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NASA NTRS2012

Earth System Modeling and Field Experiments in the Arctic-Boreal Zone - Report from a NASA Workshop

Early climate modeling studies predicted that the Arctic Ocean and surrounding circumpolar land masses would heat up earlier and faster than other parts of the planet as a result of greenhouse gas-induced climate change, augmented by the sea-ice albedo feedback effect. These predictions have been largely borne out by observations over the last thirty years. However, despite constant improvement, global climate models have greater difficulty in reproducing the current climate in the Arctic than elsewhere and the scatter between projections from different climate models is much larger in the Arctic than for other regions. Biogeochemical cycle (BGC) models indicate that the warming in the Arctic-Boreal Zone (ABZ) could lead to widespread thawing of the permafrost, along with massive releases of CO2 and CH4, and large-scale changes in the vegetation cover in the ABZ. However, the uncertainties associated with these BGC model predictions are even larger than those associated with the physical climate system models used to describe climate change. These deficiencies in climate and BGC models reflect, at least in part, an incomplete understanding of the Arctic climate system and can be related to inadequate observational data or analyses of existing data. A workshop was held at NASA/GSFC, May 22-24 2012, to assess the predictive capability of the models, prioritize the critical science questions; and make recommendations regarding new field experiments needed to improve model subcomponents. This presentation will summarize the findings and recommendations of the workshop, including the need for aircraft and flux tower measurements and extension of existing in-situ measurements to improve process modeling of both the physical climate and biogeochemical cycle systems. Studies should be directly linked to remote sensing investigations with a view to scaling up the improved process models to the Earth System Model scale. Data assimilation and observing system simulation studies should be used to guide the deployment pattern and schedule for inversion studies as well. Synthesis and integration of previously funded Arctic-Boreal projects (e.g., ABLE, BOREAS, ICESCAPE, ICEBRIDGE, ARCTAS) should also be undertaken. Such an effort would include the integration of multiple remotely sensed products from the EOS satellites and other resources.

Sellers, Piers↗

NASA NTRS2018

Seasonal Monitoring of Melt and Accumulation Within the Deep Percolation Zone of the Greenland Ice Sheet and Comparison with Simulations of Regional Climate Modeling

Increasing melt over the Greenland Ice Sheet (GrIS) recorded over the past several years has resulted in significant changes of the percolation regime of the ice sheet. It remains unclear whether Greenland's percolation zone will act as a meltwater buffer in the near future through gradually filling all pore space or if near-surface refreezing causes the formation of impermeable layers, which provoke lateral runoff. Homogeneous ice layers within perennial firn, as well as near-surface ice layers of several meter thickness have been observed in firn cores. Because firn coring is a destructive method, deriving stratigraphic changes in firn and allocation of summer melt events is challenging. To overcome this deficit and provide continuous data for model evaluations on snow and firn density, temporal changes in liquid water content and depths of water infiltration, we installed an upward-looking radar system (upGPR) 3.4 m below the snow surface in May 2016 close to Camp Raven (66.4779 deg N, 46.2856 deg W) at 2120 m a.s.l. The radar is capable of quasi-continuously monitoring changes in snow and firn stratigraphy, which occur above the antennas. For summer 2016, we observed four major melt events, which routed liquid water into various depths beneath the surface. The last event in mid-August resulted in the deepest percolation down to about 2.3 m beneath the surface. Comparisons with simulations from the regional climate model MAR are in very good agreement in terms of seasonal changes in accumulation and timing of onset of melt. However, neither bulk density of near-surface layers nor the amounts of liquid water and percolation depths predicted by MAR correspond with upGPR data. Radar data and records of a nearby thermistor string, in contrast, matched very well for both timing and depth of temperature changes and observed water percolations. All four melt events transferred a cumulative mass of 56 kg/(sq. m) into firn beneath the summer surface of 2015. We find that continuous observations of liquid water content, percolation depths and rates for the seasonal mass fluxes are sufficiently accurate to provide valuable information for validation of model approaches and help to develop a better understanding of liquid water retention and percolation in perennial firn.

