262 results on '"Randerson, James P."'
Search Results
2. We need a solid scientific basis for nature-based climate solutions in the United States
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Novick, Kimberly A, Keenan, Trevor F, Anderegg, William RL, Normile, Caroline P, Runkle, Benjamin RK, Oldfield, Emily E, Shrestha, Gyami, Baldocchi, Dennis D, Evans, Margaret EK, Randerson, James T, Sanderman, Jonathan, Torn, Margaret S, Trugman, Anna T, and Williams, Christopher A
- Published
- 2024
3. Global‐Scale Convergence Obscures Inconsistencies in Soil Carbon Change Predicted by Earth System Models
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Shi, Zheng, Hoffman, Forrest M, Xu, Min, Mishra, Umakant, Allison, Steven D, Zhou, Jizhong, and Randerson, James T
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Earth Sciences ,Physical Geography and Environmental Geoscience ,Climate Change Science ,Geology ,Climate Action ,Earth system model ,climate change ,global warming ,elevated CO2 ,Climate change science ,Physical geography and environmental geoscience - Abstract
Soil carbon (C) responses to environmental change represent a major source of uncertainty in the global C cycle. Feedbacks between soil C stocks and climate drivers could impact atmospheric CO2 levels, further altering the climate. Here, we assessed the reliability of Earth system model (ESM) predictions of soil C change using the Coupled Model Intercomparison Project phases 5 and 6 (CMIP5 and CMIP6). ESMs predicted global soil C gains under the high emission scenario, with soils taking up 43.9 Pg (95% CI: 9.2–78.5 Pg) C on average during the 21st century. The variation in global soil C change declined significantly from CMIP5 (with average of 48.4 Pg [95% CI: 2.0–94.9 Pg] C) to CMIP6 models (with average of 39.3 Pg [95% CI: 23.9–54.7 Pg] C). For some models, a small C increase in all biomes contributed to this convergence. For other models, offsetting responses between cold and warm biomes contributed to convergence. Although soil C predictions appeared to converge in CMIP6, the dominant processes driving soil C change at global or biome scales differed among models and in many cases between earlier and later versions of the same model. Random Forest models, for soil carbon dynamics, accounted for more than 63% variation of the global soil C change predicted by CMIP5 ESMs, but only 36% for CMIP6 models. Although most CMIP6 models apparently agree on increased soil C storage during the 21st century, this consensus obscures substantial model disagreement on the mechanisms underlying soil C response, calling into question the reliability of model predictions.
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- 2024
4. Evaluating the performance of WRF in simulating winds and surface meteorology during a Southern California wildfire event
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Kumar, Mukesh, Kosović, Branko, Nayak, Hara P, Porter, William C, Randerson, James T, and Banerjee, Tirtha
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Earth Sciences ,Physical Geography and Environmental Geoscience ,Atmospheric Sciences ,fire-weather ,grid resolution ,nesting ,PBL scheme ,wildfires ,WRF ,Geology ,Geophysics ,Physical geography and environmental geoscience - Abstract
The intensity and frequency of wildfires in California (CA) have increased in recent years, causing significant damage to human health and property. In October 2007, a number of small fire events, collectively referred to as the Witch Creek Fire or Witch Fire started in Southern CA and intensified under strong Santa Ana winds. As a test of current mesoscale modeling capabilities, we use the Weather Research and Forecasting (WRF) model to simulate the 2007 wildfire event in terms of meteorological conditions. The main objectives of the present study are to investigate the impact of horizontal grid resolution and planetary boundary layer (PBL) scheme on the model simulation of meteorological conditions associated with a Mega fire. We evaluate the predictive capability of the WRF model to simulate key meteorological and fire-weather forecast parameters such as wind, moisture, and temperature. Results of this study suggest that more accurate predictions of temperature and wind speed relevant for better prediction of wildfire spread can be achieved by downscaling regional numerical weather prediction products to 1 km resolution. Furthermore, accurate prediction of near-surface conditions depends on the choice of the planetary boundary layer parameterization. The MYNN parameterization yields more accurate prediction as compared to the YSU parameterization. WRF simulations at 1 km resolution result in better predictions of temperature and wind speed than relative humidity during the 2007 Witch Fire. In summary, the MYNN PBL parameterization scheme with finer grid resolution simulations improves the prediction of near-surface meteorological conditions during a wildfire event.
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- 2024
5. The time since land-use transition drives changes in fire activity in the Amazon-Cerrado region
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Ribeiro, Andreia F. S., Santos, Lucas, Randerson, James T., Uribe, Maria R., Alencar, Ane A. C., Macedo, Marcia N., Morton, Douglas C., Zscheischler, Jakob, Silvestrini, Rafaella A., Rattis, Ludmila, Seneviratne, Sonia I., and Brando, Paulo M.
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- 2024
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6. Coccidioidomycosis (Valley Fever) Case Data for the Southwestern United States
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Gorris, Morgan E, Cat, Linh Anh, Matlock, Melissa, Ogunseitan, Oladele A, Treseder, Kathleen K, Randerson, James T, and Zender, Charles S
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Good Health and Well Being ,Affordable and Clean Energy - Published
- 2023
7. Assessing the Influence of Climate on the Spatial Pattern of West Nile Virus Incidence in the United States.
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Gorris, Morgan E, Randerson, James T, Coffield, Shane R, Treseder, Kathleen K, Zender, Charles S, Xu, Chonggang, and Manore, Carrie A
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Animals ,Humans ,West Nile virus ,West Nile Fever ,Incidence ,Canada ,United States ,Cold Temperature ,Biodefense ,West Nile Virus ,Rare Diseases ,Emerging Infectious Diseases ,Vaccine Related ,Vector-Borne Diseases ,Infectious Diseases ,Prevention ,Climate Action ,Environmental Sciences ,Medical and Health Sciences ,Toxicology - Abstract
BackgroundWest Nile virus (WNV) is the leading cause of mosquito-borne disease in humans in the United States. Since the introduction of the disease in 1999, incidence levels have stabilized in many regions, allowing for analysis of climate conditions that shape the spatial structure of disease incidence.ObjectivesOur goal was to identify the seasonal climate variables that influence the spatial extent and magnitude of WNV incidence in humans.MethodsWe developed a predictive model of contemporary mean annual WNV incidence using U.S. county-level case reports from 2005 to 2019 and seasonally averaged climate variables. We used a random forest model that had an out-of-sample model performance of R2=0.61.ResultsOur model accurately captured the V-shaped area of higher WNV incidence that extends from states on the Canadian border south through the middle of the Great Plains. It also captured a region of moderate WNV incidence in the southern Mississippi Valley. The highest levels of WNV incidence were in regions with dry and cold winters and wet and mild summers. The random forest model classified counties with average winter precipitation levels
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- 2023
8. Climate-driven changes in the predictability of seasonal precipitation
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Le, Phong VV, Randerson, James T, Willett, Rebecca, Wright, Stephen, Smyth, Padhraic, Guilloteau, Clément, Mamalakis, Antonios, and Foufoula-Georgiou, Efi
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Earth Sciences ,Oceanography ,Climate Action - Abstract
Climate-driven changes in precipitation amounts and their seasonal variability are expected in many continental-scale regions during the remainder of the 21st century. However, much less is known about future changes in the predictability of seasonal precipitation, an important earth system property relevant for climate adaptation. Here, on the basis of CMIP6 models that capture the present-day teleconnections between seasonal precipitation and previous-season sea surface temperature (SST), we show that climate change is expected to alter the SST-precipitation relationships and thus our ability to predict seasonal precipitation by 2100. Specifically, in the tropics, seasonal precipitation predictability from SSTs is projected to increase throughout the year, except the northern Amazonia during boreal winter. Concurrently, in the extra-tropics predictability is likely to increase in central Asia during boreal spring and winter. The altered predictability, together with enhanced interannual variability of seasonal precipitation, poses new opportunities and challenges for regional water management.
