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NASA NTRSDate not supplied

Open Science Approach to Analyze Climate-Crop Relationships in the US Leveraging GES DISC and Galaxy Workflows

Understanding the intricate relationship between climate variability and agricultural production is crucial for ensuring food security. This study investigates the impact of climate parameters, such as temperature, precipitation, and soil moisture, on major US crop yields. Adopting an open science approach, the study analyzes the impact of climate on agricultural production in the United States. The Galaxy workflow engine serves as the primary tool for integrating climate data from the Goddard Earth Sciences Data and Information Services Center (GES DISC), retrieved via the Giovanni system, with yield statistics from the United States Department of Agriculture’s National Agricultural Statistics Service (USDA NASS). Extensions for reading, preprocessing, and analyzing external data have been developed, enabling the creation of workflows within the Galaxy platform. The development of a reproducible workflow allows for the calculation of seasonal climate averages, which are then assessed for their correlation with crop yields. This methodology ensures the replicability of the research, promoting transparency and collaboration in the scientific community. Correlational and regression analyses have been applied to different sub-zones and crops. The findings from this research offer valuable insights into the relationship between climate parameters and crop yields. These insights contribute to a deeper understanding of climate-crop relationships, providing a solid foundation for informed decision-making in the agricultural sector. The high correlation values indicate a significant relationship between climate parameters and crop yields, underscoring the importance of considering climate factors in agricultural planning and policymaking. This research also exemplifies the power of open science in advancing our understanding of complex environmental and agricultural phenomena. By leveraging open data and services, it provides a robust and replicable framework for future studies in this critical field.

Open science↗

NASA NTRS2023

Water Cycle Science Enabled By the GRACE and GRACE-FO Satellite Missions

Satellite observations of the time-variable gravity field revolutionized the monitoring of large-scale water storage changes, beginning with the 2002 launch of the Gravity Recovery and Climate Experiment (GRACE) mission. Most hydrologists were sceptical of the satellite gravimetry approach at first, but validation studies assuaged their concerns and high-profile, GRACE-based groundwater depletion studies caused an explosion of interest. The importance of GRACE observations for hydrologic and cryospheric science became so great that GRACE Follow-On (GRACE-FO) jumped the National Aeronautics and Space Administration’s Earth science mission queue and launched in 2018. A third mass change mission is currently under development. Here, we review key milestones in satellite gravimetry’s progression from the fringes of hydrology to being a staple of large-scale water cycle and water resources studies and the sole source of observations of what is now an ‘essential climate variable’, terrestrial water storage.

climate change↗

NASA NTRS2019

Satellite Ocean Colour: Current Status and Future Perspective

Spectrally resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and inter-annual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS, and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes the current status and future prospects in the field of ocean colour focusing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focusing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean colour are described and finally a summary of future perspectives is provided.

ocean colour↗

NASA NTRS2001

Flying the TRMM Satellite in a GCM

By incorporating the Tropical Rainfall Measurement Mission (TRMM) satellite orbital information into the Colorado State University General Circulation Model (CSU GCM), we are able to 'fly' a satellite in the GCM, and sample the simulated atmosphere in the same way as the TRMM sensors sample the real atmosphere. The TRMM-sampled statistics for precipitation and radiative fluxes at annual, intraseasonal, monthly-mean and seasonal-mean diurnal time scales are evaluated by comparing the satellite-sampled against fully-sampled simulated atmospheres. The sampling rates of the TRMM sensors are significantly affected by the sensors' swath widths. The TRMM Microwave Imager (TMI) and the Visible Infrared Scanner (VIRS) sample each 2.25 x 2.25 degree grid box in the Tropics and subtropics about once per day, but at a different local time every day, while the Precipitation Radar (PR) and the Clouds and the Earth's Radiant Energy System (CERES) sensor visit each grid box about once every three days and twice per day, respectively. Besides inadequate samplings resulting from sensors' swath widths, there is a large, systematic diurnal undersampling associated with TRMM's orbital geometry for grid boxes away from the Equator. When only one month of TRMM data are used, this diurnal undersampling can lead to more daytime samples relative to nighttime samples in one hemisphere, and more nighttime samples relative to daytime samples in the other hemisphere. The resulting sampling biases (3-6 W m(exp-2)) are very pronounced in outgoing longwave radiation (OLR) over the subtropical land masses. The sampling errors in OLR monthly- and seasonal-means are less than 8 W m(exp-2) (5%) for each 2.25 x 2.25 degree grid box. The OLR monthly- and seasonal-means are not sensitive to diurnal undersamplings associated with the TRMM orbits and sensors' swath widths. However, this is not the case for total precipitation. Diurnal undersampling could produce errors as large as 20% in the Tropics and 40% in the subtropics, for the zonally averaged monthly-mean rain rates. The TRMM orbits sample each 2.25 x 2.25 degree grid box in the Tropics and subtropics 1-6 times for each hour of the day within a single season. The seasonal-mean diurnal cycles of precipitation and OLR are not well sampled for any one grid box. By either accumulating the satellite data for a long enough period, or averaging the data over a large area with a relatively uniform diurnal signal, the diurnal cycles of precipitation and OLR can be satisfactorily sampled. The effects TRMM sampling errors on the inferred tropical-mean hydrologic cycle and radiative fluxes are also evaluated. There are strong spurious oscillations associated with TRMM's orbital geometry, with periods of 23 days and 3-4 months, in tropical-mean daily and monthly precipitation. While the relative fluctuations of the sampled-OLR are negligible, the relative fluctuations of the sampled precipitation have magnitudes similar to those of the observed climate variability. Caution must therefore be used when applying TRMM observations of tropical-mean precipitation to interpret climate variations at intraseasonal and interannual scales.

