Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions

Wetland methane (CH4) emissions (FCH4) are important in global carbon budgets and climate change assessments. Currently, FCH4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent FCH4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that FCH4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between FCH4 and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between FCH4 and temperature, suggesting larger FCH4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments. Wetland methane emissions contribute to global warming, and are oversimplified in climate models. Here the authors use eddy covariance measurements from 48 global sites to demonstrate seasonal hysteresis in methane-temperature relationships and suggest the importance of microbial processes.Peer reviewe

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe

An Ecosystem-Scale Flux Measurement Strategy to Assess Natural Climate Solutions.

Eddy covariance measurement systems provide direct observation of the exchange of greenhouse gases between ecosystems and the atmosphere, but have only occasionally been intentionally applied to quantify the carbon dynamics associated with specific climate mitigation strategies. Natural climate solutions (NCS) harness the photosynthetic power of ecosystems to avoid emissions and remove atmospheric carbon dioxide (CO2), sequestering it in biological carbon pools. In this perspective, we aim to determine which kinds of NCS strategies are most suitable for ecosystem-scale flux measurements and how these measurements should be deployed for diverse NCS scales and goals. We find that ecosystem-scale flux measurements bring unique value when assessing NCS strategies characterized by inaccessible and hard-to-observe carbon pool changes, important non-CO2 greenhouse gas fluxes, the potential for biophysical impacts, or dynamic successional changes. We propose three deployment types for ecosystem-scale flux measurements at various NCS scales to constrain wide uncertainties and chart a workable path forward: "pilot", "upscale", and "monitor". Together, the integration of ecosystem-scale flux measurements by the NCS community and the prioritization of NCS measurements by the flux community, have the potential to improve accounting in ways that capture the net impacts, unintended feedbacks, and on-the-ground specifics of a wide range of emerging NCS strategies

Climate‐Driven Limits to Future Carbon Storage in California's Wildland Ecosystems