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- 2023
9. AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics
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Li, Fa, Zhu, Qing, Riley, William J, Zhao, Lei, Xu, Li, Yuan, Kunxiaojia, Chen, Min, Wu, Huayi, Gui, Zhipeng, Gong, Jianya, and Randerson, James T
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Earth Sciences ,Climate Action ,Earth sciences - Abstract
African and South American (ASA) wildfires account for more than 70g% of global burned areas and have strong connection to local climate for sub-seasonal to seasonal wildfire dynamics. However, representation of the wildfire-climate relationship remains challenging due to spatiotemporally heterogenous responses of wildfires to climate variability and human influences. Here, we developed an interpretable machine learning (ML) fire model (AttentionFire_v1.0) to resolve the complex controls of climate and human activities on burned areas and to better predict burned areas over ASA regions. Our ML fire model substantially improved predictability of burned areas for both spatial and temporal dynamics compared with five commonly used machine learning models. More importantly, the model revealed strong time-lagged control from climate wetness on the burned areas. The model also predicted that, under a high-emission future climate scenario, the recently observed declines in burned area will reverse in South America in the near future due to climate changes. Our study provides a reliable and interpretable fire model and highlights the importance of lagged wildfire-climate relationships in historical and future predictions.
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- 2023
10. Using remote sensing to quantify the additional climate benefits of California forest carbon offset projects.
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Coffield, Shane R, Vo, Cassandra D, Wang, Jonathan A, Badgley, Grayson, Goulden, Michael L, Cullenward, Danny, Anderegg, William RL, and Randerson, James T
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Carbon ,Conservation of Natural Resources ,Climate ,Forestry ,California ,Climate Change ,Remote Sensing Technology ,Forests ,additionality ,carbon offsets ,improved forest management ,nature-based climate solutions ,remote sensing ,Clinical Research ,Climate Action ,Environmental Sciences ,Biological Sciences ,Ecology - Abstract
Nature-based climate solutions are a vital component of many climate mitigation strategies, including California's, which aims to achieve carbon neutrality by 2045. Most carbon offsets in California's cap-and-trade program come from improved forest management (IFM) projects. Since 2012, various landowners have set up IFM projects following the California Air Resources Board's IFM protocol. As many of these projects approach their 10th year, we now have the opportunity to assess their effectiveness, identify best practices, and suggest improvements toward future protocol revisions. In this study, we used remote sensing-based datasets to evaluate the carbon trends and harvest histories of 37 IFM projects in California. Despite some current limitations and biases, these datasets can be used to quantify carbon accumulation and harvest rates in offset project lands relative to nearby similar "control" lands before and after the projects began. Five lines of evidence suggest that the carbon accumulated in offset projects to date has generally not been additional to what might have otherwise occurred: (1) most forests in northwestern California have been accumulating carbon since at least the mid-1980s and continue to accumulate carbon, whether enrolled in offset projects or not; (2) harvest rates were high in large timber company project lands before IFM initiation, suggesting they are earning carbon credits for forests in recovery; (3) projects are often located on lands with higher densities of low-timber-value species; (4) carbon accumulation rates have not yet increased on lands that enroll as offset projects, relative to their pre-enrollment levels; and (5) harvest rates have not decreased on most project lands since offset project initiation. These patterns suggest that the current protocol should be improved to robustly measure and reward additionality. In general, our framework of geospatial analyses offers an important and independent means to evaluate the effectiveness of the carbon offsets program, especially as these data products continue improving and as offsets receive attention as a climate mitigation strategy.
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- 2022
11. Evaluation of CMIP models with IOMB: Rates of contemporary ocean carbon uptake linked with vertical temperature gradients and transport to the ocean interior
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Fu, Weiwei, Moore, J. Keith, Primeau, Francois, Collier, Nathan, Ogunro, Oluwaseun O., Hoffman, Forrest M., and Randerson, James T.
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Physics - Atmospheric and Oceanic Physics ,Physics - Biological Physics - Abstract
The International Ocean Model Benchmarking (IOMB) software package is a new community resource used here to evaluate surface and upper ocean variables from CMIP5 and CMIP6 Earth System Models (ESMs) Our analysis reveals general improvement in the multi-model mean of CMIP6 compared to CMIP5 for most of the variables we examined including surface nutrients, temperature, and salinity. We find that both CMIP5 and CMIP6 ocean models underestimate anthropogenic carbon dioxide uptake after the 1970s. For the period of 1994 to 2007, the multi-model mean from CMIP6 yields a mean cumulative carbon uptake of 27.2 +-2.2 Pg C, which is about 15% lower than the 32.0+-5.7 Pg C estimate derived from two sets of observations. Negative biases in the change in anthropogenic carbon inventory exist in the northern North Atlantic and at mid-latitudes in the southern hemisphere (30-60{\deg}S). For the few models that provided simulations of chlorofluorocarbon (CFC), we demonstrate that regions with negative anthropogenic DIC biases coincide with regions that have a negative bias in CFC concentrations. This relationship suggests that underestimates of anthropogenic carbon storage in some models originates, in part, from weak transport between the surface and interior ocean. To examine the robustness of this attribution across the full suite of CMIP5 and CMIP6 models, we examined the vertical temperature gradient between 200 and 1000m as a metric for stratification and exchange between the surface and deeper waters. On a global scale across different models and different MIPs we find a linear relationship between the bias of vertical temperature gradients and the bias in anthropogenic carbon uptake, consistent with the hypothesis that model biases in the ocean carbon sink are related to biases in surface-to-interior transport.
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- 2022
12. Wildfire exacerbates high-latitude soil carbon losses from climate warming
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Mekonnen, Zelalem A, Riley, William J, Randerson, James T, Shirley, Ian A, Bouskill, Nicholas J, and Grant, Robert F
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Agricultural ,Veterinary and Food Sciences ,Biological Sciences ,Forestry Sciences ,Climate Action ,soil carbon dynamics ,high-latitude carbon cycle ,wildfire and climate warming ,nutrient cycling ,vegetation change ,CESD-Soil Microbes and Ecosystem Function ,Meteorology & Atmospheric Sciences - Abstract
Arctic and boreal permafrost soil organic carbon (SOC) decomposition has been slower than carbon inputs from plant growth since the last glaciation. Anthropogenic climate warming has threatened this historical trend by accelerating SOC decomposition and altering wildfire regimes. We accurately modeled observed plant biomass and carbon emissions from wildfires in Alaskan ecosystems under current climate conditions. In projections to 2300 under the RCP8.5 climate scenario, we found that warming and increased atmospheric CO2 will result in plant biomass gains and higher litterfall. However, increased carbon losses from (a) wildfire combustion and (b) rapid SOC decomposition driven by increased deciduous litter production, root exudation, and active layer depth will lead to about 4.4 PgC of soil carbon losses from Alaska by 2300 and most (88%) of these loses will be from the top 1 m of soil. These SOC losses offset plant carbon gains, causing the ecosystem to transition to a net carbon source after 2200. Simulations excluding wildfire increases yielded about a factor of four lower SOC losses by 2300. Our results show that projected wildfire and its direct and indirect effects on plant and soil carbon may accelerate high-latitude soil carbon losses, resulting in a positive feedback to climate change.
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- 2022
13. Future increases in Amazonia water stress from CO2 physiology and deforestation
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Li, Yue, Baker, Jessica C. A., Brando, Paulo M., Hoffman, Forrest M., Lawrence, David M., Morton, Douglas C., Swann, Abigail L. S., Uribe, Maria del Rosario, and Randerson, James T.
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- 2023
- Full Text
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14. Tracking and classifying Amazon fire events in near real time
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Andela, Niels, Morton, Douglas C, Schroeder, Wilfrid, Chen, Yang, Brando, Paulo M, and Randerson, James T
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Life on Land - Abstract
Exceptional fire activity in 2019 sparked concern about Amazon forest conservation. However, the inability to rapidly separate satellite fire detections by fire type hampered fire suppression and assessment of ecosystem and air quality impacts. Here, we describe the development of a near-real-time approach for tracking contributions from deforestation, forest, agricultural, and savanna fires to burned area and emissions and apply the approach to the 2019 fire season in South America. Across the southern Amazon, 19,700 deforestation fire events accounted for 39% of all satellite active fire detections and the majority of fire carbon emissions (63%; 69 Tg C). Multiday fires accounted for 81% of burned area and 92% of carbon emissions from the Amazon, with many forest fires burning uncontrolled for weeks. Most fire detections from deforestation fires were correctly identified within 2 days (67%), highlighting the potential to improve situational awareness and management outcomes during fire emergencies.
