convection in the models. For nocturnal precipitation, models that produce most of nocturnal precipitation all allow convection to be triggered above the boundary layer. This indicates the importance of model capability to detect elevated convection for simulating nocturnal precipitation. Sensitivity studies indicate that (a) nudging environmental variables towards observations has a minor impact on DCP, (b) unified treatment of shallow and deep convection and the capability to capture mid-level convection can help models better capture DCP, and (c) the interactions of the atmosphere with other components in the climate system (e.g. land) are also important for DCP simulations in coupled models. Finally, these results provide long-term statistical insights on which physical processes are essential in climate models to simulate DCP.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2014

Evaluation of Cloud-Resolving and Limited Area Model Intercomparison Simulations Using TWP-ICE Observations: Precipitation Microphysics - Part 2

Ten 3-D cloud-resolving model (CRM) simulations and four 3-D limited area model (LAM) simulations of an intense mesoscale convective system observed on 23-24 January 2006 during the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) are compared with each other and with observations and retrievals from a scanning polarimetric radar, colocated UHF and VHF vertical profilers, and a Joss-Waldvogel disdrometer in an attempt to explain a low bias in simulated stratiform rainfall. Despite different forcing methodologies, similar precipitation microphysics errors appear in CRMs and LAMs with differences that depend on the details of the bulk microphysics scheme used. One-moment schemes produce too many small raindrops, which biases Doppler velocities low, but produces rainwater contents (RWCs) that are similar to observed. Two-moment rain schemes with a gamma shape parameter (mu) of 0 produce excessive size sorting, which leads to larger Doppler velocities than those produced in one-moment schemes but lower RWCs. Two-moment schemes also produce a convective median volume diameter distribution that is too broad relative to observations and, thus, may have issues balancing raindrop formation, collision-coalescence, and raindrop breakup. Assuming a mu of 2.5 rather than 0 for the raindrop size distribution improves one-moment scheme biases, and allowing mu to have values greater than 0 may improve excessive size sorting in two-moment schemes. Underpredicted stratiform rain rates are associated with underpredicted ice water contents at the melting level rather than excessive rain evaporation, in turn likely associated with convective detrainment that is too high in the troposphere and mesoscale circulations that are too weak. A limited domain size also prevents a large, well-developed stratiform region like the one observed from developing in CRMs, although LAMs also fail to produce such a region.

three dimensional models↗
DOE OSTI2021

Understanding water and energy fluxes in the Amazonia: Lessons from an observation‐model intercomparison

Abstract Tropical forests are an important part of global water and energy cycles, but the mechanisms that drive seasonality of their land‐atmosphere exchanges have proven challenging to capture in models. Here, we (1) report the seasonality of fluxes of latent heat (LE), sensible heat ( H ), and outgoing short and longwave radiation at four diverse tropical forest sites across Amazonia—along the equator from the Caxiuanã and Tapajós National Forests in the eastern Amazon to a forest near Manaus, and from the equatorial zone to the southern forest in Reserva Jaru; (2) investigate how vegetation and climate influence these fluxes; and (3) evaluate land surface model performance by comparing simulations to observations. We found that previously identified failure of models to capture observed dry‐season increases in evapotranspiration (ET) was associated with model overestimations of (1) magnitude and seasonality of Bowen ratios (relative to aseasonal observations in which sensible was only 20%–30% of the latent heat flux) indicating model exaggerated water limitation, (2) canopy emissivity and reflectance (albedo was only 10%–15% of incoming solar radiation, compared to 0.15%–0.22% simulated), and (3) vegetation temperatures (due to underestimation of dry‐season ET and associated cooling). These partially compensating model‐observation discrepancies (e.g., higher temperatures expected from excess Bowen ratios were partially ameliorated by brighter leaves and more interception/evaporation) significantly biased seasonal model estimates of net radiation ( R n ), the key driver of water and energy fluxes (LE ~ 0.6 R n and H ~ 0.15 R n ), though these biases varied among sites and models. A better representation of energy‐related parameters associated with dynamic phenology (e.g., leaf optical properties, canopy interception, and skin temperature) could improve simulations and benchmarking of current vegetation–atmosphere exchange and reduce uncertainty of regional and global biogeochemical models.