Enhanced ecosystem carbon storage is a key component of many climate mitigation pathways. The State of California has set an ambitious goal of carbon neutrality by 2045, relying in part on enhanced carbon sequestration in natural and working lands. We used statistical modeling, including random forests and climate analogues, to explore the climate-driven challenges and uncertainties associated with the goal of long-term carbon sequestration in forests and shrublands. We found that seasonal patterns of temperature and precipitation are strong controllers of the spatial distribution of aboveground live carbon. RCP8.5 projections of temperature and precipitation were estimated to drive decreases of 16.1 ± 7.5% in aboveground live carbon by the end of the century, with coastal areas of central and northern California and low/mid-elevation mountain areas being most vulnerable. With RCP4.5 projections, declines were less severe, with 8.8 ± 5.3% carbon loss. In either scenario, the increased temperature systematically caused biomass declines, and the spread of projected precipitation across 32 CMIP5 models introduced substantial uncertainty in the magnitude of that decline. Projected changes in the environmental niche for the 20 most biomass-dominant tree species revealed widespread replacement of conifers by oak species in low elevations of central and northern California, with corresponding decline in carbon storage depending on expected migration rates. The spatial patterns of vulnerability we identify may allow policymakers to assess where carbon sequestration in aboveground biomass is an appropriate part of a climate mitigation portfolio, and where future climate-driven carbon losses may be a liability.Data and code to accompany "Climate-driven limits to future carbon storage in California's wildland ecosystems" AGU Advances, 2021 Corresponding author: Shane Coffield [email protected] This archive contains input data, model output, Python scripts, and Google Earth Engine (GEE) scripts. Python and GEE scripts can also be accessed directly via https://github.com/scoffiel/carbon_projections https://code.earthengine.google.com/?accept_repo=users/scoffiel/carbon_projections git clone https://earthengine.googlesource.com/users/scoffiel/carbon_projections Data overview: input_data: contains all processed data needed to run models in Python. All were derived from public sources. Processed raster layers are 1/8-degree resolution in EPSG:4326 projection. Climate – Bias-Crrected Spatial Downscaled (BCSD) CMIP5 data for 2006-2099 which has been compiled into 6 different netcdf files in Python scripts #1-2. For RCP4.5 and RCP8.5 scenarios, we generated a "climate_present" file for 2006-2099 average and "climate_future" file for 2090-2099 average with dimensions for 32 mdels (in order of most drying to wetting), 8 variables (4 seasons of T & P), and lat/lon. An additional "climate_present_10yrs" file maintains all 10 years f data needed for calculating interannual variability in the climate analogs approach (script #9). These climate data are the driver variables for all models. Units: °C for mean daily temperature and mm/day for precipitation Web source: https://gdo-dcp.ucllnl.org/downscaled_cmip_projections/dcpInterface.html Citation: Brekke et al., 2013; Maurer et al., 2007 carbn_eighth.tif – abveground live wildland carbon for California for 2014. Rescaled from raw 30m data obtained from the California Air Resources Board (30m data available upon request from CARB). This is generated by GEE script #1 and is the target dataset for training RF regression models of carbon density in Python script #5. Units: ton C/ha Web source: https://ww2.arb.ca.gov/nwl-inventory Citation: CARB 2018, Gonzalez et al., 2015 landcver_eighth.tif – dminant land cover class generated from 30m National Land Cover Database (NLCD) for 2016 in GEE script #3. This is the target dataset for training RF classification models of vegetation type in Python script #8 Units: 1 for shrub/grass, 2 for forest Web source: https://www.mrlc.gov/data/nlcd-2016-land-cover-conus Citation: Homer et al., 2020 valid_fractin.tif – fractin of each 1/8-degree pixel which is comprised of herbaceous, shrub, or forest landcover, also derived from the NLCD dataset. Generated in GEE script #2. landcver_mask_eighth.tif – Mask layer with "1" fr all areas of the Western US that are at least 50% wildland cover, also derived from the NLCD dataset. Generated in GEE script #5 elev_eighth.tif – Derived frm 30m USGS elevation data in GEE script #3 Units: m Web source: https://lpdaac.usgs.gov/products/srtmgl1v003/ Citation: NASA JPL 2013 ffsets.zip – shapefiles f 32 forest carbon offset project polygons in California, collected from https://webmaps.arb.ca.gv/ARBOCIssuanceMap/ . Used in Pythn script #7 to assess vulnerability of these areas. ecregion_carbon_densities.tiff – frest carbon density averaged by EPA Level III ecoregions using CARB AGL carbon layer; generated in GEE script #4 and used in Python script #8 to estimate carbon change associated with vegetation type conversions. Units: ton C/ha cci_eighth.tif – abveground live carbon density for the western US and Mexico for 2017, derived from the ESA Climate Change Initiative global biomass dataset. Generated in GEE script #6 and used for climate analogs approach in Python script #9. Units: ton biomass/ha (converted to carbon in Python) Web source: https://catalogue.ceda.ac.uk/uuid/bedc59f37c9545c981a839eb552e4084 Citation: Santoro & Cartus, 2019 lemma_39spp_eighth.tif – abveground live carbon density for 39 species in California, compiled from 30m data from Oregon State for 2012 via GEE script #7 and used as target variables in species niche models (Python script #11). Each band is one species, ordered by most total biomass to least. Units: ton biomass/ha (converted to carbon in Python) Web source: https://lemma.forestry.oregonstate.edu/data Citation: Kennedy et al., 2018 model_output: contains subfolders corresponding to the four approaches discussed in the manuscript. For all approaches, we provide projections of carbon change (ton C/ha) for 6 scenarios: RCP4.5 & RCP8.5 x dry/mean/wet. Randm forest regression of carbon density Randm forest classification of dominant vegetation type Climate analgs Randm forest regression of 20 individual species' carbon density Google Earth Engine code overview Carbon_data: rescales 30m CARB carbon data layer (available upon request from CARB) to 1/8-degree to match the BCSD climate dataset, including masking out water/ag/urban landcover Valid_land_fraction: calculates the fraction of sub-gridcell area that is allowed to support aboveground carbon (excludes water/ag/urban/barren cover) Elevation: rescales 30m USGS elevation data to 1/8-degree to match the BCSD climate dataset Landcover: rescales 30m NLCD land cover data to 1/8 degree (forest, shrub/grass, null) Landcover_mask: creates a 1/8-degree layer masking out any areas of the western US that are not 50% wildland cover (for climate analogs analysis) Cci_biomass: rescales 100m CCI biomass data to 1/8-degree for US and Mexico Lemma_spp: reformats LEMMA species-level data into one raster layer with one band for each species' density at 1/8 degree Python code overview Process_climate.py: processes raw BCSD monthly climate data into combined netcdf files Process_climate_10yrs.py: duplicate of script 1 to process raw BCSD climate data, but modified slightly to maintain all 10 years of data in the present. This is needed for calculating the interannual variability in the climate analogs approach. Plot_clim_change.py: generates maps of mean annual T & P change for RCP4.5 & RCP8.5 (Fig 1) Plot_clim_spread.py: generates maps of spread of precipitation across 32 models for RCP8.5 (FigS1) and dry vs. wet models averages for RCP8.5 (FigS2) RF_carbon_model.py: approach #1. Models present-day distribution of CARB carbon layer based on climate data. Project future carbon and change RF_model_spread.py: rebuilds RF regression models from script 5, for each of the 32 climate models. Compares 3 different runs: T & P both change, T only (P constant), and P only (T constant). Offsets_change.py: compares RF regression model results from script 5 for all forests vs. carbon offset projects RF_veg_class_model.py: approach #2. Models present-day distribution of NLCD forest-vs-shrub layer based on climate data. Projects future land cover, change, and associated carbon change Climate_analogs.py. approach #3. Matches future climate pixels with their present analogue using Mahalanobis distance. Projects future carbon density by assigning that of the present analog. 3 runs: full domain (25-49 lat and -125 - -100 lon), 500 km radius, 100 km radius Analog_whittaker_plots.py: approach #3 supplementary figure - Whittaker scatter plots of mean annual P vs. T, showing how CA's gridcells shift Species_models.py: approach #4. Fits RF regression models to each of the top 20 tree spp in California. Project future carbon and change. Applies restrictions on distance between spp present and future locations (migration scenarios)