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- 2022
15. Human-ignited fires result in more extreme fire behavior and ecosystem impacts.
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Hantson, Stijn, Andela, Niels, Goulden, Michael L, and Randerson, James T
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Humans ,Trees ,Fires ,Ecosystem ,Forests ,Wildfires ,Climate Action - Abstract
California has experienced a rapid increase in burned area over the past several decades. Although fire behavior is known to be closely tied to ecosystem impacts, most analysis of changing fire regimes has focused solely on area burned. Here we present a standardized database of wildfire behavior, including daily fire rate-of-spread and fire radiative power for large, multiday wildfires in California during 2012-2018 using remotely-sensed active fire observations. We observe that human-ignited fires start at locations with lower tree cover and during periods with more extreme fire weather. These characteristics contribute to more explosive growth in the first few days following ignition for human-caused fires as compared to lightning-caused fires. The faster fire spread, in turn, yields a larger ecosystem impact, with tree mortality more than three times higher for fast-moving fires (>1 km day-1) than for slow moving fires (
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- 2022
16. Hotspots of Predictability: Identifying Regions of High Precipitation Predictability at Seasonal Timescales From Limited Time Series Observations
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Mamalakis, Antonios, AghaKouchak, Amir, Randerson, James T, and Foufoula‐Georgiou, Efi
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Hydrology ,Atmospheric Sciences ,Earth Sciences ,predictability ,seasonal precipitation ,extremes ,drought ,copula models ,sea surface temperatures ,El Nino-southern oscillation ,ocean indices ,El Niño‐southern oscillation ,Physical Geography and Environmental Geoscience ,Civil Engineering ,Environmental Engineering ,Civil engineering ,Environmental engineering - Abstract
Precipitation prediction at seasonal timescales is important for planning and management of water resources as well as preparedness for hazards such as floods, droughts and wildfires. Quantifying predictability is quite challenging as a consequence of a large number of potential drivers, varying antecedent conditions, and small sample size of high-quality observations available at seasonal timescales, that in turn, increases prediction uncertainty and the risk of model overfitting. Here, we introduce a generalized probabilistic framework to account for these issues and assess predictability under uncertainty. We focus on prediction of winter (Nov-Mar) precipitation across the contiguous United States, using sea surface temperature-derived indices (averaged in Aug-Oct) as predictors. In our analysis we identify "predictability hotspots," which we define as regions where precipitation is inherently more predictable. Our framework estimates the entire predictive distribution of precipitation using copulas and quantifies prediction uncertainties, while employing principal component analysis for dimensionality reduction and a cross validation technique to avoid overfitting. We also evaluate how predictability changes across different quantiles of the precipitation distribution (dry, normal, wet amounts) using a multi-category 3 × 3 contingency table. Our results indicate that well-defined predictability hotspots occur in the Southwest and Southeast. Moreover, extreme dry and wet conditions are shown to be relatively more predictable compared to normal conditions. Our study may help with water resources management in several subregions of the United States and can be used to assess the fidelity of earth system models in successfully representing teleconnections and predictability.
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- 2022
17. Author Correction: Climate-driven changes in the predictability of seasonal precipitation
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Le, Phong V. V., Randerson, James T., Willett, Rebecca, Wright, Stephen, Smyth, Padhraic, Guilloteau, Clément, Mamalakis, Antonios, and Foufoula-Georgiou, Efi
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- 2023
- Full Text
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18. Escalating carbon emissions from North American boreal forest wildfires and the climate mitigation potential of fire management
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Phillips, Carly A, Rogers, Brendan M, Elder, Molly, Cooperdock, Sol, Moubarak, Michael, Randerson, James T, and Frumhoff, Peter C
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Climate Action - Abstract
Wildfires in boreal forests release large quantities of greenhouse gases to the atmosphere, exacerbating climate change. Here, we characterize the magnitude of recent and projected gross and net boreal North American wildfire carbon dioxide emissions, evaluate fire management as an emissions reduction strategy, and quantify the associated costs. Our results show that wildfires in boreal North America could, by mid-century, contribute to a cumulative net source of nearly 12 gigatonnes of carbon dioxide, about 3% of remaining global carbon dioxide emissions associated with keeping temperatures within the Paris Agreement's 1.5°C limit. With observations from Alaska, we show that current fire management practices limit the burned area. Further, the costs of avoiding carbon dioxide emissions by means of increasing investment in fire management are comparable to or lower than those of other mitigation strategies. Together, our findings highlight the climate risk that boreal wildfires pose and point to fire management as a cost-effective way to limit emissions.
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- 2022
19. Deforestation-induced climate change reduces carbon storage in remaining tropical forests.
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Li, Yue, Brando, Paulo M, Morton, Douglas C, Lawrence, David M, Yang, Hui, and Randerson, James T
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Trees ,Carbon ,Conservation of Natural Resources ,Biomass ,Tropical Climate ,Climate Change ,Forests ,Life on Land ,Climate Action - Abstract
Biophysical effects from deforestation have the potential to amplify carbon losses but are often neglected in carbon accounting systems. Here we use both Earth system model simulations and satellite-derived estimates of aboveground biomass to assess losses of vegetation carbon caused by the influence of tropical deforestation on regional climate across different continents. In the Amazon, warming and drying arising from deforestation result in an additional 5.1 ± 3.7% loss of aboveground biomass. Biophysical effects also amplify carbon losses in the Congo (3.8 ± 2.5%) but do not lead to significant additional carbon losses in tropical Asia due to its high levels of annual mean precipitation. These findings indicate that tropical forests may be undervalued in carbon accounting systems that neglect climate feedbacks from surface biophysical changes and that the positive carbon-climate feedback from deforestation-driven climate change is higher than the feedback originating from fossil fuel emissions.
- Published
- 2022
20. Uncertainty in US forest carbon storage potential due to climate risks
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Wu, Chao, Coffield, Shane R., Goulden, Michael L., Randerson, James T., Trugman, Anna T., and Anderegg, William R. L.
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- 2023
- Full Text
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21. Building a machine learning surrogate model for wildfire activities within a global Earth system model
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Zhu, Qing, Li, Fa, Riley, William J, Xu, Li, Zhao, Lei, Yuan, Kunxiaojia, Wu, Huayi, Gong, Jianya, and Randerson, James
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Earth Sciences ,Atmospheric Sciences ,Climate Action ,CESD-Wildfire ,Earth sciences - Abstract
Wildfire is an important ecosystem process, influencing land biogeophysical and biogeochemical dynamics and atmospheric composition. Fire-driven loss of vegetation cover, for example, directly modifies the surface energy budget as a consequence of changing albedo, surface roughness, and partitioning of sensible and latent heat fluxes. Carbon dioxide and methane emitted by fires contribute to a positive atmospheric forcing, whereas emissions of carbonaceous aerosols may contribute to surface cooling. Process-based modeling of wildfires in Earth system land models is challenging due to limited understanding of human, climate, and ecosystem controls on fire counts, fire size, and burned area. Integration of mechanistic wildfire models within Earth system models requires careful parameter calibration, which is computationally expensive and subject to equifinality. To explore alternative approaches, we present a deep neural network (DNN) scheme that surrogates the process-based wildfire model with the Energy Exascale Earth System Model (E3SM) interface. The DNN wildfire model accurately simulates observed burned area with over 90g% higher accuracy with a large reduction in parameterization time compared with the current process-based wildfire model. The surrogate wildfire model successfully captured the observed monthly regional burned area during validation period 2011 to 2015 (coefficient of determination, R2Combining double low line0.93). Since the DNN wildfire model has the same input and output requirements as the E3SM process-based wildfire model, our results demonstrate the applicability of machine learning for high accuracy and efficient large-scale land model development and predictions.
