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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

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

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

climate↗
NASA NTRS2015

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

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

Celine J W Bonfils↗
NASA NTRS2022

Implementation and Evaluation of Irrigation Techniques in the Community Land Model

Several previous studies have highlighted the irrigation-induced impacts on the global and regional water cycle, energy budget, and near-surface climate. While land models are widely used to address this question, the implementations of irrigation in these models vary in complexity. Here, we expand the representation of irrigation in Community Land Model to enable six different irrigation methods. We find that using a combination of irrigation methods, including default, sprinkler, flood and paddy techniques performs best as determined by evaluating the simulated irrigation water withdrawals against observations, and therefore select this combination as the new irrigation scheme. Then, the impact of the new irrigation scheme on surface fluxes is evaluated and detected using single-point simulations. Finally, the global and regional irrigation-induced impacts on surface energy and water fluxes are compared using both the original and the new irrigation scheme. The new irrigation scheme substantially reduces the bias and root-mean-square error of simulated irrigation water withdrawal in the USA and other countries, but considerably overestimates withdrawals in Central China. Results of single-point experiments show that different irrigation methods have different effects on surface fluxes, while the magnitudes are small. At the global scale, the new scheme enlarges the irrigation-induced impacts on water and energy variables relative to the original scheme, with varying magnitudes across regions. Overall, our results suggest that this newly developed scheme is a better tool for simulating irrigation-induced impacts on climate, and highlight the added value of incorporating human water management in Earth system models.

Community land models↗
NASA NTRS1992

Carbon cycling in high-latitude ecosystems

The carbon-rich soils and peatlands of high-latitude ecosystems could substantially influence atmospheric concentrations of CO2 and CH4 in a changing climate. Currently, cold, often waterlogged conditions retard decomposition, and release of carbon back to the atmosphere may be further slowed by physical protection of organic matter in permafrost. As a result, many northern ecosystems accumulate carbon over time (Billings et al., 1982; Poole and Miller, 1982), and although such rates of accumulation are low, thousands of years of development have left Arctic ecosystems with an extremely high soil carbon content; Schlesinger's (1984) average value of 20.4 kg C/m(sup 2) leads to a global estimate of 163 x 10(exp 15) g C. All GCM simulations of a doubled CO2 climate predict the greatest warming to occur in the polar regions (Dickinson, 1986; Mitchell, 1989). Given the extensive northern carbon pools and the strong sensitivity of decomposition processes to temperature, even a slight warming of the soil could dramatically alter the carbon balance of Arctic ecosystems. If warming accelerates rates of decomposition more than rates of primary production, a sizeable additional accumulation of CO2 in the atmosphere could occur. Furthermore, CH4 produced in anaerobic soils and peatlands of the Arctic already composes a good percentage of the global efflux (Cicerone and Oremlund, 1988); if northern soils become warmer and wetter as a whole, CH4 emissions could dramatically rise. A robust understanding of the primary controls of carbon fluxes in Arctic ecosystems is critical. As a framework for a systematic examination of these controls, we discussed a conceptual model of regional-scale Arctic carbon turnover, including CH4 production, and based upon the Century soil organic matter model.

Townsend, Alan↗
NASA NTRSDate not supplied

Simulations of X-59 Sonic Thumps and Traditional Sonic Booms Propagated Around the World for Three Atmospheric Models

Propagation simulations of sonic booms from supersonic aircraft through atmospheric data over time at fixed locations provides the opportunity to assess noise exposure statistics for different climate regions. Knowledge of climate-based differences in sonic boom noise exposure statistics is important to ensure that future civil supersonic aircraft noise certification standards are globally applicable and effective. In this presentation, simulated sonic booms from the NASA X-59 Quesst quiet supersonic aircraft and conventional supersonic aircraft were propagated through atmospheric data at 100 locations across the world using PCBoom. Noise exposure statistics are compared for propagation results from three different atmospheric databases (NOAA Global Forecast System, NOAA Climate Forecast System Version 2, and the ECMWF Reanalysis Version 5). These atmospheric models were chosen due to their global coverage, popularity, and database availability. Preliminary statistical models are fit to assess the impact of several factors including flight direction, season, ground elevation, and climate on noise exposure size and loudness. Areas with prevalence of higher noise due to their climate are identified, which could help inform future supersonic aircraft noise standards.