Heilig, Achim↗

NASA NTRS1997

A Comparison of Sea Ice Type, Sea Ice Temperature, and Snow Thickness Distributions in the Arctic Seasonal Ice Zones with the DMSP SSM/I

Global climate studies have shown that sea ice is a critical component in the global climate system through its effect on the ocean and atmosphere, and on the earth's radiation balance. Polar energy studies have further shown that the distribution of thin ice and open water largely controls the distribution of surface heat exchange between the ocean and atmosphere within the winter Arctic ice pack. The thickness of the ice, the depth of snow on the ice, and the temperature profile of the snow/ice composite are all important parameters in calculating surface heat fluxes. In recent years, researchers have used various combinations of DMSP SSMI channels to independently estimate the thin ice type (which is related to ice thickness), the thin ice temperature, and the depth of snow on the ice. In each case validation efforts provided encouraging results, but taken individually each algorithm gives only one piece of the information necessary to compute the energy fluxes through the ice and snow. In this paper we present a comparison of the results from each of these algorithms to provide a more comprehensive picture of the seasonal ice zone using passive microwave observations.

St.Germain, Karen↗

NASA NTRS2024

Integrating State Data Assimilation and Innovative Model Parameterization Reduces Simulated Carbon Uptake in the Arctic and Boreal Region

Model representation of carbon uptake and storage is essential for accurate projection of the response of the arctic‐boreal zone to a rapidly changing climate. Land model estimates of LAI and aboveground biomass that can have a marked influence on model projections of carbon uptake and storage vary substantially in the arctic and boreal zone, making it challenging to correctly evaluate model estimates of Gross Primary Productivity (GPP). To understand and correct bias of LAI and aboveground biomass in the Community Land Model (CLM), we assimilated the 8‐day Moderate Resolution Imaging Spectroradiometer (MODIS) LAI observation and a machine learning product of annual aboveground biomass into CLM using an Ensemble Adjustment Kalman Filter (EAKF) in an experimental region including Alaska and Western Canada. Assimilating LAI and aboveground biomass reduced these model estimates by 58% and 72%, respectively. The change of aboveground biomass was consistent with independent estimates of canopy top height at both regional and site levels. The International Land Model Benchmarking system assessment showed that data assimilation significantly improved CLM's performance in simulating the carbon and hydrological cycles, as well as in representing the functional relationships between LAI and other variables. To further reduce the remaining bias in GPP after LAI bias correction, we re‐parameterized CLM to account for low temperature suppression of photosynthesis. The LAI bias corrected model that included the new parameterization showed the best agreement with model benchmarks. Combining data assimilation with model parameterization provides a useful framework to assess photosynthetic processes in LSMs.

land data assimilation↗

NASA NTRS2019

Enhanced Habitability on High Obliquity Bodies Near the Outer Edge of the Habitable Zone of Sun-Like Stars

High obliquity planets represent potentially extreme limits of terrestrial climate, as they exhibit large seasonality, a reversed annual-mean pole-to-equator gradient of stellar heating, and novel cryospheres. A suite of 3-D global climate model simulations with a dynamic ocean is performed with Earthlike atmospheres for low and high obliquity planets with various stellar fluxes, CO2 concentrations, and initial conditions to explore the propensity for high obliquity climates approaching the outer edge of the Habitable Zone to undergo global glaciation. We also simulate planets with thick CO2 or H2 atmospheres, such as those expected to develop near or beyond the outer edge of the Habitable Zone. We show that high obliquity planets are hotter than their low obliquity counterparts due to ice-albedo feedbacks for cold climates, and water vapor in warm climates. We suggest that the water vapor greenhouse trapping is greater on high obliquity bodies due to the different dynamical regimes that occur between the two states. While equatorial ice-belts are stable at high obliquity in some climate regimes, it is harder to achieve global glaciation than for a low obliquity planet. Temperate polar conditions can be present at high obliquity at forcings for which low obliquity planets would be in a hard snowball state. We suggest the conditions on high obliquity planets are likely to be more favorable for a robust biosphere to develop approaching the outer edge of the HZ. However, the influence of obliquity diminishes for dense atmospheres, in agreement with calculations from 1-D Energy Balance Models.

planets and satellites: atmospheres↗

NASA NTRS1993

Habitable zones around main sequence stars

A mechanism for stabilizing climate on the earth and other earthlike planets is described, and the physical processes that define the inner and outer boundaries of the habitable zone (HZ) around the sun and main sequence stars are discussed. Physical constraints on the HZ obtained from Venus and Mars are taken into account. A 1D climate model is used to estimate the width of the HZ and the continuously habitable zone around the sun, and the analysis is extended to other main sequence stars. Whether other stars have planets and where such planets might be located with respect to the HZ is addressed. The implications of the findings for NASA's SETI project are considered.

