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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Use of MODIS Land and Sea Surface Temperatures to Initialize Mesoscale Models

As computer power continues to increase, mesoscale models are initialized at all hours of the day and continue to be run at higher and higher spatial resolutions. As a result, initializing land surface temperature can be problematic. The majority of research-based models are initialized at 00 and 12 UTC when upper air observations and reanalysis fields are available. The landsea surface temperatures are then set equal to the two-meter air temperature produced by the preprocessor analysis system. This particular procedure might be valid in the early morning hours just prior to sunrise, but it becomes less valid during the remainder of the diurnal cycle. Operational models, such as the Rapid Update Cycle run at the National Centers for Environmental Prediction (NCEP), are initialized every hour on a daily basis. This presents a unique challenge to the initial specification of the land temperature, especially during the first several hours of the solar heating cycle when land and overlying air temperatures are far from being the same. Another issue that needs to be addressed is the spatial variability of land surface temperature. By early next year, the NCEP operational Eta model will be employed at 8 km resolution. Methods to accurately specify the initial land surface temperature at such high resolution need to be explored. This paper presents the results of using data from the NASA Moderate Imaging Sensor aboard the TERRA Satellite to initialize land and sea surface temperatures within the Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) 5'th generation Mesoscale Model (MM5). We have simulated a northern Gulf Coast sea breeze case to demonstrate the utility of using the MODIS data to initialize both the land and sea surface temperature fields. Model grid configurations of 12-, 4-, and l-km are employed.

Arnold, James E.↗
NASA NTRS2019

Efficacy of Climate Forcings in PDRMIP Models

Quantifying the efficacy of different climate forcings is important for understanding the real‐world climate sensitivity. This study presents a systematic multimodel analysis of different climate driver efficacies using simulations from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP). Efficacies calculated from instantaneous radiative forcing deviate considerably from unity across forcing agents and models. Effective radiative forcing (ERF) is a better predictor of global mean near‐surface air temperature (GSAT) change. Efficacies are closest to one when ERF is computed using fixed sea surface temperature experiments and adjusted for land surface temperature changes using radiative kernels. Multimodel mean efficacies based on ERF are close to one for global perturbations of methane, sulfate, black carbon, and insolation, but there is notable intermodel spread. We do not find robust evidence that the geographic location of sulfate aerosol affects its efficacy. GSAT is found to respond more slowly to aerosol forcing than CO2 in the early stages of simulations. Despite these differences, we find that there is no evidence for an efficacy effect on historical GSAT trend estimates based on simulations with an impulse response model, nor on the resulting estimates of climate sensitivity derived from the historical period. However, the considerable intermodel spread in the computed efficacies means that we cannot rule out an efficacy‐induced bias of ±0.4 K in equilibrium climate sensitivity to CO2 doubling when estimated using the historical GSAT trend.

T B Richardson↗
NASA NTRSDate not supplied

Establishing an Urban Heat Exposure Severity Index for Infrastructure Prioritization in Tempe, Arizona, Using NASA Earth Observations and LiDAR

Located on the banks of the Salt River in the Sonoran Desert, Tempe, Arizona, features a semi-arid climate with summer daily maximum temperatures regularly exceeding 37.8°C. Tempe is also subject to the southwestern monsoon season from July-September and the humidity exacerbates the high temperatures. Furthermore, the rapid urbanization experienced in Tempe has resulted in an intensification of the urban heat island. The summer of 2020 shattered the previous record of days exceeding 43.4°C, leading to higher energy and water costs, lower comfort, and increased risk of heat stroke for residents. Recognizing the impacts of extreme heat, the City of Tempe partnered with the Healthy Urban Environments initiative and NASA DEVELOP to identify census tracts that experience a higher mean land surface temperature than the city average. The NASA DEVELOP team used remotely sensed land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI), and albedo data calculated from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) instruments from 2015 to 2020 to create heat hazard and exposure maps. LiDAR point cloud data, provided by the United States Geological Survey through Arizona State University’s Map and Geospatial Hub, were used to derive 3D buildings, building footprints, and tree point data for a shading analysis of walking paths, roads, and buildings at the census tract level. In situ meteorological measurements including air temperature and humidity were used to compare the macro-scale temperature measurements. The team worked with the City of Tempe to develop a methodology to process available data and identify areas of highest concern for urban heat effects within the city. With these insights, Tempe, Arizona can better address these issues with data-driven information to make decisions regarding heat mitigation and adaptation efforts.