X-59↗
NASA NTRS2020

Irrigation Water Demand Sensitivity to Climate Variability across the Contiguous United States

Climate variability is an important driver of irrigation water use in many regions. Efforts to anticipate climate change impacts on future water availability can benefit from understanding how irrigation water demand has responded to these drivers to date. Here we apply satellite derived data, meteorological reanalysis, an advanced land surface model, and available state level reports to quantify irrigation demand sensitivities to temperature and precipitation across the Contiguous United States, for the period of 2002-2017. As expected, strong negative correlations are found between precipitation and irrigation withdrawals, both simulated and reported. Temperature sensitivities, however, vary by region and season, as do the interactive effects of temperature and precipitation on irrigation. Climate-induced irrigation variability is largest in transitional climate zones. These transitional zones are generally separate from the regions where rates of irrigation withdrawals are greatest, such that climate-induced variability in irrigation demand represents a water resource consideration that is distinct from chronic over pumping.

Wanshu Nie↗
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↗
NASA NTRS2018

Climate and the Global Famine of 1876-78

From 1875 to 1878, concurrent multiyear droughts in Asia, Brazil, and Africa, referred to as the Great Drought, caused widespread crop failures, catalyzing the so-called Global Famine, which had fatalities exceeding 50 million people and long-lasting societal consequences. Observations, paleoclimate reconstructions, and climate model simulations are used 1) to demonstrate the severity and characterize the evolution of drought across different regions, and 2) to investigate the underlying mechanisms driving its multiyear persistence. Severe or record-setting droughts occurred on continents in both hemispheres and in multiple seasons, with the "Monsoon Asia" region being the hardest hit, experiencing the single most intense and the second most expansive drought in the last 800 years. The extreme severity, duration, and extent of this global event is associated with an extraordinary combination of preceding cool tropical Pacific conditions (1870-76), a record-breaking El Nino (1877-78), a record strong Indian Ocean dipole (1877), and record warm North Atlantic Ocean (1878) conditions. Composites of historical analogs and two sets of ensemble simulations - one forced with global sea surface temperatures (SSTs) and another forced with tropical Pacific SST - were used to distinguish the role of the extreme conditions in different ocean basins. While the drought in most regions was largely driven by the tropical Pacific SST conditions, an extreme positive phase of the Indian Ocean dipole and warm North Atlantic SSTs, both likely aided by the strong El Nino in 1877-78, intensified and prolonged droughts in Australia and Brazil, respectively, and extended the impact to northern and southeastern Africa. Climatic conditions that caused the Great Drought and Global Famine arose from natural variability, and their recurrence, with hydrological impacts intensified by global warming, could again potentially undermine global food security.

multi-year drought↗
NASA NTRS2015

Evaluating the Sensitivity of Agricultural Model Performance to Different Climate Inputs: Supplemental Material

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled and observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources reanalysis, reanalysis that is bias corrected with observed climate, and a control dataset and compared with observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by non-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. Some issues persist for all choices of climate inputs: crop yields appear to be oversensitive to precipitation fluctuations but under sensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves.

simulation↗
NASA NTRSDate not supplied

Experiment Design and Visualization Techniques for an X-59 Low-boom Variability Study

This presentation outlines the design of experiments approach and data visualization techniques for a simulation study of sonic booms from NASA’s X-59 supersonic aircraft. The X-59 will soon be flown over communities across the contiguous USA as it produces a low-loudness sonic boom, or low-boom. Survey data on human perception of low-booms will be collected to support development of potential future commercial supersonic aircraft noise regulatory standards. The macroscopic atmosphere plays a critical role in the loudness of sonic booms. The extensive sonic boom simulation study presented herein was completed to assess climatological, geographical, and seasonal effects on the variability of the X-59’s low-boom loudness and noise exposure region size in order to inform X-59 community test planning. The loudness and extent of the noise exposure region make up the “sonic boom carpet.” Two spatial and temporal resolutions of atmospheric input data to the simulation were investigated. A Fast Flexible Space-Filling Design was used to select the locations across the USA for the two spatial resolutions. Analysis of simulated X-59 low-boom loudness data within a regional subset of the northeast USA was completed using a bootstrap forest to determine the final spatial and temporal resolution of the countrywide simulation study. Atmospheric profiles from NOAA’s Climate Forecast System Version 2 database were used to generate over one million simulated X-59 carpets at the final selected 138 locations across the USA. Effects of aircraft heading, season, geography, and climate zone on low-boom levels and noise exposure region size were analyzed. Models were developed to estimate loudness metrics throughout the USA for X-59 supersonic cruise overflight, and results were visualized on maps to show geographical and seasonal trends. These results inform regulators and mission planners on expected variations in boom levels and carpet extent from atmospheric variations. Understanding potential carpet variability is important when planning community noise surveys using the X-59.