Kasting, James F.↗

NASA NTRS2010

Mineralogy of Antarctica Dry Valley Soils: Implications for Pedogenic Processes on Mars

The Antarctic Dry Valleys (ADVs) located in the Transantarctic Mountains are the coldest and driest locations on Earth. The mean annual air temperature is -20 C or less and the ADVs receive 100mm or less of precipitation annually in the form of snow. The cold and dry climate in the ADVs is one of the best terrestrial analogs for the climatic conditions on Mars [2]. The soils in the ADVs have been categorized into three soil moisture zones: subxerous, xerous and ultraxerous. The subxerous zone is a coastal region in which soils have ice-cemented permafrost relatively close to the surface. Moisture is available in relatively large amounts and soil temperatures are above freezing throughout the soil profile (above ice permafrost) in summer months. The xerous zone, the most widespread of the three zones, is an inland region with a climate midway between the subxerous and ultraxerous. The soils from this zone have dry permafrost at moderate depths (30-75cm) but have sufficient water in the upper soil horizons to allow leaching of soluble materials. The ultraxerous zone is a high elevation zone, where both temperature and precipitation amounts are very low resulting in dry permafrost throughout the soil profile. The three moisture regime regions are similar to the three microclimatic zones (coastal thaw, inland mixed, stable upland) defined by Marchant and Head.

Quinn, J. E.↗

NASA NTRS2019

Simulated Phase-dependent Spectra of Terrestrial Aquaplanets in M Dwarf Systems

Orbital phase-dependent variations in thermal emission and reflected stellar energy spectra can provide meaningful constraints on the climate states of terrestrial extrasolar planets orbiting M dwarf stars. Spatial distributions of water vapor, clouds, and surface ice are controlled by climate. In turn, water, in each of its thermodynamic phases, imposes significant modulations to thermal and reflected planetary spectra. Here we explore these characteristic spectral signals, based on 3D climate simulations of Earth-sized aquaplanets orbiting M dwarf stars near the habitable zone. By using 3D models, we can self-consistently predict surface temperatures and the location ofwater vapor, clouds, and surface ice in the climate system. Habitable zone planets in M dwarf systems are expected to be in synchronous rotation with their host star and thus present distinct differences in emitted and reflected energy fluxes depending on the observed hemisphere. Here we illustrate that icy, temperate, and incipient runaway greenhouse climate states exhibit phase-dependent spectral signals that enable their characterization.

Wolf, E. T.↗

NASA NTRS1993

Comparison of dynamic height variations in the tropical Atlantic during 1987-1989 as viewed in sections hydrography and Geosat altimetry

During the 1980s the Soviet Union conducted a series of 25 hydrographic cruises in the tropical Atlantic under the Sections (acronym for Energetically Active Zones of the Ocean and Climate Variability) program. As one of its objectives, Sections has produced the most extensive hydrographic data set ever collected in the tropical Atlantic. Here we examine dynamic topography from seven cruises during the years 1987 through 1989 and compare them to corresponding maps of sea level from the Geosat satellite mission. The two independent estimates of sea level variation are well correlated, with the sea level fields explaining approximately half of the variance of dynamic height fields. There is a seasonal variation to the agreement, so that the correlation increases somewhat in summer and fall, as the variability of the height field increases. Wavenumber spectra of both dynamic height and sea level are quite red for wavelengths greater than 400-600 km. Smaller wavelengths cannot be resolved by the hydrographic sampling program. A comparison of the dynamic height and sea level spectra indicates that sea level has somewhat less variability than dynamic height at all wavenumbers.