Restrepo‐Coupe, Natalia↗
NASA NTRS2011

Evaluation of Cloud-Resolving Model Intercomparison Simulations Using TWP-ICE Observations: Precipitation and Cloud Structure

The Tropical Warm Pool.International Cloud Experiment (TWP ]ICE) provided extensive observational data sets designed to initialize, force, and constrain atmospheric model simulations. In this first of a two ]part study, precipitation and cloud structures within nine cloud ]resolving model simulations are compared with scanning radar reflectivity and satellite infrared brightness temperature observations during an active monsoon period from 19 to 25 January 2006. Seven of nine simulations overestimate convective area by 20% or more leading to general overestimation of convective rainfall. This is balanced by underestimation of stratiform rainfall by 5% to 50% despite overestimation of stratiform area by up to 65% because of a preponderance of very low stratiform rain rates in all simulations. All simulations fail to reproduce observed radar reflectivity distributions above the melting level in convective regions and throughout the troposphere in stratiform regions. Observed precipitation ]sized ice reaches higher altitudes than simulated precipitation ]sized ice despite some simulations that predict lower than observed top ]of ]atmosphere infrared brightness temperatures. For the simulations that overestimate radar reflectivity aloft, graupel is the cause with one ]moment microphysics schemes whereas snow is the cause with two ]moment microphysics schemes. Differences in simulated radar reflectivity are more highly correlated with differences in mass mean melted diameter (Dm) than differences in ice water content. Dm is largely dependent on the mass ]dimension relationship and gamma size distribution parameters such as size intercept (N0) and shape parameter (m). Having variable density, variable N0, or m greater than zero produces radar reflectivities closest to those observed.

Varble, Adam↗
NASA NTRS2015

The Agricultural Model Intercomparison and Improvement Project (AgMIP) Town Hall

AgMIP (www.agmip.org) is an international community of climate, crop, livestock, economics, and IT experts working to further the development and application of multi-model, multi-scale, multi-disciplinary agricultural models that can inform policy and decision makers around the world. This meeting will engage the AGU community by providing a brief overview of AgMIP, in particular its new plans for a Coordinated Global and Regional Assessment of climate change impacts on agriculture and food security for AR6. This Town Hall will help identify opportunities for participants to become involved in AgMIP and its 30+ activities.

farm crops↗
NASA NTRS2016

Forecasting the Northern African Dust Outbreak Towards Europe in April 2011: A Model Intercomparison

In the framework of the World Meteorological Organisation's Sand and Dust Storm Warning Advisory and Assessment System, we evaluated the predictions of five state-of-the-art dust forecast models during an intense Saharan dust outbreak affecting western and northern Europe in April 2011. We assessed the capacity of the models to predict the evolution of the dust cloud with lead times of up to 72 hours using observations of aerosol optical depth (AOD) from the AErosol RObotic NETwork (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS) and dust surface concentrations from a ground-based measurement network. In addition, the predicted vertical dust distribution was evaluated with vertical extinction profiles from the Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP). To assess the diversity in forecast capability among the models, the analysis was extended to wind field (both surface and profile), synoptic conditions, emissions and deposition fluxes. Models predict the onset and evolution of the AOD for all analysed lead times. On average, differences among the models are larger than differences among lead times for each individual model. In spite of large differences in emission and deposition, the models present comparable skill for AOD. In general, models are better in predicting AOD than near-surface dust concentration over the Iberian Peninsula. Models tend to underestimate the long-range transport towards northern Europe. Our analysis suggests that this is partly due to difficulties in simulating the vertical distribution dust and horizontal wind. Differences in the size distribution and wet scavenging efficiency may also account for model diversity in long-range transport.

dust storms↗
NASA NTRS2019

Model Intercomparison of Maize Response to Climate Change in Low-Input Smallholder Cropping Systems

Smallholder farming systems are characterized by poor soil fertility and low agricultural input use; process-based crop growth models can help quantifying the potential impact of climate change on productivity in these systems.With limiting conditions (water and nutrients), crop models need to rigorously account for soil water, nutrient, CO2, and temperature interactions when simulating climate change effects.