X-59↗
NASA NTRS2022

Using Atmospheric Trace Gas Vertical Profiles to Evaluate Model Fluxes: A Case Study of Arctic-Cap Observations and Geos Simulations for the ABoVE Domain

Accurate estimates of carbon–climate feedbacks require an independent means for evaluating surface flux models at regional scales. The altitude-integrated enhancement (AIE) derived from the Arctic Carbon Atmospheric Profiles (Arctic-CAP) project demonstrates the utility of this bulk quantity for surface flux model evaluation. This bulk quantity leverages background mole fraction values from the middle free troposphere, is agnostic to uncertainties in boundary layer height, and can be derived from model estimates of mole fractions and vertical gradients. To demonstrate the utility of the bulk quantity, six airborne profiling surveys of atmospheric carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) throughout Alaska and northwestern Canada between April and November 2017 were completed as part of NASA’s Arctic–Boreal Vulnerability Experiment (ABoVE). The Arctic-CAP sampling strategy involved acquiring vertical profiles of CO2, CH4, and CO from the surface to 5 km altitude at 25 sites around the ABoVE domain on a 4- to 6-week time interval. All Arctic-CAP measurements were compared to a global simulation using the Goddard Earth Observing System (GEOS) modeling system. Comparisons of the AIE bulk quantity from aircraft observations and GEOS simulations of atmospheric CO2, CH4, and CO highlight the fidelity of the modeled surface fluxes. The model–data comparison over the ABoVE domain reveals that while current state-of-the-art models and flux estimates are able to capture broad-scale spatial and temporal patterns in near-surface CO2 and CH4 concentrations, more work is needed to resolve fine-scale flux features that are captured in CO observations

CO2↗
NASA NTRS2023

Risks of Synchronized Low Yields Are Underestimated in Climate and Crop Model Projections

Simultaneous harvest failures across major crop-producing regions are a threat to global food security. Concurrent weather extremes driven by a strongly meandering jet stream could trigger such events, but so far this has not been quantified. Specifically, the ability of state-of-the art crop and climate models to adequately reproduce such high impact events is a crucial component for estimating risks to global food security. Here we find an increased likelihood of concurrent low yields during summers featuring meandering jets in observations and models. While climate models accurately simulate atmospheric patterns, associated surface weather anomalies and negative effects on crop responses are mostly underestimated in bias-adjusted simulations. Given the identified model biases, future assessments of regional and concurrent crop losses from meandering jet states remain highly uncertain. Our results suggest that model-blind spots for such high-impact but deeply-uncertain hazards have to be anticipated and accounted for in meaningful climate risk assessments.

Atmospheric dynamics↗
NASA NTRS2014

Geostationary Coastal and Air Pollution Events (GEO-CAPE) Sensitivity Analysis Experiment

Geostationary Coastal and Air pollution Events (GEO-CAPE) is a NASA decadal survey mission to be designed to provide surface reflectance at high spectral, spatial, and temporal resolutions from a geostationary orbit necessary for studying regional-scale air quality issues and their impact on global atmospheric composition processes. GEO-CAPE's Atmospheric Science Questions explore the influence of both gases and particles on air quality, atmospheric composition, and climate. The objective of the GEO-CAPE Observing System Simulation Experiment (OSSE) is to analyze the sensitivity of ozone to the global and regional NOx emissions and improve the science impact of GEO-CAPE with respect to the global air quality. The GEO-CAPE OSSE team at Jet propulsion Laboratory has developed a comprehensive OSSE framework that can perform adjoint-sensitivity analysis for a wide range of observation scenarios and measurement qualities. This report discusses the OSSE framework and presents the sensitivity analysis results obtained from the GEO-CAPE OSSE framework for seven observation scenarios and three instrument systems.