Lin, Xin↗

NASA NTRS2014

Assessment of Satellite Ocean Colour Radiometry and Derived Geophysical Products

Standardization of methods to assess and assign quality metrics to satellite ocean color radiometry and derived geophysical products has become paramount with the inclusion of the marine reflectance and chlorophyll-a concentration (Chla) as essential climate variables (ECV; [1]) and the recognition that optical remote sensing of the oceans can only contribute to climate research if and when a continuous succession of satellite missions can be shown to collectively provide a consistent, long-term record with known uncertainties. In 20 years, the community has made significant advancements toward that objective, but providing a complete uncertainty budget for all products and for all conditions remains a daunting task. In the retrieval of marine water-leaving radiance from observed top-of-atmosphere radiance, the sources of uncertainties include those associated with propagation of sensor noise and radiometric calibration and characterization errors, as well as a multitude of uncertainties associated with the modeling and removal of effects from the atmosphere and sea surface. This chapter describes some common approaches used to assess quality and consistency of ocean color satellite products and reviews the current status of uncertainty quantification in the field. Its focus is on the primary ocean color product, the spectrum of marine reflectance Rrs, but uncertainties in some derived products such as the Chla or inherent optical properties (IOPs) will also be considered.

Ocean↗

NASA NTRS2017

The 2015-2016 El Nino and the Response of the Carbon Cycle: Findings from NASA's OCO-2 Mission

The El Nino Southern Oscillation (ENSO) is the most important mode of tropical climate variability on interannual to decadal time scales. Correlations between atmospheric CO2 growth rate and ENSO activity are relatively well known but the magnitude of this correlation, the contribution from tropical marine vs. terrestrial flux components, and the causal mechanisms, are poorly constrained in space and time. The launch of NASA's Orbiting Carbon Observatory-2 (OCO-2) mission in July 2014 was rather timely given the development of strong ENSO conditions over the tropical Pacific Ocean in 2015-2016. In this presentation, we will discuss how the high-density observations from OCO-2 provided us with a novel dataset to resolve the linkages between El Nino and atmospheric CO2. Along with information from in situ observations of ÎpCO2 from NOAA's Tropical Atmosphere Ocean (TAO) project and atmospheric CO2 from the Scripps CO2 Program, and other remote-sensing missions, we are able to piece together the time dependent response of atmospheric CO2 concentrations over the Tropics. Our findings confirm the hypothesis from studies following the 1997-1998 El Nino event that an early reduction in CO2 outgassing from the tropical Pacific Ocean is later reversed by enhanced net CO2 emissions from the terrestrial biosphere. This implies that a component of the interannual variability (IAV) in the growth rate of atmospheric CO2, which has typically been used to constrain the climate sensitivity of tropical land carbon fluxes, is strongly influenced and modified by ocean fluxes during the early phase of the ENSO event. Our analyses shed further light on the understanding of the marine vs. terrestrial partitioning of tropical carbon fluxes during El Nino events, their relative contributions to the global atmospheric CO2 growth rate, and provide clues about the sensitivity of the carbon cycle to climate forcing on interannual time scales.