Maize↗
NASA NTRS2023

On the Remote Impacts of Mid-Holocene Saharan Vegetation on South American Hydroclimate: A Modeling Intercomparison

Proxy reconstructions from the mid-Holocene (MH: 6,000 years ago) indicate an intensification of the West African Monsoon and a weakening of the South American Monsoon, primarily resulting from orbitally-driven insolation changes. However, model studies that account for MH orbital configurations and greenhouse gas concentrations can only partially reproduce these changes. Most model studies do not account for the remarkable vegetation changes that occurred during the MH, in particular over the Sahara, precluding realistic simulations of the period. Here, we study precipitation changes over northern Africa and South America using four fully coupled global climate models by accounting for the Saharan greening. Incorporating the Green Sahara amplifies orbitally-driven changes over both regions, and leads to an improvement in proxy-model agreement. Our work highlights the local and remote impacts of vegetation and the importance of considering vegetation changes in the Sahara when studying and modeling global climate.

Paleoclimate modeling↗
NASA NTRS1998

Aviation Fuel Tracer Simulation: Model Intercomparison and Implications

An upper limit for aircraft-produced perturbations to aerosols and gaseous exhaust products in the upper troposphere and lower stratosphere (UT/LS) is derived using the 1992 aviation fuel tracer simulation performed by eleven global atmospheric models. Key Endings are that subsonic aircraft emissions: (1) have not be responsible for the observed water vapor trends at 40 deg N; (2) could be a significant source of soot mass near 12 km, but not at 20 km; (3) might cause a noticeable increase in the background sulfate aerosol surface area and number densities (but not mass density) near the northern mid-latitude tropopause; and (4) could provide a global, annual mean top of the atmosphere radiative forcing up to +0.006 W/sq m and -0.013 W/sq m due to emitted soot and sulfur, respectively.

Danilin, M. Y.↗
NASA NTRS1998

Aviation Fuel Tracer Simulation: Model Intercomparison and Implications

An upper limit for aircraft-produced perturbations to aerosols and gaseous exhaust products in the upper troposphere and lower stratosphere (UT/LS) is derived using the 1992 aviation fuel tracer simulation performed by eleven global atmospheric models. Key findings are that subsonic aircraft emissions: (1) have not been responsible for the observed water vapor trends at 40degN; (2) could be a significant source of soot mass near 12 km, but not at 20 km; (3) might cause a noticeable increase in the background sulfate aerosol surface area and number densities (but not mass density) near the northern mid-latitude tropopause; and (4) could provide a global, annual mean top of the atmosphere radiative forcing up to +0.006 W/sq m and -0.013 W/sq m due to emitted soot and sulfur, respectively.

Danilin, M. Y.↗
NASA NTRS2017

The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6.

The primary objective of CFMIP is to inform future assessments of cloud feedbacks through improved understanding of cloud-climate feedback mechanisms and better evaluation of cloud processes and cloud feedbacks in climate models. However, the CFMIP approach is also increasingly being used to understand other aspects of climate change, and so a second objective has now been introduced, to improve understanding of circulation, regional-scale precipitation, and non-linear changes. CFMIP is supporting ongoing model inter-comparison activities by coordinating a hierarchy of targeted experiments for CMIP6, along with a set of cloud-related output diagnostics. CFMIP contributes primarily to addressing the CMIP6 questions 'How does the Earth system respond to forcing?' and 'What are the origins and consequences of systematic model biases?' and supports the activities of the WCRP Grand Challenge on Clouds, Circulation and Climate Sensitivity. A compact set of Tier 1 experiments is proposed for CMIP6 to address this question: (1) what are the physical mechanisms underlying the range of cloud feedbacks and cloud adjustments predicted by climate models, and which models have the most credible cloud feedbacks? Additional Tier 2 experiments are proposed to address the following questions. (2) Are cloud feedbacks consistent for climate cooling and warming, and if not, why? (3) How do cloud-radiative effects impact the structure, the strength and the variability of the general atmospheric circulation in present and future climates? (4) How do responses in the climate system due to changes in solar forcing differ from changes due to CO2, and is the response sensitive to the sign of the forcing? (5) To what extent is regional climate change per CO2 doubling state-dependent (non-linear), and why? (6) Are climate feedbacks during the 20th century different to those acting on long-term climate change and climate sensitivity? (7) How do regional climate responses (e.g. in precipitation) and their uncertainties in coupled models arise from the combination of different aspects of CO2 forcing and sea surface warming? CFMIP also proposes a number of additional model outputs in the CMIP DECK, CMIP6 Historical and CMIP6 CFMIP experiments, including COSP simulator outputs and process diagnostics to address the following questions. 1. How well do clouds and other relevant variables simulated by models agree with observations? 2. What physical processes and mechanisms are important for a credible simulation of clouds, cloud feedbacks and cloud adjustments in climate models? 3. Which models have the most credible representations of processes relevant to the simulation of clouds? 4. How do clouds and their changes interact with other elements of the climate system?