Carton, James A.↗

NASA NTRS2014

Global-Scale Comparison of Passive (SMOS) and Active (ASCAT) Satellite Based Microwave Soil Moisture Retrievals with Soil Moisture Simulations (MERRA-Land)

Global surface soil moisture (SSM) datasets are being produced based on active and passive microwave satellite observations and simulations from land surface models (LSM). This study investigates the consistency of two global satellite-based SSM datasets based on microwave remote sensing observations from the passive Soil Moisture and Ocean Salinity (SMOS;SMOSL3 version 2.5) and the active Advanced Scatterometer (ASCAT; version TUWien- WARP 5.5) with respect to LSM SSM from the MERRA-Land data product. The relationship between the global-scale SSM products was studied during the 2010-2012 period using (1) a time series statistics (considering both original SSM data and anomalies), (2) a space-time analysis using Hovmoller diagrams, and (3) a triple collocation error model. The SMOSL3 and ASCAT retrievals are consistent with the temporal dynamics of modeled SSM (correlation R (is) greater than 0.70 for original SSM) in the transition zones between wet and dry climates, including the Sahel, the Indian subcontinent, the Great Plains of North America, eastern Australia, and southeastern Brazil. Over relatively dense vegetation covers, a better consistency with MERRA-Land was obtained with ASCAT than with SMOSL3. However, it was found that ASCAT retrievals exhibit negative correlation versus MERRA-Land in some arid regions (e.g., the Sahara and the Arabian Peninsula). In terms of anomalies, SMOSL3 better captures the short term SSM variability of the reference dataset (MERRA-Land) than ASCAT over regions with limited radio frequency interference (RFI) effects (e.g., North America, South America, and Australia). The seasonal and latitudinal variations of SSM are relatively similar for the three products, although the MERRALand SSM values are generally higher and their seasonal amplitude is much lower than for SMOSL3 and ASCAT. Both SMOSL3 and ASCAT have relatively comparable triple collocation errors with similar spatial error patterns: (i) lowest errors in arid regions (e.g., Sahara and Arabian Peninsula), due to the very low natural variability of soil moisture in these areas, and Central America, and (ii) highest errors over most of the vegetated regions (e.g., northern Australia, India, central Asia, and South America). However, the ASCAT SSM product is prone to larger random errors in some regions (e.g., north-western Africa, Iran, and southern South Africa). Vegetation density was found to be a key factor to interpret the consistency with MERRA-Land between the two remotely sensed products (SMOSL3 and ASCAT) which provides complementary information on SSM. This study shows that both SMOS and ASCAT have thus a potential for data fusion into long-term data records.

ASCAT↗

NASA NTRS2018

Global Assessment of the SMAP Level-4 Soil Moisture Product Using Assimilation Diagnostics

The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with approx. 2.5-day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O-F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of approx. 0.37 K for the O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under approx. 3 K), the soil moisture increments (under approx. 0.01 cu m/cu m), and the surface soil temperature increments (under approx. 1 K). Typical instantaneous values are approx. 6 K for O-F residuals, approx. 0.01 (approx. 0.003) cu m/cu m for surface (root-zone) soil moisture increments, and approx. 0.6 K for surface soil temperature increments. The O-F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O-F auto-correlations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.

Reichle, Rolf↗

NASA NTRS2018

Using Data Assimilation Diagnostics to Assess the SMAP Level-4 Soil Moisture Product

The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with approx.2.5-day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O-F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of approx. 0.37 K for the O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under approx. 3 K), the soil moisture increments (under approx. 0.01 cu m/cu m), and the surface soil temperature increments (under approx. 1 K). Typical instantaneous values are approx. 6 K for O-F residuals, approx. 0.01 (approx. 0.003) cu m/cu m for surface (root-zone) soil moisture increments, and approx. 0.6 K for surface soil temperature increments. The O-F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O-F auto-correlations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.

SMAP↗

NASA NTRS2015

Stratospheric Temperatures and Water Loss From Moist Greenhouse Atmospheres of Earth-Like Planets

A radiative-convective climate model is used to calculate stratospheric temperatures and water vapor concentrations for ozone-free atmospheres warmer than that of modern Earth. Cold, dry stratospheres are predicted at low surface temperatures, in agreement with recent 3D calculations. However, at surface temperatures above 350 K, the stratosphere warms and water vapor becomes a major upper atmospheric constituent, allowing water to be lost by photodissociation and hydrogen escape. Hence, a moist greenhouse explanation for loss of water from Venus, or some exoplanet receiving a comparable amount of stellar radiation, remains a viable hypothesis. Temperatures in the upper parts of such atmospheres are well below those estimated for a gray atmosphere, and this factor should be taken into account when performing inverse climate calculations to determine habitable zone boundaries using 1D models.