John Dialesandro↗
NASA NTRSDate not supplied

Employing NASA Earth Observations and Socioeconomic Data to Conduct Site Suitability Analyses on Residential Tree Planting Initiatives in Phoenix, Arizona

Phoenix, Arizona is the hottest large city in the United States with an average summer daytime temperature of 106°F. Temperatures in Phoenix continue to climb due to increasing global greenhouse gas concentrations and regional urbanization. The impacts of high temperatures, including heat-related illnesses and deaths, are disproportionately concentrated in low-income neighborhoods often characterized by little tree canopy, lack of green space, and insufficient access to shade. The City of Phoenix’s Office of Heat Response and Mitigation and Arizona State University’s Urban Climate Research Center, partnered with NASA DEVELOP to identify residential neighborhoods and parcels within qualified census tracts (QCTs) to be prioritized for tree planting initiatives using funding from the American Rescue Plan Act (ARPA). This project conducted analyses using NASA Earth observations, socioeconomic data from the 2019 American Community Survey, and local tree canopy and mobility data. For Earth observations, daytime land surface temperature, vegetation, and land cover were obtained from the Landsat 8 Thermal Infrared Sensor (TIRS) and Operational Land Imager (OLI). The project team incorporated these data into a heat vulnerability index (HVI) with an emphasis on tree canopy and social vulnerability to rank block groups within QCTs and focused on the resulting top 25 block group HVI scores. These top 25 most vulnerable block groups were then processed through a parcel analysis to determine the feasibility of residential tree planting based on building footprints on each parcel. Within the top 25 block groups, 2,411 parcels were analyzed, and 3,133 existing trees were identified averaging 1.3 trees per parcel with 90% of parcels having 3 trees or less. Homes with 2 trees or fewer were considered high priority for future planting efforts. Based on the City’s goals for increased tree canopy the project team determined that <10,000 additional trees would need to be planted within the most vulnerable 25 block groups. The project findings have helped initiate community engagement efforts and have contributed to the approval of tree planting funds in Phoenix.

Ryan Hammock↗
NASA NTRS2020

The Role of Declining Snow Cover in the Desiccation of the Great Salt Lake, Utah, using MODIS Data