CO2↗

NASA NTRS2018

The 2015-2016 El Nino and the Response of the Carbon Cycle: Findings from NASA's OCO-2 Mission

The El Nino Southern Oscillation (ENSO) is the most important mode of tropical climate variability on interannual to decadal time scales. Correlations between atmospheric CO2 growth rate and ENSO activity are relatively well known but the magnitude of this correlation, the contribution from tropical marine vs. terrestrial flux components, and the causal mechanisms, are poorly constrained in space and time. The launch of NASA's Orbiting Carbon Observatory-2 (OCO-2) mission in July 2014 was rather timely given the development of strong ENSO conditions over the tropical Pacific Ocean in 2015-2016. In this presentation, we will discuss how the high-density observations from OCO-2 provided us with a novel dataset to resolve the linkages between El Nino and atmospheric CO2. Along with information from in situ observations of CO2 from NOAA's Tropical Atmosphere Ocean (TAO) project and atmospheric CO2 from the Scripps CO2 Program, and other remote-sensing missions, we are able to piece together the time dependent response of atmospheric CO2 concentrations over the Tropics. Our findings confirm the hypothesis from studies following the 1997-1998 El Nino event that an early reduction in CO2 outgassing from the tropical Pacific Ocean is later reversed by enhanced net CO2 emissions from the terrestrial biosphere. This implies that a component of the interannual variability (IAV) in the growth rate of atmospheric CO2, which has typically been used to constrain the climate sensitivity of tropical land carbon fluxes, is strongly influenced and modified by ocean fluxes during the early phase of the ENSO event. Our analyses shed further light on the understanding of the marine vs. terrestrial partitioning of tropical carbon fluxes during El Nino events, their relative contributions to the global atmospheric CO2 growth rate, and provide clues about the sensitivity of the carbon cycle to climate forcing on interannual time scales.

OCO-2↗

NASA NTRSDate not supplied

TPSAS-NF1676L-33231-DND

It is well established that clouds have a profound influence on Earth's energy budget and that cloud feedbacks are responsible for much of the uncertainty in climate model projections of global warming in response to radiative forcing by greenhouse gases. While most model evaluation efforts focus on model representation of the observed mean state. For example, the regional mean climatology of key climate variables there is also tremendous value in testing how a model represents observed internal variations of the climate system. Here we use CERES observations during and after the so-called global warming hiatus to evaluate a subset of CMIP6 climate models. The CERES data show a marked 0.83 Wm-2 reduction in global mean reflected SW TOA flux during the three years following the hiatus that results in an increase in net energy into the climate system. The primary driver of the decrease in SW TOA flux is a reduction in low cloud cover over the eastern Pacific Ocean that occurs in response to an unprecedented increase in SSTs in that region following the hiatus. The CMIP6 simulations consist of AMIP-style experiments in which SSTs and sea-ice boundary conditions are constrained by observations. For each model, the AMIP runs were extended through the end of 2017 from the official CMIP6 AMIP end period in 2014. Results show that the models do a good job at capturing the pattern of observed changes in TOA flux observed by CERES, but tend to underestimate the magnitude.

Norman G Loeb↗

NASA NTRS2020

Satellite Monitoring of Global Surface Soil Organic Carbon Dynamics Using the SMAP Level 4 Carbon Product

Soil organic carbon (SOC) is an important metric of soil health and the terrestrial carbon balance. Short‐term climate variations affect SOC through changes in temperature and moisture, which control vegetation growth and soil decomposition. We evaluated a satellite data‐driven carbon model, operating under the NASA Soil Moisture Active‐Passive (SMAP) mission, as a means of monitoring global surface SOC dynamics. The SMAP Level 4 Carbon (L4C) product estimates a daily global carbon budget including surface (0‐ to 5‐cm depth) SOC. We found that the L4C mean latitudinal SOC distribution is generally consistent with alternative assessments from static soil inventory records and dynamic global vegetation models (r ≥ 0.89). Within forest systems, based on inventory data, L4C SOC is most similar in magnitude to litterfall but is correlated with coarse woody debris ( urn:x-wiley:jgrg:media:jgrg21790:jgrg21790-math-0001) and total SOC ( urn:x-wiley:jgrg:media:jgrg21790:jgrg21790-math-0002). L4C SOC is sensitive to seasonal and annual climate variability, with mean residence times that range from 1.5 years in the wet tropics to 17 years in the cold tundra. Incorporating soil moisture retrievals from the SMAP L‐band (1.4 GHz) microwave radiometer within the L4C algorithm provides enhanced soil moisture sensitivity under low‐to‐moderate vegetation cover (<5 kg/sq.m vegetation water content). The L‐band soil moisture had the greatest impact on the L4C carbon budget in semiarid regions, which span almost 60% of the globe and account for substantial variability in the terrestrial carbon sink. The L4C operational product enables prognostic investigations into effects of recent climate trends and anomalies (e.g., droughts and pluvials) on shallow soil carbon dynamics.