Astrobiology↗

NASA NTRS2017

Constraints on Climate and Habitability for Earth-like Exoplanets Determined from a General Circulation Model

Conventional definitions of habitability require abundant liquid surface water to exist continuously over geologic timescales. Water in each of its thermodynamic phases interacts with solar and thermal radiation and is the cause for strong climatic feedbacks. Thus, assessments of the habitable zone require models to include a complete treatment of the hydrological cycle over geologic time. Here, we use the Community Atmosphere Model from the National Center for Atmospheric Research to study the evolution of climate for an Earth-like planet at constant CO2, under a wide range of stellar fluxes from F-, G-, and K-dwarf main sequence stars. Around each star we find four stable climate states defined by mutually exclusive global mean surface temperatures (Ts); snowball (Ts ≤ 235 K), waterbelt (235 K ≤ Ts ≤ 250 K), temperate (275 K ≤ Ts ≤ 315 K), and moist greenhouse (Ts ≥ 330 K). Each is separated by abrupt climatic transitions. Waterbelt, temperate, and cooler moist greenhouse climates can maintain open-ocean against both sea ice albedo and hydrogen escape processes respectively, and thus constitute habitable worlds. We consider the warmest possible habitable planet as having Ts ∼ 355 K, at which point diffusion limited water-loss could remove an Earth ocean in ∼1 Gyr. Without long timescale regulation of non-condensable greenhouse species at Earth-like temperatures and pressures, such as CO2, habitability can be maintained for an upper limit of ∼2.2, ∼2.4, and ∼4.7 Gyr around F-, G-, and K-dwarf stars respectively, due to main sequence brightening.

Eric T Wolf↗

NASA NTRS2017

Constraints on Climate and Habitability for Earth-like Exoplanets Determined from a General Circulation Model

Conventional definitions of habitability require abundant liquid surface water to exist continuously over geologic timescales. Water in each of its thermodynamic phases interacts with solar and thermal radiation and is the cause for strong climatic feedbacks. Thus, assessments of the habitable zone require models to include a complete treatment of the hydrological cycle over geologic time. Here, we use the Community Atmosphere Model from the National Center for Atmospheric Research to study the evolution of climate for an Earth-like planet at constant CO2, under a wide range of stellar fluxes from F-, G-, and K-dwarf main sequence stars. Around each star we find four stable climate states defined by mutually exclusive global mean surface temperatures (T(sub s)); snowball (T(sub s) ≼ 235 K), waterbelt (235 K ≼ T(sub s) ≼ 250 K), temperate (275 K ≼ T(sub s) ≼ 315 K), and moist greenhouse (T(sub s) ≽ 330 K). Each is separated by abrupt climatic transitions. Waterbelt, temperate, and cooler moist greenhouse climates can maintain open-ocean against both sea ice albedo and hydrogen escape processes respectively, and thus constitute habitable worlds. We consider the warmest possible habitable planet as having T(sub s) ∼ 355 K, at which point diffusion limited water-loss could remove an Earth ocean in ∼1 Gyr. Without long timescale regulation of non-condensable greenhouse species at Earth-like temperatures and pressures, such as CO2, habitability can be maintained for an upper limit of ∼2.2, ∼2.4, and ∼4.7 Gyr around F-, G-, and K-dwarf stars respectively, due to main sequence brightening.