Carbon Flux Trajectories and Site Conditions from Restored Impounded Marshes in the Sacramento‐San Joaquin Delta

Wetlands can sequester carbon over the long term, providing natural climate solutions. This requires that existing carbon stocks be maintained and additional greenhouse gas (GHG) emissions are outweighed by carbon sequestration. We analyzed 25 site-years of continuous CO 2 and methane fluxes by eddy covariance from 4 restored wetlands to detect long-term trends and estimate their GHG budgets. Sites showed large interannual variability with no clear trends in CO 2 fluxes beyond the initial uptake phase during vegetation establishment following restoration. Methane emissions followed either decreasing or increasing trends likely because of variable site conditions, such as water level fluctuations and soil mineral concentrations. Several site-years were GHG neutral or sinks depending on the global warming potential used. Carbon sink sites were projected to offset the radiative forcing from methane emissions after 62 to 202 years. We compared and contrasted restoration design and management strategies based on this and previous studies at these sites to balance the climate mitigation potentials with other beneficial wetland services. We conclude that these restored wetlands indeed provide climate mitigation benefits provided they are appropriately maintained over the long term (100 years), as poor management can cause large carbon losses even decades after restoration

The magnitude and pace of photosynthetic recovery after wildfire in California ecosystems

Wildfire modifies the short- and long-term exchange of carbon between terrestrial ecosystems and the atmosphere, with impacts on ecosystem services such as carbon uptake. Dry western US forests historically experienced low-intensity, frequent fires, with patches across the landscape occupying different points in the fire-recovery trajectory. Contemporary perturbations, such as recent severe fires in California, could shift the historic stand-age distribution and impact the legacy of carbon uptake on the landscape. Here, we combine flux measurements of gross primary production (GPP) and chronosequence analysis using satellite remote sensing to investigate how the last century of fires in California impacted the dynamics of ecosystem carbon uptake on the fire-affected landscape. A GPP recovery trajectory curve of more than five thousand fires in forest ecosystems since 1919 indicated that fire reduced GPP by [Formula: see text] g C m[Formula: see text] y[Formula: see text]([Formula: see text]) in the first year after fire, with average recovery to prefire conditions after [Formula: see text] y. The largest fires in forested ecosystems reduced GPP by [Formula: see text] g C m[Formula: see text] y[Formula: see text] (n = 401) and took more than two decades to recover. Recent increases in fire severity and recovery time have led to nearly [Formula: see text] MMT CO[Formula: see text] (3-y rolling mean) in cumulative forgone carbon uptake due to the legacy of fires on the landscape, complicating the challenge of maintaining California's natural and working lands as a net carbon sink. Understanding these changes is paramount to weighing the costs and benefits associated with fuels management and ecosystem management for climate change mitigation

Impact of Insolation Data Source on Remote Sensing Retrievals of Evapotranspiration over the California Delta

The energy delivered to the land surface via insolation is a primary driver of evapotranspiration (ET)—the exchange of water vapor between the land and atmosphere. Spatially distributed ET products are in great demand in the water resource management community for real-time operations and sustainable water use planning. The accuracy and deliverability of these products are determined in part by the characteristics and quality of the insolation data sources used as input to the ET models. This paper investigates the practical utility of three different insolation datasets within the context of a satellite-based remote sensing framework for mapping ET at high spatiotemporal resolution, in an application over the Sacramento⁻San Joaquin Delta region in California. The datasets tested included one reanalysis product: The Climate System Forecast Reanalysis (CFSR) at 0.25° spatial resolution, and two remote sensing insolation products generated with geostationary satellite imagery: a product for the continental United States at 0.2°, developed by the University of Wisconsin Space Sciences and Engineering Center (SSEC) and a coarser resolution (1°) global Clouds and the Earth’s Radiant Energy System (CERES) product. The three insolation data sources were compared to pyranometer data collected at flux towers within the Delta region to establish relative accuracy. The satellite products significantly outperformed CFSR, with root-mean square errors (RMSE) of 2.7, 1.5, and 1.4 MJ·m−2·d−1 for CFSR, CERES, and SSEC, respectively, at daily timesteps. The satellite-based products provided more accurate estimates of cloud occurrence and radiation transmission, while the reanalysis tended to underestimate solar radiation under cloudy-sky conditions. However, this difference in insolation performance did not translate into comparable improvement in the ET retrieval accuracy, where the RMSE in daily ET was 0.98 and 0.94 mm d−1 using the CFSR and SSEC insolation data sources, respectively, for all the flux sites combined. The lack of a notable impact on the aggregate ET performance may be due in part to the predominantly clear-sky conditions prevalent in central California, under which the reanalysis and satellite-based insolation data sources have comparable accuracy. While satellite-based insolation data could improve ET retrieval in more humid regions with greater cloud-cover frequency, over the California Delta and climatologically similar regions in the western U.S., the CFSR data may suffice for real-time ET modeling efforts

Fire effects on the persistence of soil organic matter and long-term carbon storage

One paradigm in biogeochemistry is that frequent disturbance tends to deplete carbon (C) in soil organic matter (SOM) by reducing biomass inputs and promoting losses. However, disturbance by fire has challenged this paradigm because soil C responses to frequent and/or intense fires are highly variable, despite observed declines in biomass inputs. Here, we review recent advances to illustrate that fire-driven changes in decomposition, mediated by altered SOM stability, are an important compensatory process offsetting declines in aboveground biomass pools. Fire alters the stability of SOM by affecting both the physicochemical properties of the SOM and the environmental drivers of decomposition, potentially offsetting C lost via combustion, but the mechanisms affecting the SOM stability differ across ecosystems. Thus, shifting our focus from a top-down view of fire impacting C cycling via changes in plant biomass to a bottom-up view of changes in decomposition may help to elucidate counterintuitive trends in the response of SOM to burning. Given that 70% of global topsoil C is in fire-prone regions, using fire to promote SOM stability may be an important nature-based climate solution to increase C storage