Lee, Meemong↗
NASA NTRS2016

Using High-Resolution Airborne Remote Sensing to Study Aerosol Near Clouds

The horizontal space in between clear and cloudy air is very complex. This so-called twilight zone includes activated aerosols that are not quite clouds, thin cloud fragments that are not easily observable, and dying clouds that have not quite disappeared. This is a huge challenge for satellite remote sensing, specifically for retrieval of aerosol properties. Identifying what is cloud versus what is not cloud is critically important for attributing radiative effects and forcings to aerosols. At the same time, the radiative interactions between clouds and the surrounding media (molecules, surface and aerosols themselves) will contaminate retrieval of aerosol properties, even in clear skies. Most studies on aerosol cloud interactions are relevant to moderate resolution imagery (e.g. 500 m) from sensors such as MODIS. Since standard aerosol retrieval algorithms tend to keep a distance (e.g. 1 km) from the nearest detected cloud, it is impossible to evaluate what happens closer to the cloud. During Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS), the NASA ER-2 flew with the enhanced MODIS Airborne Simulator (eMAS), providing MODIS-like spectral observations at high (50 m) spatial resolution. We have applied MODIS-like aerosol retrieval for the eMAS data, providing new detail to characterization of aerosol near clouds. Interpretation and evaluation of these eMAS aerosol retrievals is aided by independent MODIS-like cloud retrievals, as well as profiles from the co-flying Cloud Physics Lidar (CPL). Understanding aerosolcloud retrieval at high resolution will lead to better characterization and interpretation of long-term, global products from lower resolution (e.g.MODIS) satellite retrievals.

aerosols near clouds↗
NASA NTRS2010

Studies of 21st-Century Precipitation Trends Over West Africa

West Africa includes a semi-arid zone between the Sahara Desert and the humid Gulf of Guinea coast, approximately between 10 N and 20 N, which is irrigated by summer monsoon rains. This article refers to the region as the Sahel. Rain-fed agriculture is the primary sustenance for Sahel populations, and severe droughts (in the 1970s and 1980s), therefore, have devastating negative societal impacts. The future frequency of Sahel droughts and the evolution of its hydrological balance are therefore of great interest. The article reviews 10 recent research studies that attempt to discover how climate changes will affect the hydrology of the Sahel throughout the 21st century. All 10 studies rely on atmosphere ocean global climate model (AOGCM) simulations based on a range of greenhouse gas emissions scenarios. Many of the simulations are contained in the Intergovernmental Panel on Climate Change archives for Assessment Reports #3 and #4. Two of the studies use AOGCM data to drive regional climate models. Seven studies make projections for the first half of the 21st century and eight studies make projections for the second half. Some studies make projections of wetter conditions and some predict more frequent droughts, and each describes the atmospheric processes associated with its prediction. Only one study projects more frequent droughts before 2050, and that is only for continent-wide degradation in vegetation cover. The challenge to correctly simulate Sahel rainfall decadal trends is particularly daunting because multiple physical mechanisms compete to drive the trend upwards or downwards. A variety of model deficiencies, regarding the simulation of one or more of these physical processes, taints models climate change projections. Consequently, no consensus emerges regarding the impact of anticipated greenhouse gas forcing on the hydrology of the Sahel in the second half of the 21st century.

Druyan, Leonard M.↗
NASA NTRS1999

Response to CO2 Transient Increase in the GISS Coupled Model: Regional Coolings in a Warming Climate

The (GISS) Goddard Institute for Space Studies coupled atmosphere-ocean model is used to investigate the effect of increased atmospheric CO2 by comparing a compounded 1 percent CO2 increase experiment with a control simulation. After 70 years of integration, the global surface air temperature in the 1 percent CO2 experiment is 1.43 C warmer. In spite of this global warming, there are two distinct regions, the northern Atlantic Ocean and the southern Pacific Ocean, where the surface air temperature is up to 4 C cooler. This situation is maintained by two positive feedbacks: a local effect on convection in the South Pacific and a non-local impact on the meridional circulation in the North Atlantic. The poleward transport of latent energy and dry static energy by the atmosphere is greater in the 1 percent CO2 experiment, caused by warming and therefore increased water vapor and greater greenhouse capacity at lower latitudes. The larger atmospheric transports tend to reduce upward vertical fluxes of heat and moisture from the ocean surface at high latitudes, which has the effect of stabilizing the ocean, reducing both convection and the thermohaline circulation. With less convection, less warm water is brought up from below, and with a reduced North Atlantic thermohaline circulation (by 30 percent at time of CO2 doubling), the poleward energy transport by the oceans decreases. The colder water then leads to further reductions in evaporation, decreases of salinity at high latitudes, continued stabilization of the ocean, and maintenance of reduced convection and meridional overturning. Although sea ice decreases globally, it increases in the cooling regions which reduces the overall climate sensitivity; its effect is most pronounced in the Southern Hemisphere. Tropical warming has been observed over the past several decades; if modeling studies such as this and others which have produced similar effects are valid, these processes may already be beginning.