K. Arthur Endsley↗

NASA NTRS2014

Use of Machine Learning Techniques for Iidentification of Robust Teleconnections to East African Rainfall Variability in Observations and Models

Providing advance warning of East African rainfall variations is a particular focus of several groups including those participating in the Famine Early Warming Systems Network. Both seasonal and long-term model projections of climate variability are being used to examine the societal impacts of hydrometeorological variability on seasonal to interannual and longer time scales. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of both seasonal and climate model projections to develop downscaled scenarios for using in impact modeling. The utility of these projections is reliant on the ability of current models to capture the embedded relationships between East African rainfall and evolving forcing within the coupled ocean-atmosphere-land climate system. Previous studies have posited relationships between variations in El Niño, the Walker circulation, Pacific decadal variability (PDV), and anthropogenic forcing. This study applies machine learning methods (e.g. clustering, probabilistic graphical model, nonlinear PCA) to observational datasets in an attempt to expose the importance of local and remote forcing mechanisms of East African rainfall variability. The ability of the NASA Goddard Earth Observing System (GEOS5) coupled model to capture the associated relationships will be evaluated using Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations.

Roberts, J. Brent↗

NASA NTRS2011

Applying Geospatial Technologies for International Development and Public Health: The USAID/NASA SERVIR Program

Background: SERVIR -- the Regional Visualization and Monitoring System -- helps people use Earth observations and predictive models based on data from orbiting satellites to make timely decisions that benefit society. SERVIR operates through a network of regional hubs in Mesoamerica, East Africa, and the Hindu Kush-Himalayas. USAID and NASA support SERVIR, with the long-term goal of transferring SERVIR capabilities to the host countries. Objective/Purpose: The purpose of this presentation is to describe how the SERVIR system helps the SERVIR regions cope with eight areas of societal benefit identified by the Group on Earth Observations (GEO): health, disasters, ecosystems, biodiversity, weather, water, climate, and agriculture. This presentation will describe environmental health applications of data in the SERVIR system, as well as ongoing and future efforts to incorporate additional health applications into the SERVIR system. Methods: This presentation will discuss how the SERVIR Program makes environmental data available for use in environmental health applications. SERVIR accomplishes its mission by providing member nations with access to geospatial data and predictive models, information visualization, training and capacity building, and partnership development. SERVIR conducts needs assessments in partner regions, develops custom applications of Earth observation data, and makes NASA and partner data available through an online geospatial data portal at SERVIRglobal.net. Results: Decision makers use SERVIR to improve their ability to monitor air quality, extreme weather, biodiversity, and changes in land cover. In past several years, the system has been used over 50 times to respond to environmental threats such as wildfires, floods, landslides, and harmful algal blooms. Given that the SERVIR regions are experiencing increased stress under larger climate variability than historic observations, SERVIR provides information to support the development of adaptation strategies for nations affected by climate change. Conclusions: SERVIR is a platform for collaboration and cross-agency coordination, international partnerships, and delivery of web-based geospatial information services and applications. SERVIR makes a variety of geospatial data available for use in studies of environmental health outcomes.

Hemmings, Sarah↗

NASA NTRS2014

Irrigation as an Historical Climate Forcing

Irrigation is the single largest anthropogenic water use, a modification of the land surface that significantly affects surface energy budgets, the water cycle, and climate. Irrigation, however, is typically not included in standard historical general circulation model (GCM) simulations along with other anthropogenic and natural forcings. To investigate the importance of irrigation as an anthropogenic climate forcing, we conduct two 5-member ensemble GCM experiments. Both are setup identical to the historical forced (anthropogenic plus natural) scenario used in version 5 of the Coupled Model Intercomparison Project, but in one experiment we also add water to the land surface using a dataset of historically estimated irrigation rates. Irrigation has a negligible effect on the global average radiative balance at the top of the atmosphere, but causes significant cooling of global average surface air temperatures over land and dampens regional warming trends. This cooling is regionally focused and is especially strong in Western North America, the Mediterranean, the Middle East, and Asia. Irrigation enhances cloud cover and precipitation in these same regions, except for summer in parts of Monsoon Asia, where irrigation causes a reduction in monsoon season precipitation. Irrigation cools the surface, reducing upward fluxes of longwave radiation (increasing net longwave), and increases cloud cover, enhancing shortwave reflection (reducing net shortwave). The relative magnitude of these two processes causes regional increases (northern India) or decreases (Central Asia, China) in energy availability at the surface and top of the atmosphere. Despite these changes in net radiation, however, climate responses are due primarily to larger magnitude shifts in the Bowen ratio from sensible to latent heating. Irrigation impacts on temperature, precipitation, and other climate variables are regionally significant, even while other anthropogenic forcings (anthropogenic aerosols, greenhouse gases, etc.) dominate the long term climate evolution in the simulations. To better constrain the magnitude and uncertainties of irrigation-forced climate anomalies, irrigation should therefore be considered as another important anthropogenic climate forcing in the next generation of historical climate simulations and multimodel assessments.