The Great Salt Lake (GSL) in Utah has been shrinking since the middle of the 19th Century, leading to decreased area and volume, and increased salinity. We use satellite data products from the Terra and Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) and the Landsat-7 and -8 satellites, along with meteorological and streamflow data, and modeled data products to study the relationship between changing snow-cover conditions and the decline of the GSL since 2000 in the context of the historical record of lake levels. The GSL basin includes much of the snow-dominated Wasatch and Uinta mountain ranges to the east of the lake. Snowmelt feeds the Bear, Jordan, and Weber rivers which are the three main rivers that flow into the lake. Snowmelt-timing maps, derived from a new MODIS standard snow-cover product, MOD10A1F, show that snow melted ~9.5 days earlier in the GSL basin during the study period, extending from 2000 – 2018. Air temperatures derived from 26 meteorological stations and surface temperatures measured by the Aqua MODIS land-surface temperature (LST) products, MYD21A1D and MYD21A1N, show trends of increasing temperature of ~0.94°C (a=0.05), and ~2.18°C, respectively, with most of the LST trends in the GSL basin being statistically significant (a=0.05). Increasing air temperatures in the basin have led to less precipitation falling as snow, lower snow depth (by ~34.5 mm (=0.01)) and snow-water equivalent (0.02 mm (a=0.01)), and earlier snowmelt. Also during the study period, Global Land surface Evaporation Amsterdam Model data show evaporation increasing by ~3.2 mm/yr, with trends in much of the basin being statistically significant (a=0.05). Trends calculated from the various products are generally in agreement indicating higher temperatures, greater evaporation, less snowfall and snow-on-the ground, and earlier snowmelt. Earlier snowmelt contributes to increasing evaporative loss from water flowing toward the lake. Furthermore, a lower mountain snowpack and less precipitation falling as snow (versus rain) is associated with lower stream discharge even if overall precipitation stays the same. The surface-water temperature of the GSL also increased over the study period by ~ 0.69°C, according to the MODIS LST data products, and the surface-water elevation of the lake dropped by ~1.7 m between 2000 and 2018 based on United States Geological Survey measurements, and the areal extent of the lake decreased by ~901 km2 as measured using Landsat imagery. Desiccation of the lake is associated with deleterious effects on wildlife, recreational activities, and some local industries. And, importantly, an expanding lake bed can also fuel dust storms that promote dangerous air quality along the Wasatch Front. This work elucidates the key role that satellite remote sensing can play in documenting earlier snowmelt and other changes in the GSL basin that influence the ongoing decline of the Great Salt Lake.

Dorothy K Hall↗
NASA NTRS2021

Maine Ecological Forecasting: Using NASA Earth Observations to Assess Federally Endangered Atlantic Salmon Habitat in Maine

Atlantic salmon (Salmo salar) is a species of anadromous fish that was historically prevalent throughout the New England region. Overfishing and habitat loss caused a severe decline in the salmon population, restricting North America’s remaining wild Atlantic salmon to rivers in Maine. Land use and land cover (LULC) change and factors associated with temperature and precipitation are important for understanding the suitability of freshwater habitat for juvenile salmon. In collaboration with the Maine Department of Marine Resources and the Downeast Salmon Federation, the team utilized NASA Earth observations to aid partners in understanding how these factors change in relation to critical salmon habitat. Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) imagery was analyzed to assess changes in LULC between 1985 and 2021. Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG) data were used to determine land surface temperature and precipitation, respectively; between 2000 and 2020. Lastly, temperature and precipitation anomaly maps visualized deviation from the 20-year climatic average for each pixel. LULC analysis for 1985 to 2020 showed a loss of forest cover throughout critical salmon habitat although gains in forested area were also observed. Assessment of mean summer land surface temperature revealed an increase in temperature from 2000 to 2020 and anomaly maps highlighted areas experiencing abnormally high or low summer precipitation and temperature. These results and the underlying data were packaged for the partner organizations to inform future conservation efforts.

Michael Corley↗
NASA NTRS2011

Exploring the Influence of Impervious Surface Density and Shape on Urban Heat Islands in the Northeast USA Using MODIS and Landsat

Impervious surface area (ISA) from the National Land Cover Database (NLCD) 2001 and land surface temperature (LST) from MODIS averaged over three annual cycles (2003-2005) are used in a spatial analysis to assess the urban heat island (UHI) signature and its relationship to settlement size and shape, development intensity distribution, and land cover composition for 42 urban settlements embedded in forest biomes in the Northeastern United States. Development intensity zones, based on percent ISA, are defined for each urban area emanating outward from the urban core to nearby rural areas and are used to stratify land surface temperature. The stratification is further constrained by biome type and elevation to insure objective intercomparisons between urban zones within an urban settlement and between settlements. Stratification based on ISA allows the definition of hierarchically ordered urban zones that are consistent across urban settlements and scales. In addition to the surrounding ecological context, we find that the settlement size and shape as well as the development intensity distribution significantly influence the amplitude of summer daytime UHI. Within the Northeastern US temperate broadleaf mixed forest, UHI magnitude is positively related to the logarithm of the urban area size. Our study indicates that for similar urban area sizes, the development intensity distribution is one of the major drivers of UHI. In addition to urban area size and development intensity distribution, this analysis shows that both the shape of the urban area and the land cover composition in the surrounding rural area play an important role in modulating the UHI magnitude in different urban settlements. Our results indicate that remotely sensed urban area size and shape as well as the development intensity distribution influence UHI amplitude across regional scales.