carbon dioxide↗
NASA NTRS2019

Turbulent Transport in the Gray Zone: A Large Eddy Model Intercomparison Study of the CONSTRAIN Cold Air Outbreak Case

To quantify the turbulent transport at gray zone length scales between 1 and 10 km the Lagrangian evolution of the CONSTRAIN cold air outbreak (CAO) case was simulated with seven large eddy models. The case is characterized by rather large latent and sensible heat fluxes, and a rapid deepening rate of the boundary layer. In some models the entrainment velocity exceeds 4 cm/s. A significant fraction of this growth is attributed to a strong longwave radiative cooling of the inversion layer. The evolution and the timing of the breakup of the stratocumulus cloud deck differ significantly among the models. Sensitivity experiments demonstrate that a decrease in the prescribed cloud droplet number concentration, and the inclusion of ice microphysics, both act to speed up the thinning of the stratocumulus by enhancing the production of precipitation. In all models the formation of mesoscale fluctuations is clearly evident in the cloud fields but also in the horizontal wind velocity. Resolved vertical fluxes remain important for scales up to 10 km. The simulation results show that the resolved vertical velocity variance gradually diminishes with a coarsening of the horizontal mesh, but the total vertical fluxes of heat, moisture, and momentum are only weakly affected. This is a promising result as it demonstrates the potential use of a mesh size dependent turbulent length scale for convective boundary layers at gray zone model resolutions.

gray zone↗
NASA NTRS2015

The AgMIP GRIDded Crop Modeling Initiative (AgGRID) and the Global Gridded Crop Model Intercomparison (GGCMI)

Climate change is a significant risk for agricultural production. Even under optimistic scenarios for climate mitigation action, present-day agricultural areas are likely to face significant increases in temperatures in the coming decades, in addition to changes in precipitation, cloud cover, and the frequency and duration of extreme heat, drought, and flood events (IPCC, 2013). These factors will affect the agricultural system at the global scale by impacting cultivation regimes, prices, trade, and food security (Nelson et al., 2014a). Global-scale evaluation of crop productivity is a major challenge for climate impact and adaptation assessment. Rigorous global assessments that are able to inform planning and policy will benefit from consistent use of models, input data, and assumptions across regions and time that use mutually agreed protocols designed by the modeling community. To ensure this consistency, large-scale assessments are typically performed on uniform spatial grids, with spatial resolution of typically 10 to 50 km, over specified time-periods. Many distinct crop models and model types have been applied on the global scale to assess productivity and climate impacts, often with very different results (Rosenzweig et al., 2014). These models are based to a large extent on field-scale crop process or ecosystems models and they typically require resolved data on weather, environmental, and farm management conditions that are lacking in many regions (Bondeau et al., 2007; Drewniak et al., 2013; Elliott et al., 2014b; Gueneau et al., 2012; Jones et al., 2003; Liu et al., 2007; M¨uller and Robertson, 2014; Van den Hoof et al., 2011;Waha et al., 2012; Xiong et al., 2014). Due to data limitations, the requirements of consistency, and the computational and practical limitations of running models on a large scale, a variety of simplifying assumptions must generally be made regarding prevailing management strategies on the grid scale in both the baseline and future periods. Implementation differences in these and other modeling choices contribute to significant variation among global-scale crop model assessments in addition to differences in crop model implementations that also cause large differences in site-specific crop modeling (Asseng et al., 2013; Bassu et al., 2014).

climate↗