simulation↗

NASA NTRS2016

Using Probabilistic Methods in Water Scarcity Assessments: A First Step Towards a Water Scarcity Risk Assessment Framework

Water scarcity -driven by climate change, climate variability, and socioeconomic developments- is recognized as one of the most important global risks, both in terms of likelihood and impact. Whilst a wide range of studies have assessed the role of long term climate change and socioeconomic trends on global water scarcity, the impact of variability is less well understood. Moreover, the interactions between different forcing mechanisms, and their combined effect on changes in water scarcity conditions, are often neglected. Therefore, we provide a first step towards a framework for global water scarcity risk assessments, applying probabilistic methods to estimate water scarcity risks for different return periods under current and future conditions while using multiple climate and socioeconomic scenarios.

water scarcity↗

NASA NTRS2021

Climate Change Impacts and Adaptation for Dryland Farming Systems in Zimbabwe: A Stakeholder-Driven Integrated Multi-Model Assessment

Decision makers need accurate information to address climate variability and change and accelerate transformation to sustainability. A stakeholder-driven, science-based multi-model approach has been developed and used by the Agricultural Model Intercomparison and Improvement Project (AgMIP) to generate actionable information for adaptation planning processes. For a range of mid-century climate projections—likely to be hotter, drier, and more variable—contrasting future socio-economic scenarios (Representative Agricultural Pathways, RAPs) were co-developed with stakeholders to portray a sustainable development scenario and a rapid economic growth pathway. The unique characteristic of this application is the integration of a multi-modeling approach with stakeholder engagement to co-develop scenarios and adaptation strategies. Distribution of outcomes were simulated with climate, crop, livestock, and economic impact assessment models for smallholder crop livestock farmers in a typical dryland agro-ecological zone in Zimbabwe, characterized by low and erratic rainfall and nutrient depleted soils. Results showed that in Nkayi District, Western Zimbabwe, climate change would threaten most of the farms, and, in particular, those with large cattle herds due to feed shortages. Adaptation strategies that showed the most promise included diversification using legume production, soil fertility improvement, and investment in conducive market environments. The switch to more legumes in the farming systems reduced the vulnerability of the very poor as well as the more resourced farmers. Overall, the sustainable development scenario consistently addressed institutional failures and motivated productivity-enhancing, environmentally sound technologies and inclusive development approaches. This yielded more favorable outcomes than investment in quick economic wins from commercializing agriculture.

Climate change adaptation↗

NASA NTRS2004

Correlations between Inter-Annual Variations in Arctic Sea Ice Extent, Greenland Surface Melt, and Boreal Snow Cover

Intensification of global warming in recent decades has caused a rise of interest in year-to-year and decadal-scale climate variability in the Arctic. This is because the Arctic is believed to be one of the most sensitive and vulnerable regions to climatic changes. For over two decades satellite passive microwave observations have been utilized to continuously monitor the Arctic environment. Derived parameters include sea ice cover, snow cover and snow water equivalent over land, and Greenland melt extent and length of melt season. Most studies have primarily concentrated on trends and variations of individual variables. In this study we investigated how variations in sea ice cover, Greenland surface melt, and boreal snow cover are correlated. This was done on hemispheric as well as on regional scales. Latest results will be presented including data from the summer of 2004.