Zhang, Ping↗
NASA NTRS2021

Emerging satellite observations for diurnal cycling of ecosystem processes

Diurnal cycling of plant carbon uptake and water use, and their responses to water and heat stresses, provide direct insight into assessing ecosystem productivity, agricultural production and management practices, carbon and water cycles, and feedbacks to the climate. Temperature, light, atmospheric water demand, soil moisture and leaf water potential vary over the course of the day, leading to diurnal variations in stomatal conductance, photosynthesis and transpiration. Earth observations from polar-orbiting satellites are incapable of studying these diurnal variations. Here, we review the emerging satellite observations that have the potential for studying how plant functioning and ecosystem processes vary over the course of the diurnal cycle. The recently launched ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and Orbiting Carbon Observatory-3 (OCO-3) provide land surface temperature, evapotranspiration (ET), gross primary production (GPP) and solar-induced chlorophyll fluorescence data at different times of day. New generation operational geostationary satellites such as Himawari-8 and the GOES-R series can provide continuous, high-frequency data of land surface temperature, solar radiation, GPP and ET. Future satellite missions such as GeoCarb, TEMPO and Sentinel-4 are also planned to have diurnal sampling capability of solar-induced chlorophyll fluorescence. We explore the unprecedented opportunities for characterizing and understanding how GPP, ET and water use efficiency vary over the course of the day in response to temperature and water stresses, and management practices. We also envision that these emerging observations will revolutionize studies of plant functioning and ecosystem processes in the context of climate change and that these observations and findings can inform agricultural and forest management and lead to improvements in Earth system models and climate projections.

Plant functioning↗
NASA NTRS2022

Tropical Surface Temperature Response to Vegetation Cover Changes and the Role of Drylands

Vegetation cover creates competing effects on land surface temperature: it typically cools through enhancing energy dissipation and warms via decreasing surface albedo. Global vegetation has been previously found to overall net cool land surfaces with cooling contributions from temperate and tropical vegetation and warming contributions from boreal vegetation. Recent studies suggest dryland vegetation across the tropics strongly contributes to this global net cooling feedback. However, observation-based vegetation-temperature interaction studies have been limited in the tropics, especially in their widespread drylands. Theoretical considerations also call into question the ability of dryland vegetation to strongly cool the surface under low water availability. Here, we use satellite observations to investigate how tropical vegetation cover influences the surface energy balance. We find that while increased vegetation cover would impart net cooling feedbacks across the tropics, net vegetal cooling effects are subdued in drylands. Using observations, we determine that dryland plants have less ability to cool the surface due to their cooling pathways being reduced by aridity, overall less efficient dissipation of turbulent energy, and their tendency to strongly increase solar radiation absorption. As a result, while proportional greening across the tropics would create an overall biophysical cooling feedback, dryland tropical vegetation reduces the overall tropical surface cooling magnitude by at least 14%, instead of enhancing cooling as suggested by previous global studies.

Biophysical Feedbacks↗
NASA NTRS2010

Remote Sensing of the Urban Heat Island Effect Across Biomes in the Continental USA