- Published
- 2022
22. California wildfire spread derived using VIIRS satellite observations and an object-based tracking system
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Chen, Yang, Hantson, Stijn, Andela, Niels, Coffield, Shane R, Graff, Casey A, Morton, Douglas C, Ott, Lesley E, Foufoula-Georgiou, Efi, Smyth, Padhraic, Goulden, Michael L, and Randerson, James T
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Ecological Applications ,Environmental Sciences - Abstract
Changing wildfire regimes in the western US and other fire-prone regions pose considerable risks to human health and ecosystem function. However, our understanding of wildfire behavior is still limited by a lack of data products that systematically quantify fire spread, behavior and impacts. Here we develop a novel object-based system for tracking the progression of individual fires using 375 m Visible Infrared Imaging Radiometer Suite active fire detections. At each half-daily time step, fire pixels are clustered according to their spatial proximity, and are either appended to an existing active fire object or are assigned to a new object. This automatic system allows us to update the attributes of each fire event, delineate the fire perimeter, and identify the active fire front shortly after satellite data acquisition. Using this system, we mapped the history of California fires during 2012-2020. Our approach and data stream may be useful for calibration and evaluation of fire spread models, estimation of near-real-time wildfire emissions, and as means for prescribing initial conditions in fire forecast models.
- Published
- 2022
23. Wildfire response to changing daily temperature extremes in California’s Sierra Nevada
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Gutierrez, Aurora A, Hantson, Stijn, Langenbrunner, Baird, Chen, Bin, Jin, Yufang, Goulden, Michael L, and Randerson, James T
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Burned area has increased across California, especially in the Sierra Nevada range. Recent fires there have had devasting social, economic, and ecosystem impacts. To understand the consequences of new extremes in fire weather, here we quantify the sensitivity of wildfire occurrence and burned area in the Sierra Nevada to daily meteorological variables during 2001–2020. We find that the likelihood of fire occurrence increases nonlinearly with daily temperature during summer, with a 1°C increase yielding a 19 to 22% increase in risk. Area burned has a similar, nonlinear sensitivity, with 1°C of warming yielding a 22 to 25% increase in risk. Solely considering changes in summer daily temperatures from climate model projections, we estimate that by the 2040s, fire number will increase by 51 ± 32%, and burned area will increase by 59 ± 33%. These trends highlight the threat posed to fire management by hotter and drier summers.
- Published
- 2021
24. Climate‐Driven Limits to Future Carbon Storage in California's Wildland Ecosystems
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Coffield, Shane R, Hemes, Kyle S, Koven, Charles D, Goulden, Michael L, and Randerson, James T
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Earth Sciences ,Physical Geography and Environmental Geoscience ,Climate Change Science ,Geology ,Life on Land ,Climate Action ,aboveground live carbon ,ecological forecasting ,machine learning ,random forest ,land management ,Climate change science ,Physical geography and environmental geoscience - Abstract
Abstract: 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 forest and climate analog approaches, 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 are 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 are less severe, with 8.8% ± 5.3% carbon loss. In either scenario, increases in temperature systematically cause biomass declines, and the spread of projected precipitation across 32 CMIP5 models contributes to 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 elevation regions of central and northern California, with a 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.
- Published
- 2021
25. Zonally opposing shifts of the intertropical convergence zone in response to climate change
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Mamalakis, Antonios, Randerson, James T., Yu, Jin-Yi, Pritchard, Michael S., Magnusdottir, Gudrun, Smyth, Padhraic, Levine, Paul A., Yu, Sungduk, and Foufoula-Georgiou, Efi
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Physics - Atmospheric and Oceanic Physics ,Physics - Geophysics - Abstract
Future changes in the location of the intertropical convergence zone (ITCZ) due to climate change are of high interest since they could substantially alter precipitation patterns in the tropics and subtropics. Although models predict a future narrowing of the ITCZ during the 21st century in response to climate warming, uncertainties remain large regarding its future position, with most past work focusing on the zonal-mean ITCZ shifts. Here we use projections from 27 state-of-the-art climate models (CMIP6) to investigate future changes in ITCZ location as a function of longitude and season, in response to climate warming. We document a robust zonally opposing response of the ITCZ, with a northward shift over eastern Africa and the Indian Ocean, and a southward shift in the eastern Pacific and Atlantic Ocean by 2100, for the SSP3-7.0 scenario. Using a two-dimensional energetics framework, we find that the revealed ITCZ response is consistent with future changes in the divergent atmospheric energy transport over the tropics, and sector-mean shifts of the energy flux equator (EFE). The changes in the EFE appear to be the result of zonally opposing imbalances in the hemispheric atmospheric heating over the two sectors, consisting of increases in atmospheric heating over Eurasia and cooling over the Southern Ocean, which contrast with atmospheric cooling over the North Atlantic Ocean due to a model-projected weakening of the Atlantic meridional overturning circulation.
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- 2020
- Full Text
- View/download PDF
26. The role of fire in global forest loss dynamics
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Wees, Dave, Werf, Guido R, Randerson, James T, Andela, Niels, Chen, Yang, and Morton, Douglas C
- Subjects
Ecological Applications ,Environmental Sciences ,Life on Land ,Africa ,Ecosystem ,Fires ,Forests ,Humans ,Indonesia ,Trees ,active fires ,burned area ,deforestation ,fire ,forest loss ,satellite data ,tree mortality ,Biological Sciences ,Ecology ,Biological sciences ,Earth sciences ,Environmental sciences - Abstract
Fires, among other forms of natural and anthropogenic disturbance, play a central role in regulating the location, composition and biomass of forests. Understanding the role of fire in global forest loss is crucial in constraining land-use change emissions and the global carbon cycle. We analysed the relationship between forest loss and fire at 500 m resolution based on satellite-derived data for the 2003-2018 period. Satellite fire data included burned area and active fire detections, to best account for large and small fires, respectively. We found that, on average, 38 ± 9% (± range) of global forest loss was associated with fire, and this fraction remained relatively stable throughout the study period. However, the fraction of fire-related forest loss varied substantially on a regional basis, and showed statistically significant trends in key tropical forest areas. Decreases in the fraction of fire-related forest loss were found where deforestation peaked early in our study period, including the Amazon and Indonesia while increases were found for tropical forests in Africa. The inclusion of active fire detections accounted for 41%, on average, of the total fire-related forest loss, with larger contributions in small clearings in interior tropical forests and human-dominated landscapes. Comparison to higher-resolution fire data with resolutions of 375 and 20 m indicated that commission errors due to coarse resolution fire data largely balanced out omission errors due to missed small fire detections for regional to continental-scale estimates of fire-related forest loss. Besides an improved understanding of forest dynamics, these findings may help to refine and separate fire-related and non-fire-related land-use change emissions in forested ecosystems.
- Published
- 2021
27. The role of fire in global forest loss dynamics.
- Author
-
van Wees, Dave, van der Werf, Guido R, Randerson, James T, Andela, Niels, Chen, Yang, and Morton, Douglas C
- Subjects
Humans ,Trees ,Fires ,Ecosystem ,Africa ,Indonesia ,Forests ,active fires ,burned area ,deforestation ,fire ,forest loss ,satellite data ,tree mortality ,Environmental Sciences ,Biological Sciences ,Ecology - Abstract
Fires, among other forms of natural and anthropogenic disturbance, play a central role in regulating the location, composition and biomass of forests. Understanding the role of fire in global forest loss is crucial in constraining land-use change emissions and the global carbon cycle. We analysed the relationship between forest loss and fire at 500 m resolution based on satellite-derived data for the 2003-2018 period. Satellite fire data included burned area and active fire detections, to best account for large and small fires, respectively. We found that, on average, 38 ± 9% (± range) of global forest loss was associated with fire, and this fraction remained relatively stable throughout the study period. However, the fraction of fire-related forest loss varied substantially on a regional basis, and showed statistically significant trends in key tropical forest areas. Decreases in the fraction of fire-related forest loss were found where deforestation peaked early in our study period, including the Amazon and Indonesia while increases were found for tropical forests in Africa. The inclusion of active fire detections accounted for 41%, on average, of the total fire-related forest loss, with larger contributions in small clearings in interior tropical forests and human-dominated landscapes. Comparison to higher-resolution fire data with resolutions of 375 and 20 m indicated that commission errors due to coarse resolution fire data largely balanced out omission errors due to missed small fire detections for regional to continental-scale estimates of fire-related forest loss. Besides an improved understanding of forest dynamics, these findings may help to refine and separate fire-related and non-fire-related land-use change emissions in forested ecosystems.