Markus, Thorstena↗

NASA NTRS2011

Climate Hazard Assessment for Stakeholder Adaptation Planning in New York City

This paper describes a time-sensitive approach to climate change projections, developed as part of New York City's climate change adaptation process, that has provided decision support to stakeholders from 40 agencies, regional planning associations, and private companies. The approach optimizes production of projections given constraints faced by decision makers as they incorporate climate change into long-term planning and policy. New York City stakeholders, who are well-versed in risk management, helped pre-select the climate variables most likely to impact urban infrastructure, and requested a projection range rather than a single 'most likely' outcome. The climate projections approach is transferable to other regions and consistent with broader efforts to provide climate services, including impact, vulnerability, and adaptation information. The approach uses 16 Global Climate Models (GCMs) and three emissions scenarios to calculate monthly change factors based on 30-year average future time slices relative to a 30- year model baseline. Projecting these model mean changes onto observed station data for New York City yields dramatic changes in the frequency of extreme events such as coastal flooding and dangerous heat events. Based on these methods, the current 1-in-10 year coastal flood is projected to occur more than once every 3 years by the end of the century, and heat events are projected to approximately triple in frequency. These frequency changes are of sufficient magnitude to merit consideration in long-term adaptation planning, even though the precise changes in extreme event frequency are highly uncertain

Horton, Radley M.↗

NASA NTRS2022

Mineral Inputs, Paleoecological Change, and Holocene Carbon Accumulation at a Boreal Peatland in the Hudson Bay Lowlands, Canada

The Hudson Bay Lowlands (HBL) is a vast contiguous peatland extending over >370,000 km2 in Canada's boreal-subarctic, and is the traditional land of the Omushkego Cree. It is currently undergoing climatic warming alongside other anthropogenic stressors, and contains a large below-ground carbon pool. Understanding how climate variability and multiple stressors impact peat accumulation in this region is critical to discerning how northern peatlands will respond to future climate and land-use changes. Pollen- and macrofossil-based paleoecological reconstructions, and analyses of aluminum (Al) and titanium (Ti) fluxes in a Holocene-aged peat core (VM375) taken from a bog in the Attawapiskat watershed were conducted to link ecosystem changes with hydroclimate and long-term carbon storage. Peat initiation is dated to 5780 cal yr B.P., coincident with land emergence driven by glacial isostatic adjustment. From 4500 to 4200 cal yr B.P., apparent rate of carbon accumulation increased, and was linked to more rapid rates of peat accretion and increases in minerotrophic indicators in the pollen record. This increase in peat accretion and shift in vegetation composition co-occur with higher rates of mineral influx as shown by Ti and Al concentrations, which may have supplied nutrients. A fen to bog transition takes place ~3300 cal yr B.P., with increases in Sphagnum spores and macrofossils, and a decline in the apparent rate of carbon accumulation relative to the earlier half of the record, where paleoecological proxies indicate treed wetland and fen stages. Since peat inception, the total carbon stock of the 260-cm peat column is 110 kg C m−2. This multi-proxy record shows an association between changing peat types and variability in apparent rates of carbon accumulation, and supports the hypothesis that mineral nutrients either supplied by surface hydrology or by eolian deposition played a role in Holocene peat carbon accumulation in eastern North American boreal peatlands.

Dust↗

NASA NTRS2021

Tree-ring cellulose δ(18)O records similar large-scale climate influences as precipitation δ(18)O in the Northwest Territories of Canada

Stable oxygen isotopes measured in tree rings are useful for reconstructing climate variability and explaining changes in physiological processes occurring in forests, complementing other tree-ring parameters such as ring width. Here, we analyzed the relationships between different climate parameters and annually resolved tree-ring δ(18)O records (δ(18)O(TR)) from white spruce (Picea glauca [Moench]Voss) trees located near Tungsten (Northwest Territories, Canada) and used the NASA GISS ModelE2 isotopically-equipped general circulation model (GCM) to better interpret the observed relationships. We found that the δ(18)O(TR) series were primarily related to temperature variations in spring and summer, likely through temperature effects on the precipitation δ(18)O in spring, and evaporative enrichment at leaf level in summer. The GCM simulations showed significant positive relationships between modelled precipitation δ(18)O over the study region and surface temperature and geopotential height over northwestern North America, but of stronger magnitudes during fall-winter than during spring–summer. The modelled precipitation δ(18)O was only significantly associated with moisture transport during the fall-winter season. The δ(18)O(TR) showed similar correlation patterns to modelled precipitation δ(18)O only during spring–summer when water matters more for trees, with significant positive correlations with surface temperature and geopotential height, but no correlations with moisture transport. Overall, the δ(18)O(TR) records for northwestern Canada reflect the same significant large-scale climate patterns as precipitation δ(18)O for spring–summer, and therefore have potential for reconstructing past atmospheric dynamics in addition to temperature variability in the region.

paleoclimate↗