Impervious surface area (ISA) from the Landsat TM-based NLCD 2001 dataset and land surface temperature (LST) from MODIS averaged over three annual cycles (2003-2005) are used in a spatial analysis to assess the urban heat island (UHI) skin temperature amplitude and its relationship to development intensity, size, and ecological setting for 38 of the most populous cities in the continental United States. Development intensity zones based on %ISA are defined for each urban area emanating outward from the urban core to the nonurban rural areas nearby and used to stratify sampling for land surface temperatures and NDVI. Sampling is further constrained by biome and elevation to insure objective intercomparisons between zones and between cities in different biomes permitting the definition of hierarchically ordered zones that are consistent across urban areas in different ecological setting and across scales. We find that ecological context significantly influences the amplitude of summer daytime UHI (urban-rural temperature difference) the largest (8 C average) observed for cities built in biomes dominated by temperate broadleaf and mixed forest. For all cities combined, ISA is the primary driver for increase in temperature explaining 70% of the total variance in LST. On a yearly average, urban areas are substantially warmer than the non-urban fringe by 2.9 C, except for urban areas in biomes with arid and semiarid climates. The average amplitude of the UHI is remarkably asymmetric with a 4.3 C temperature difference in summer and only 1.3 C in winter. In desert environments, the LST's response to ISA presents an uncharacteristic "U-shaped" horizontal gradient decreasing from the urban core to the outskirts of the city and then increasing again in the suburban to the rural zones. UHI's calculated for these cities point to a possible heat sink effect. These observational results show that the urban heat island amplitude both increases with city size and is seasonally asymmetric for a large number of cities across most biomes. The implications are that for urban areas developed within forested ecosystems the summertime UHI can be quite high relative to the wintertime UHI suggesting that the residential energy consumption required for summer cooling is likely to increase with urban growth within those biomes.

Imhoff, Marc L.↗
NASA NTRS2018

Developing a Remotely Sensed Drought Monitoring Indicator for Morocco

Drought is one of the most serious climatic and natural disasters inflicting serious impacts on the socio-economy of Morocco, which is characterized both by low-average annual rainfall and high irregularity in the spatial distribution and timing of precipitation across the country. This work aims to develop a comprehensive and integrated method for drought monitoring based on remote sensing techniques. The main input parameters are derived monthly from satellite data at the national scale and are then combined to generate a composite drought index presenting different severity classes of drought. The input parameters are: Standardized Precipitation Index calculated from satellite-based precipitation data since 1981 (CHIRPS), anomalies in the day-night difference of Land Surface Temperature as a proxy for soil moisture, Normalized Difference Vegetation Index anomalies from Moderate Resolution Imaging Spectroradiometer (MODIS) data and Evapotranspiration anomalies from surface energy balance modeling. All of these satellite-based indices are being used to monitor vegetation condition, rainfall and land surface temperature. The weighted combination of these input parameters into one composite indicator takes into account the importance of the rainfall-based parameter (SPI). The composite drought index maps were generated during the growing seasons going back to 2003. These maps have been compared to both the historical, in situ precipitation data across Morocco and with the historical yield data across different provinces with information being available since 2000. The maps are disseminated monthly to several main stakeholders' groups including the Ministry of Agriculture and Department of Water in Morocco.