- Published
- 2021
28. Zonally contrasting shifts of the tropical rainbelt in response to climate change.
- Author
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Mamalakis, Antonios, Randerson, James T, Yu, Jin-Yi, Pritchard, Michael S, Magnusdottir, Gudrun, Smyth, Padhraic, Levine, Paul A, Yu, Sungduk, and Foufoula-Georgiou, Efi
- Subjects
physics.ao-ph ,physics.geo-ph ,Atmospheric Sciences ,Physical Geography and Environmental Geoscience ,Environmental Science and Management - Abstract
Future changes in the position of the intertropical convergence zone (ITCZ; a narrow band of heavy precipitation in the tropics) with climate change could affect the livelihood and food security of billions of people. Although models predict a future narrowing of the ITCZ, uncertainties remain large regarding its future position, with most past work focusing on zonal-mean shifts. Here we use projections from 27 state-of-the-art (CMIP6) climate models and document a robust zonally-varying ITCZ response to the SSP3-7.0 scenario by 2100, with a northward shift over eastern Africa and the Indian Ocean, and a southward shift in the eastern Pacific and Atlantic Oceans. The zonally-varying response is consistent with changes in the divergent atmospheric energy transport, and sector-mean shifts of the energy flux equator. Our analysis provides insight about mechanisms influencing the future position of the tropical rainbelt, and may allow for more robust projections of climate change impacts.
- Published
- 2021
29. Zonally contrasting shifts of the tropical rain belt in response to climate change
- Author
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Mamalakis, Antonios, Randerson, James T, Yu, Jin-Yi, Pritchard, Michael S, Magnusdottir, Gudrun, Smyth, Padhraic, Levine, Paul A, Yu, Sungduk, and Foufoula-Georgiou, Efi
- Subjects
Climate Action ,physics.ao-ph ,physics.geo-ph ,Atmospheric Sciences ,Physical Geography and Environmental Geoscience ,Environmental Science and Management - Abstract
Future changes in the position of the intertropical convergence zone (ITCZ; a narrow band of heavy precipitation in the tropics) with climate change could affect the livelihood and food security of billions of people. Although models predict a future narrowing of the ITCZ, uncertainties remain large regarding its future position, with most past work focusing on zonal-mean shifts. Here we use projections from 27 state-of-the-art (CMIP6) climate models and document a robust zonally-varying ITCZ response to the SSP3-7.0 scenario by 2100, with a northward shift over eastern Africa and the Indian Ocean, and a southward shift in the eastern Pacific and Atlantic Oceans. The zonally-varying response is consistent with changes in the divergent atmospheric energy transport, and sector-mean shifts of the energy flux equator. Our analysis provides insight about mechanisms influencing the future position of the tropical rainbelt, and may allow for more robust projections of climate change impacts.
- Published
- 2021
30. Graph-Guided Regularized Regression of Pacific Ocean Climate Variables to Increase Predictive Skill of Southwestern U.S. Winter Precipitation.
- Author
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Stevens, Abby, Willett, Rebecca, Mamalakis, Antonios, Foufoula-Georgiou, Efi, Tejedor, Alejandro, Randerson, James T, Smyth, Padhraic, and Wright, Stephen
- Subjects
Climate Action ,Precipitation ,Seasonal forecasting ,Climate models ,Dimensionality reduction ,Machine learning ,Regression ,Atmospheric Sciences ,Oceanography ,Geomatic Engineering ,Meteorology & Atmospheric Sciences - Abstract
Understanding the physical drivers of seasonal hydroclimatic variability and improving predictive skill remains a challenge with important socioeconomic and environmental implications for many regions around the world. Physics-based deterministic models show limited ability to predict precipitation as the lead time increases, due to imperfect representation of physical processes and incomplete knowledge of initial conditions. Similarly, statistical methods drawing upon established climate teleconnections have low prediction skill due to the complex nature of the climate system. Recently, promising data-driven approaches have been proposed, but they often suffer from overparameterization and overfitting due to the short observational record, and they often do not account for spatiotemporal dependencies among covariates (i.e., predictors such as sea surface temperatures). This study addresses these challenges via a predictive model based on a graph-guided regularizer that simultaneously promotes similarity of predictive weights for highly correlated covariates and enforces sparsity in the covariate domain. This approach both decreases the effective dimensionality of the problem and identifies the most predictive features without specifying them a priori. We use large ensemble simulations from a climate model to construct this regularizer, reducing the structural uncertainty in the estimation. We apply the learned model to predict winter precipitation in the southwestern United States using sea surface temperatures over the entire Pacific basin, and demonstrate its superiority compared to other regularization approaches and statistical models informed by known teleconnections. Our results highlight the potential to combine optimally the space-time structure of predictor variables learned from climate models with new graph-based regularizers to improve seasonal prediction.
- Published
- 2020
31. Recent California tree mortality portends future increase in drought-driven forest die-off
- Author
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Madakumbura, Gavin D, Goulden, Michael L, Hall, Alex, Fu, Rong, Moritz, Max A, Koven, Charles D, Kueppers, Lara M, Norlen, Carl A, and Randerson, James T
- Subjects
Agricultural ,Veterinary and Food Sciences ,Environmental Sciences ,Forestry Sciences ,Good Health and Well Being ,tree mortality ,vegetation stress ,drought ,climate change ,climate modeling ,Meteorology & Atmospheric Sciences - Abstract
Vegetation tolerance to drought depends on an array of site-specific environmental and plant physiological factors. This tolerance is poorly understood for many forest types despite its importance for predicting and managing vegetation stress. We analyzed the relationships between precipitation variability and forest die-off in California's Sierra Nevada and introduce a new measure of drought tolerance that emphasizes plant access to subsurface moisture buffers. We applied this metric to California's severe 2012-2015 drought, and show that it predicted the patterns of tree mortality. We then examined future climate scenarios, and found that the probability of droughts that lead to widespread die-off increases threefold by the end of the 21st century. Our analysis shows that tree mortality in the Sierra Nevada will likely accelerate in the coming decades and that forests in the Central and Northern Sierra Nevada that largely escaped mortality in 2012-2015 are vulnerable to die-off.
- Published
- 2020
32. Forecasting Global Fire Emissions on Subseasonal to Seasonal (S2S) Time Scales.
- Author
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Chen, Yang, Randerson, James T, Coffield, Shane R, Foufoula-Georgiou, Efi, Smyth, Padhraic, Graff, Casey A, Morton, Douglas C, Andela, Niels, van der Werf, Guido R, Giglio, Louis, and Ott, Lesley E
- Subjects
El Niño–Southern Oscillation ,autoregression ,fire forecasting ,ocean climate indices ,statistical model ,vapor pressure deficit ,El Nino-Southern Oscillation ,Atmospheric Sciences - Abstract
Fire emissions of gases and aerosols alter atmospheric composition and have substantial impacts on climate, ecosystem function, and human health. Warming climate and human expansion in fire-prone landscapes exacerbate fire impacts and call for more effective management tools. Here we developed a global fire forecasting system that predicts monthly emissions using past fire data and climate variables for lead times of 1 to 6 months. Using monthly fire emissions from the Global Fire Emissions Database (GFED) as the prediction target, we fit a statistical time series model, the Autoregressive Integrated Moving Average model with eXogenous variables (ARIMAX), in over 1,300 different fire regions. Optimized parameters were then used to forecast future emissions. The forecast system took into account information about region-specific seasonality, long-term trends, recent fire observations, and climate drivers representing both large-scale climate variability and local fire weather. We cross-validated the forecast skill of the system with different combinations of predictors and forecast lead times. The reference model, which combined endogenous and exogenous predictors with a 1 month forecast lead time, explained 52% of the variability in the global fire emissions anomaly, considerably exceeding the performance of a reference model that assumed persistent emissions during the forecast period. The system also successfully resolved detailed spatial patterns of fire emissions anomalies in regions with significant fire activity. This study bridges the gap between the efforts of near-real-time fire forecasts and seasonal fire outlooks and represents a step toward establishing an operational global fire, smoke, and carbon cycle forecasting system.