Astrobiology↗

NASA NTRS2012

Role of Temperature, Humidity and Rainfall on Influenza Transmission in Guatemala, El Salvador and Panama

Worldwide, seasonal influenza causes about 500,000 deaths and 5 million severe illnesses per year. The environmental drivers of influenza transmission are poorly understood especially in the tropics. We aimed to identify meteorological factors for influenza transmission in tropical Central America. We gathered laboratory-confirmed influenza case-counts by week from Guatemala City, San Salvador Department (El Salvador) and Panama Province from 2006 to 2010. The average total cases per year were: 390 (Guatemala), 99 (San Salvador) and 129 (Panama). Meteorological factors including daily air temperature, rainfall, relative and absolute humidity (RH, AH) were obtained from ground stations, NASA satellites and land models. For these factors, we computed weekly averages and their deviation from the 5-yr means. We assessed the relationship between the number of influenza case-counts and the meteorological factors, including effects lagged by 1 to 4 weeks, using Poisson regression for each site. Our results showed influenza in San Salvador would increase by 1 case within a week of every 1 day with RH>75% (Relative Risk (RR)= 1.32, p=.001) and every 1C increase in minimum temperature (RR=1.29, p=.007) but it would decrease by 1 case for every 1mm-above mean weekly rainfall (RR=0.93,p<.001) (model pseudo-R2=0.55). Within 2 weeks, influenza in Panama was increased by 1 case for every 1% increase in RH (RR=1.04, p=.003), and it was increased by 2 cases for every 1C increase of minimum temperature (RR=2.01, p<.001) (model pseudo-R2=0.4). Influenza counts in Guatemala had 1 case increase for every 1C increase in minimum temperature in the previous week (RR=1.21, p<.001), and for every 1mm/day-above normal increase of rainfall rate (RR=1.03, p=.03) (model pseudo-R2=0.54). Our findings that cases increase with temperature and humidity differ from some temperate-zone studies. But they indicate that climate parameters such as humidity and temperature could be predictive of influenza activity and should be incorporated into country-specific influenza transmission models

Soebiyanto, Radina P.↗

NASA NTRS2016

FluidCam 1 & 2 - UAV-based Fluid Lensing Instruments for High-Resolution 3D Subaqueous Imaging and Automated Remote Biosphere Assessment of Reef Ecosystems

We present NASA ESTO FluidCam 1 & 2, Visible and NIR Fluid-Lensing-enabled imaging payloads for Unmanned Aerial Vehicles (UAVs). Developed as part of a focused 2014 earth science technology grant, FluidCam 1&2 are Fluid-Lensing-based computational optical imagers designed for automated 3D mapping and remote sensing of underwater coastal targets from airborne platforms. Fluid Lensing has been used to map underwater reefs in 3D in American Samoa and Hamelin Pool, Australia from UAV platforms at sub-cm scale, which has proven a valuable tool in modern marine research for marine biosphere assessment and conservation. We share FluidCam 1&2 instrument validation and testing results as well as preliminary processed data from field campaigns. Petabyte-scale aerial survey efforts using Fluid Lensing to image at-risk reefs demonstrate broad applicability to large-scale automated species identification, morphology studies and reef ecosystem characterization for shallow marine environments and terrestrial biospheres, of crucial importance to improving bathymetry data for physical oceanographic models and understanding climate change’s impact on coastal zones, global oxygen production, carbon sequestration.

Chirayath, Ved↗

NASA NTRS2019

Sensitivity of Maize Yield in Smallholder Systems to Climate Scenarios in Semi-Arid Regions of West Africa: Accounting for Variability in Farm Management Practices

Climate change is estimated to exacerbate existing challenges faced by smallholder farmers in Sub-Sahara Africa. However, limited studies quantify the extent of variation in climate change impact under these systems at the local scale. The Decision Support System for Agro-technological Transfer (DSSAT) was used to quantify variation in climate change impacts on maize yield under current agricultural practices in semi-arid regions of Senegal (Nioro du Rip) and Ghana (Navrongo and Tamale). Multi-benchmark climate models (Mid-Century, 2040–2069 for two Representative Concentration Pathways, RCP4.5 and RCP8.5), and multiple soil and management information from agronomic surveys were used as input for DSSAT. The average impact of climate scenarios on grain yield among farms ranged between −9% and −39% across sites. Substantial variation in climate response exists across farms in the same farming zone with relative standard deviations from 8% to 117% at Nioro du Rip, 13% to 64% in Navrongo and 9% to 37% in Tamale across climate models. Variations in fertilizer application, planting dates and soil types explained the variation in the impact among farms. This study provides insight into the complexities of the impact of climate scenarios on maize yield and the need for better representation of heterogeneous farming systems for optimized outcomes in adaptation and resilience planning in smallholder systems.

Freduah, Bright S.↗