Drought monitoring↗
NASA NTRS2024

Modeling Impacts of Dust Mineralogy on Fast Climate Response

Mineralogical composition drives dust impacts on Earth's climate systems. However, most climate models still use homogeneous dust, without accounting for the temporal and spatial variation in mineralogy. To quantify the radiative impact of resolving dust mineralogy on Earth's climate, we implement and simulate the distribution of dust minerals (i.e., illite, kaolinite, smectite, hematite, calcite, feldspar, quartz, and gypsum) from Claquin et al. (1999) (C1999) and activate their interaction with radiation in the GFDL AM4.0 model. Resolving mineralogy reduces dust absorption compared to the homogeneous dust used in the standard GFDL AM4.0 model that assumes a globally uniform hematite volume content of 2.7 % (HD27). The reduction in dust absorption results in improved agreement with observation-based single-scattering albedo (SSA), radiative fluxes from CERES (the Clouds and the Earth's Radiant Energy System), and land surface temperature from the CRU (Climatic Research Unit) compared to the baseline HD27 model version. It also results in distinct radiative impacts on Earth's climate over North Africa. Over the 19-year (from 2001 to 2019) modeled period during JJA (June–July–August), the reduction in dust absorption in AM4.0 leads to a reduction of over 50 % in net downward radiation across the Sahara and approximately 20 % over the Sahel at the top of the atmosphere (TOA) compared to the baseline HD27 model version. The reduced dust absorption weakens the atmospheric warming effect of dust aerosols and leads to an alteration in land surface temperature, resulting in a decrease of 0.66 K over the Sahara and an increase of 0.7 K over the Sahel. The less warming in the atmosphere suppresses ascent and weakens the monsoon inflow from the Gulf of Guinea. This brings less moisture to the Sahel, which combined with decreased ascent induces a reduction of precipitation. To isolate the effect of reduced absorption compared to resolving spatial and temporal mineralogy, we carry out a simulation where the hematite volume content of homogeneous dust is reduced from 2.7 % to 0.9 % (HD09). The dust absorption (e.g., single-scattering albedo) of HD09 is comparable to that of the mineralogically speciated model on a global mean scale, albeit with a lower spatial variation that arises solely from particle size. Comparison of the two models indicates that the spatial inhomogeneity in dust absorption resulting from resolving mineralogy does not have significant impacts on Earth's radiation and climate, provided there is a similar level of dust absorption on a global mean scale before and after resolving dust mineralogy. However, uncertainties related to emission and distribution of minerals may blur the advantages of resolving minerals to study their impact on radiation, cloud properties, ocean biogeochemistry, air quality, and photochemistry. On the other hand, lumping together clay minerals (i.e., illite, kaolinite, and smectite), but excluding externally mixed hematite and gypsum, appears to provide both computational efficiency and relative accuracy. Nevertheless, for specific research, it may be necessary to fully resolve mineralogy to achieve accuracy.

Soil dust↗
NASA NTRS2012

An Enhanced TIMESAT Algorithm for Estimating Vegetation Phenology Metrics from MODIS Data

An enhanced TIMESAT algorithm was developed for retrieving vegetation phenology metrics from 250 m and 500 m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indexes (VI) over North America. MODIS VI data were pre-processed using snow-cover and land surface temperature data, and temporally smoothed with the enhanced TIMESAT algorithm. An objective third derivative test was applied to define key phenology dates and retrieve a set of phenology metrics. This algorithm has been applied to two MODIS VIs: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In this paper, we describe the algorithm and use EVI as an example to compare three sets of TIMESAT algorithm/MODIS VI combinations: a) original TIMESAT algorithm with original MODIS VI, b) original TIMESAT algorithm with pre-processed MODIS VI, and c) enhanced TIMESAT and pre-processed MODIS VI. All retrievals were compared with ground phenology observations, some made available through the National Phenology Network. Our results show that for MODIS data in middle to high latitude regions, snow and land surface temperature information is critical in retrieving phenology metrics from satellite observations. The results also show that the enhanced TIMESAT algorithm can better accommodate growing season start and end dates that vary significantly from year to year. The TIMESAT algorithm improvements contribute to more spatial coverage and more accurate retrievals of the phenology metrics. Among three sets of TIMESAT/MODIS VI combinations, the start of the growing season metric predicted by the enhanced TIMESAT algorithm using pre-processed MODIS VIs has the best associations with ground observed vegetation greenup dates.

Tan, Bin↗
NASA NTRS1982

The GLAS physical inversion method for analysis of HIRS2/MSU sounding data

Goddard Laboratory for Atmospheric Sciences has developed a method to derive atmospheric temperature profiles, sea or land surface temperatures, sea ice extent and snow cover, and cloud heights and fractional cloud, from HIRS2/MSU radiance data. Chapter 1 describes the physics used in the radiative transfer calculations and demonstrates the accuracy of the calculations. Chapter 2 describes the rapid transmittance algorithm used and demonstrates its accuracy. Chapter 3 describes the theory and application of the techniques used to analyze the satellite data. Chapter 4 shows results obtained for January 1979.

Susskind, J.↗
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↗