- Published
- 2020
33. The age distribution of global soil carbon inferred from radiocarbon measurements
- Author
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Shi, Zheng, Allison, Steven D, He, Yujie, Levine, Paul A, Hoyt, Alison M, Beem-Miller, Jeffrey, Zhu, Qing, Wieder, William R, Trumbore, Susan, and Randerson, James T
- Subjects
Earth Sciences ,Physical Geography and Environmental Geoscience ,Climate Action ,Meteorology & Atmospheric Sciences ,Physical geography and environmental geoscience - Abstract
Soils contain more carbon than the atmosphere and vegetation combined. An increased flow of carbon from the atmosphere into soil pools could help mitigate anthropogenic emissions of carbon dioxide and climate change. Yet we do not know how quickly soils might respond because the age distribution of soil carbon is uncertain. Here we used 789 radiocarbon (∆14C) profiles, along with other geospatial information, to create globally gridded datasets of mineral soil ∆14C and mean age. We found that soil depth is a primary driver of ∆14C, whereas climate (for example, mean annual temperature) is a major control on the spatial pattern of ∆14C in surface soil. Integrated to a depth of 1 m, global soil carbon has a mean age of 4,830 ± 1,730 yr, with older carbon in deeper layers and permafrost regions. In contrast, vertically resolved land models simulate ∆14C values that imply younger carbon ages and a more rapid carbon turnover. Our data-derived estimates of older mean soil carbon age suggest that soils will accumulate less carbon than predicted by current Earth system models over the twenty-first century. Reconciling these models with the global distribution of soil radiocarbon will require a better representation of the mechanisms that control carbon persistence in soils.
- Published
- 2020
34. Coccidioidomycosis (Valley Fever) Case Data for the Southwestern United States
- Author
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Gorris, Morgan E, Cat, Linh Anh, Matlock, Melissa, Ogunseitan, Oladele A, Treseder, Kathleen K, Randerson, James T, and Zender, Charles S
- Published
- 2020
35. The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty
- Author
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Lawrence, David M, Fisher, Rosie A, Koven, Charles D, Oleson, Keith W, Swenson, Sean C, Bonan, Gordon, Collier, Nathan, Ghimire, Bardan, van Kampenhout, Leo, Kennedy, Daniel, Kluzek, Erik, Lawrence, Peter J, Li, Fang, Li, Hongyi, Lombardozzi, Danica, Riley, William J, Sacks, William J, Shi, Mingjie, Vertenstein, Mariana, Wieder, William R, Xu, Chonggang, Ali, Ashehad A, Badger, Andrew M, Bisht, Gautam, van den Broeke, Michiel, Brunke, Michael A, Burns, Sean P, Buzan, Jonathan, Clark, Martyn, Craig, Anthony, Dahlin, Kyla, Drewniak, Beth, Fisher, Joshua B, Flanner, Mark, Fox, Andrew M, Gentine, Pierre, Hoffman, Forrest, Keppel‐Aleks, Gretchen, Knox, Ryan, Kumar, Sanjiv, Lenaerts, Jan, Leung, L Ruby, Lipscomb, William H, Lu, Yaqiong, Pandey, Ashutosh, Pelletier, Jon D, Perket, Justin, Randerson, James T, Ricciuto, Daniel M, Sanderson, Benjamin M, Slater, Andrew, Subin, Zachary M, Tang, Jinyun, Thomas, R Quinn, Martin, Maria Val, and Zeng, Xubin
- Subjects
Earth Sciences ,Atmospheric Sciences ,Geoinformatics ,Climate Action ,global land model ,Earth System Modeling ,carbon and nitrogen cycling ,hydrology ,benchmarking ,Atmospheric sciences - Abstract
The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time-evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5.
- Published
- 2019
36. Expansion of Coccidioidomycosis Endemic Regions in the United States in Response to Climate Change.
- Author
-
Gorris, Morgan E, Treseder, Kathleen K, Zender, Charles S, and Randerson, James T
- Subjects
Coccidioidomycosis ,Valley fever ,human health ,infectious diseases ,mycoses ,niche model - Abstract
Coccidioidomycosis (Valley fever) is a fungal disease endemic to the southwestern United States. Across this region, temperature and precipitation influence the extent of the endemic region and number of Valley fever cases. Climate projections for the western United States indicate that temperatures will increase and precipitation patterns will shift, which may alter disease dynamics. We estimated the area potentially endemic to Valley fever using a climate niche model derived from contemporary climate and disease incidence data. We then used our model with projections of climate from Earth system models to assess how endemic areas will change during the 21st century. By 2100 in a high warming scenario, our model predicts that the area of climate-limited endemicity will more than double, the number of affected states will increase from 12 to 17, and the number of Valley fever cases will increase by 50%. The Valley fever endemic region will expand north into dry western states, including Idaho, Wyoming, Montana, Nebraska, South Dakota, and North Dakota. Precipitation will limit the disease from spreading into states farther east and along the central and northern Pacific coast. This is the first quantitative estimate of how climate change may influence Valley fever in the United States. Our predictive model of Valley fever endemicity may provide guidance to public health officials to establish disease surveillance programs and design mitigation efforts to limit the impacts of this disease.
- Published
- 2019
37. Machine learning to predict final fire size at the time of ignition.
- Author
-
Coffield, Shane R, Graff, Casey A, Chen, Yang, Smyth, Padhraic, Foufoula-Georgiou, Efi, and Randerson, James T
- Subjects
boreal forests ,decision trees ,fire management ,random forests ,vapour pressure deficit ,Forestry ,Environmental Science and Management ,Ecology ,Forestry Sciences - Abstract
Fires in boreal forests of Alaska are changing, threatening human health and ecosystems. Given expected increases in fire activity with climate warming, insight into the controls on fire size from the time of ignition is necessary. Such insight may be increasingly useful for fire management, especially in cases where many ignitions occur in a short time period. Here we investigated the controls and predictability of final fire size at the time of ignition. Using decision trees, we show that ignitions can be classified as leading to small, medium or large fires with 50.4 ± 5.2% accuracy. This was accomplished using two variables: vapour pressure deficit and the fraction of spruce cover near the ignition point. The model predicted that 40% of ignitions would lead to large fires, and those ultimately accounted for 75% of the total burned area. Other machine learning classification algorithms, including random forests and multi-layer perceptrons, were tested but did not outperform the simpler decision tree model. Applying the model to areas with intensive human management resulted in overprediction of large fires, as expected. This type of simple classification system could offer insight into optimal resource allocation, helping to maintain a historical fire regime and protect Alaskan ecosystems.