Russell, Gary L.↗
NASA NTRS2015

Ocean-Atmosphere Coupled Model Simulations of Precipitation in the Central Andes

The meridional extent and complex orography of the South American continent contributes to a wide diversity of climate regimes ranging from hyper-arid deserts to tropical rainforests to sub-polar highland regions. In addition, South American meteorology and climate are also made further complicated by ENSO, a powerful coupled ocean-atmosphere phenomenon. Modelling studies in this region have typically resorted to either atmospheric mesoscale or atmosphere-ocean coupled global climate models. The latter offers full physics and high spatial resolution, but it is computationally inefficient typically lack an interactive ocean, whereas the former offers high computational efficiency and ocean-atmosphere coupling, but it lacks adequate spatial and temporal resolution to adequate resolve the complex orography and explicitly simulate precipitation. Explicit simulation of precipitation is vital in the Central Andes where rainfall rates are light (0.5-5 mm hr-1), there is strong seasonality, and most precipitation is associated with weak mesoscale-organized convection. Recent increases in both computational power and model development have led to the advent of coupled ocean-atmosphere mesoscale models for both weather and climate study applications. These modelling systems, while computationally expensive, include two-way ocean-atmosphere coupling, high resolution, and explicit simulation of precipitation. In this study, we use the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST), a fully-coupled mesoscale atmosphere-ocean modeling system. Previous work has shown COAWST to reasonably simulate the entire 2003-2004 wet season (Dec-Feb) as validated against both satellite and model analysis data when ECMWF interim analysis data were used for boundary conditions on a 27-9-km grid configuration (Outer grid extent: 60.4S to 17.7N and 118.6W to 17.4W).

Regional Climate Model↗
NASA NTRS2018

Climate Shifts Within Major Agricultural Seasons for +1.5 and +2.0 °C Worlds: HAPPI Projections and AgMIP Modeling Scenarios

This study compares climate changes in major agricultural regions and current agricultural seasons associated with global warming of +1.5 or +2.0 °C above pre-industrial conditions. It describes the generation of climate scenarios for agricultural modeling applications conducted as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Global and Regional Assessments. Climate scenarios from the Half a degree Additional warming, Projections, Prognosis and Impacts project (HAPPI) are largely consistent with transient scenarios extracted from RCP4.5 simulations of the Coupled Model Intercomparison Project phase 5 (CMIP5). Focusing on food and agricultural systems and top-producing breadbaskets in particular, we distinguish maize, rice, wheat, and soy season changes from global annual mean climate changes. Many agricultural regions warm at a rate that is faster than the global mean surface temperature (including oceans) but slower than the mean land surface temperature, leading to regional warming that exceeds 0.5 °C between the +1.5 and +2.0 °C Worlds. Agricultural growing seasons warm at a pace slightly behind the annual temperature trends in most regions, while precipitation increases slightly ahead of the annual rate. Rice cultivation regions show reduced warming as they are concentrated where monsoon rainfall is projected to intensify, although projections are influenced by Asian aerosol loading in climate mitigation scenarios. Compared to CMIP5, HAPPI slightly underestimates the CO2 concentration that corresponds to the +1.5 °C World but overestimates the CO2 concentration for the +2.0 °C World, which means that HAPPI scenarios may also lead to an overestimate in the beneficial effects of CO2 on crops in the +2.0 °C World. HAPPI enables detailed analysis of the shifting distribution of extreme growing season temperatures and precipitation, highlighting widespread increases in extreme heat seasons and heightened skewness toward hot seasons in the tropics. Shifts in the probability of extreme drought seasons generally tracked median precipitation changes; however, some regions skewed toward drought conditions even where median precipitation changes were small. Together, these findings highlight unique seasonal and agricultural region changes in the +1.5 °C and +2.0 °C worlds for adaptation planning in these climate stabilization targets.

Alexander C. Ruane↗