- Published
- 2019
38. Expansion of high-latitude deciduous forests driven by interactions between climate warming and fire
- Author
-
Mekonnen, Zelalem A, Riley, William J, Randerson, James T, Grant, Robert F, and Rogers, Brendan M
- Subjects
Biological Sciences ,Ecology ,Climate Action ,Alaska ,Climate Change ,Fires ,Forests ,Global Warming ,Models ,Biological ,Taiga ,Tracheophyta ,CESD-Soil Microbes and Ecosystem Function ,Plant Biology ,Crop and Pasture Production ,Plant biology - Abstract
High-latitude regions have experienced rapid warming in recent decades, and this trend is projected to continue over the twenty-first century1. Fire is also projected to increase with warming2,3. We show here, consistent with changes during the Holocene4, that changes in twenty-first century climate and fire are likely to alter the composition of Alaskan boreal forests. We hypothesize that competition for nutrients after fire in early succession and for light in late succession in a warmer climate will cause shifts in plant functional type. Consistent with observations, our ecosystem model predicts evergreen conifers to be the current dominant tree type in Alaska. However, under future climate and fire, our analysis suggests the relative dominance of deciduous broadleaf trees nearly doubles, accounting for 58% of the Alaska ecosystem's net primary productivity by 2100, with commensurate declines in contributions from evergreen conifer trees and herbaceous plants. Post-fire deciduous broadleaf tree growth under a future climate is sustained from enhanced microbial nitrogen mineralization caused by warmer soils and deeper active layers, resulting in taller trees that compete more effectively for light. The expansion of deciduous broadleaf forests will affect the carbon cycle, surface energy fluxes and ecosystem function, thereby modifying important feedbacks with the climate system.
- Published
- 2019
39. Improving Representation of Deforestation Effects on Evapotranspiration in the E3SM Land Model
- Author
-
Cai, Xitian, Riley, William J, Zhu, Qing, Tang, Jinyun, Zeng, Zhenzhong, Bisht, Gautam, and Randerson, James T
- Subjects
Earth Sciences ,Atmospheric Sciences ,Geoinformatics ,Life on Land ,Climate Action ,Atmospheric sciences - Abstract
Evapotranspiration (ET) plays an important role in land-atmosphere coupling of energy, water, and carbon cycles. Following deforestation, ET is typically observed to decrease substantially as a consequence of decreases in leaf area and roots and increases in runoff. Changes in ET (latent heat flux) revise the surface energy and water budgets, which further affects large-scale atmospheric dynamics and feeds back positively or negatively to long-term forest sustainability. In this study, we used observations from a recent synthesis of 29 pairs of adjacent intact and deforested FLUXNET sites to improve model parameterization of stomatal characteristics, photosynthesis, and soil water dynamics in version 1 of the Energy Exascale Earth System Model (E3SM) Land Model (ELMv1). We found that default ELMv1 predicts an increase in ET after deforestation, likely leading to incorrect estimates of the effects of deforestation on land-atmosphere coupling. The calibrated model accurately represented the FLUXNET observed deforestation effects on ET. Importantly, the search for global optimal parameters converged at values consistent with recent observational syntheses, confirming the reliability of the calibrated physical parameters. Applying this improved model parameterization to the globe scale reduced the bias of annual ET simulation by up to ~600 mm/year. Analysis on the roles of parameters suggested that future model development to improve ET simulation should focus on stomatal resistance and soil water-related parameterizations. Finally, our predicted differences in seasonal ET changes from deforestation are large enough to substantially affect land-atmosphere coupling and should be considered in such studies.
- Published
- 2019
40. Reply to: A critical examination of a newly proposed interhemispheric teleconnection to Southwestern US winter precipitation.
- Author
-
Mamalakis, Antonios, Yu, Jin-Yi, Randerson, James T, AghaKouchak, Amir, and Foufoula-Georgiou, Efi
- Subjects
MD Multidisciplinary - Published
- 2019
41. Comparison With Global Soil Radiocarbon Observations Indicates Needed Carbon Cycle Improvements in the E3SM Land Model
- Author
-
Chen, Jinsong, Zhu, Qing, Riley, William J, He, Yujie, Randerson, James T, and Trumbore, Susan
- Subjects
Earth Sciences ,Geophysics ,Climate Action ,Earth System Models ,advanced land modeling ,soil organic carbon ,radiocarbon ,statistical analysis ,machine learning - Abstract
We evaluated global soil organic carbon (SOC) stocks and turnover time predictions from a global land model (ELMv1-ECA) integrated in an Earth System Model (E3SM) by comparing them with observed soil bulk and Δ14C values around the world. We analyzed observed and simulated SOC stocks and Δ14C values using machine learning methods at the Earth System Model grid cell scale (~200 km). In grid cells with sufficient observations, the model provided reasonable estimates of soil carbon stocks across soil depth and Δ14C values near the surface but underestimated Δ14C at depth. Among many explanatory variables, soil albedo index, soil order, plant function type, air temperature, and SOC content were major factors affecting predicted SOC Δ14C values. The influences of soil albedo index, soil order, and air temperature were primarily important in the shallow subsurface (≤30 cm). We also performed sensitivity studies using different vertical root distributions and decomposition turnover times and compared to observed SOC stock and Δ14C profiles. The analyses support the role of vegetation in affecting soil carbon turnover, particularly in deep soil, possibly through supplying fresh carbon and degrading physical-chemical protection of SOC via root activities. Allowing for grid cell-specific rooting and decomposition rates substantially reduced discrepancies between observed and predicted Δ14C values and SOC content. Our results highlight the need for more explicit representation of roots, microbes, and soil physical protection in land models.
- Published
- 2019
42. Economic carbon cycle feedbacks may offset additional warming from natural feedbacks.
- Author
-
Woodard, Dawn L, Davis, Steven J, and Randerson, James T
- Subjects
carbon cycle feedbacks ,climate change ,economic damages ,fossil fuels ,integrated assessment models ,MD Multidisciplinary - Abstract
As the Earth warms, carbon sinks on land and in the ocean will weaken, thereby increasing the rate of warming. Although natural mechanisms contributing to this positive climate-carbon feedback have been evaluated using Earth system models, analogous feedbacks involving human activities have not been systematically quantified. Here we conceptualize and estimate the magnitude of several economic mechanisms that generate a carbon-climate feedback, using the Kaya identity to separate a net economic feedback into components associated with population, GDP, heating and cooling, and the carbon intensity of energy production and transportation. We find that climate-driven decreases in economic activity (GDP) may in turn decrease human energy use and thus fossil fuel CO2 emissions. In a high radiative forcing scenario, such decreases in economic activity reduce fossil fuel emissions by 13% this century, lowering atmospheric CO2 by over 100 ppm in 2100. The natural carbon-climate feedback, in contrast, increases atmospheric CO2 over this period by a similar amount, and thus, the net effect including both feedbacks is nearly zero. Our work highlights the importance of improving the representation of climate-economic feedbacks in scenarios of future change. Although the effects of climate warming on the economy may offset weakening land and ocean carbon sinks, a loss of economic productivity will have high societal costs, potentially increasing wealth inequity and limiting resources available for effective adaptation.
- Published
- 2019
43. Smoke radiocarbon measurements from Indonesian fires provide evidence for burning of millennia-aged peat.
- Author
-
Wiggins, Elizabeth B, Czimczik, Claudia I, Santos, Guaciara M, Chen, Yang, Xu, Xiaomei, Holden, Sandra R, Randerson, James T, Harvey, Charles F, Kai, Fuu Ming, and Yu, Liya E
- Subjects
global carbon cycle ,human health ,isotope ,land cover change ,tropical peatlands ,MD Multidisciplinary - Abstract
In response to a strong El Niño, fires in Indonesia during September and October 2015 released a large amount of carbon dioxide and created a massive regional smoke cloud that severely degraded air quality in many urban centers across Southeast Asia. Although several lines of evidence indicate that peat burning was a dominant contributor to emissions in the region, El Niño-induced drought is also known to increase deforestation fires and agricultural waste burning in plantations. As a result, uncertainties remain with respect to partitioning emissions among different ecosystem and fire types. Here we measured the radiocarbon content (14C) of carbonaceous aerosol samples collected in Singapore from September 2014 through October 2015, with the aim of identifying the age and origin of fire-emitted fine particulate matter (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm). The Δ14C of fire-emitted aerosol was -76 ± 51‰, corresponding to a carbon pool of combusted organic matter with a mean turnover time of 800 ± 420 y. Our observations indicated that smoke plumes reaching Singapore originated primarily from peat burning (∼85%), and not from deforestation fires or waste burning. Atmospheric transport modeling confirmed that fires in Sumatra and Borneo were dominant contributors to elevated PM2.5 in Singapore during the fire season. The mean age of the carbonaceous aerosol, which predates the Industrial Revolution, highlights the importance of improving peatland fire management during future El Niño events for meeting climate mitigation and air quality commitments.
- Published
- 2018
44. Plant Physiological Responses to Rising CO2 Modify Simulated Daily Runoff Intensity With Implications for Global‐Scale Flood Risk Assessment
- Author
-
Kooperman, Gabriel J, Fowler, Megan D, Hoffman, Forrest M, Koven, Charles D, Lindsay, Keith, Pritchard, Michael S, Swann, Abigail LS, and Randerson, James T
- Subjects
Climate Action ,climate change ,flooding ,runoff ,precipitation ,stomatal conductance ,Earth system model ,Meteorology & Atmospheric Sciences - Abstract
Climate change is expected to increase the frequency of flooding events and, thus, the risks of flood-related mortality and infrastructure damage. Global-scale assessments of future flooding from Earth system models based only on precipitation changes neglect important processes that occur within the land surface, particularly plant physiological responses to rising CO2. Higher CO2 can reduce stomatal conductance and transpiration, which may lead to increased soil moisture and runoff in some regions, promoting flooding even without changes in precipitation. Here we assess the relative impacts of plant physiological and radiative greenhouse effects on changes in daily runoff intensity over tropical continents using the Community Earth System Model. We find that extreme percentile rates increase significantly more than mean runoff in response to higher CO2. Plant physiological effects have a small impact on precipitation intensity but are a dominant driver of runoff intensification, contributing to one half of the 99th and one third of the 99.9th percentile runoff intensity changes.
- Published
- 2018
45. The International Land Model Benchmarking (ILAMB) System: Design, Theory, and Implementation
- Author
-
Collier, Nathan, Hoffman, Forrest M, Lawrence, David M, Keppel‐Aleks, Gretchen, Koven, Charles D, Riley, William J, Mu, Mingquan, and Randerson, James T
- Subjects
Earth Sciences ,Atmospheric Sciences ,Geoinformatics ,2.5 Research design and methodologies (aetiology) ,Life on Land ,benchmarking ,Earth system model ,model evaluation ,Atmospheric sciences - Abstract
The increasing complexity of Earth system models has inspired efforts to quantitatively assess model fidelity through rigorous comparison with best available measurements and observational data products. Earth system models exhibit a high degree of spread in predictions of land biogeochemistry, biogeophysics, and hydrology, which are sensitive to forcing from other model components. Based on insights from prior land model evaluation studies and community workshops, the authors developed an open source model benchmarking software package that generates graphical diagnostics and scores model performance in support of the International Land Model Benchmarking (ILAMB) project. Employing a suite of in situ, remote sensing, and reanalysis data sets, the ILAMB package performs comprehensive model assessment across a wide range of land variables and generates a hierarchical set of web pages containing statistical analyses and figures designed to provide the user insights into strengths and weaknesses of multiple models or model versions. Described here is the benchmarking philosophy and mathematical methodology embodied in the most recent implementation of the ILAMB package. Comparison methods unique to a few specific data sets are presented, and guidelines for configuring an ILAMB analysis and interpreting resulting model performance scores are discussed. ILAMB is being adopted by modeling teams and centers during model development and for model intercomparison projects, and community engagement is sought for extending evaluation metrics and adding new observational data sets to the benchmarking framework.
- Published
- 2018
46. Harnessing cross-border resources to confront climate change
- Author
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Aburto-Oropeza, Octavio, Johnson, Andrew F, Agha, Mickey, Allen, Edith B, Allen, Michael F, González, Jesús Arellano, Moreno, Diego M Arenas, Beas-Luna, Rodrigo, Butterfield, Scott, Caetano, Gabriel, Caselle, Jennifer E, Gaytán, Gamaliel Castañeda, Castorani, Max CN, Cat, Linh Anh, Cavanaugh, Kyle, Chambers, Jeffrey Q, Cooper, Robert D, Arafeh-Dalmau, Nur, Dawson, Todd, de la Vega Pérez, Aníbal Díaz, DiMento, Joseph FC, Guerrero, Saúl Domínguez, Edwards, Matthew, Ennen, Joshua R, Estrada-Medina, Hector, Fierro-Estrada, Natalia, Gadsden, Héctor, Galina-Tessaro, Patricia, Gibbons, Paul M, Goode, Eric V, Gorris, Morgan E, Harmon, Thomas, Hecht, Susanna, Fragoso, Marco Antonio Heredia, Hernández-Solano, Alan, Hernández-Cortés, Danae, Hernández-Carmona, Gustavo, Hillard, Scott, Huey, Raymond B, Hufford, Matthew B, Jenerette, G Darrel, Jiménez-Osornio, Juan, López-Nava, Karla Joana, Reséndiz, Rafael A Lara, Leslie, Heather M, López-Feldman, Alejandro, Luja, Víctor H, Méndez, Norberto Martínez, Mautz, William J, Medellín-Azuara, Josué, Meléndez-Torres, Cristina, de la Cruz, Fausto R Méndez, Micheli, Fiorenza, Miles, Donald B, Montagner, Giovanna, Montaño-Moctezuma, Gabriela, Müller, Johannes, Oliva, Paulina, Álvarez, José Abraham Ortinez, Ortiz-Partida, J Pablo, Palleiro-Nayar, Julio, Figueroa, Víctor Hugo Páramo, Parnell, P Ed, Raimondi, Peter, Ramírez-Valdez, Arturo, Randerson, James T, Reed, Daniel C, Riquelme, Meritxell, Torres, Teresita Romero, Rosen, Philip C, Ross-Ibarra, Jeffrey, Sánchez-Cordero, Victor, Sandoval-Solis, Samuel, Santos, Juan Carlos, Sawers, Ruairidh, Sinervo, Barry, Sites, Jack W, Sosa-Nishizaki, Oscar, Stanton, Travis, Stapp, Jared R, Stewart, Joseph AE, Torre, Jorge, Torres-Moye, Guillermo, Treseder, Kathleen K, Valdez-Villavicencio, Jorge, Jiménez, Fernando I Valle, Vaughn, Mercy, Welton, Luke, Westphal, Michael F, Woolrich-Piña, Guillermo, Yunez-Naude, Antonio, Zertuche-González, José A, and Taylor, J Edward
- Subjects
Environmental Sciences ,Political Science ,Human Society ,Climate Action ,US southwest ,Northern Mexico ,Binational collaborations ,Environmental innovation ,Cross-border transformation ,Research integration ,Agricultural and Veterinary Sciences ,Studies in Human Society ,Agricultural ,veterinary and food sciences ,Environmental sciences ,Human society - Abstract
The US and Mexico share a common history in many areas, including language and culture. They face ecological changes due to the increased frequency and severity of droughts and rising energy demands; trends that entail economic costs for both nations and major implications for human wellbeing. We describe an ongoing effort by the Environment Working Group (EWG), created by The University of California's UC-Mexico initiative in 2015, to promote binational research, teaching, and outreach collaborations on the implications of climate change for Mexico and California. We synthesize current knowledge about the most pressing issues related to climate change in the US-Mexico border region and provide examples of cross-border discoveries and research initiatives, highlighting the need to move forward in six broad rubrics. This and similar binational cooperation efforts can lead to improved living standards, generate a collaborative mindset among participating universities, and create an international network to address urgent sustainability challenges affecting both countries.
- Published
- 2018
47. A new interhemispheric teleconnection increases predictability of winter precipitation in southwestern US.
- Author
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Mamalakis, Antonios, Yu, Jin-Yi, Randerson, James T, AghaKouchak, Amir, and Foufoula-Georgiou, Efi
- Abstract
Reliable prediction of seasonal precipitation in the southwestern US (SWUS) remains a challenge with significant implications for the economy, water security and ecosystem management of the region. Winter precipitation in the SWUS has been linked to several climate modes, including the El Niño-Southern Oscillation (ENSO), with limited predictive ability. Here we report evidence that late-summer sea surface temperature and geopotential height anomalies close to New Zealand exhibit higher correlation with SWUS winter precipitation than ENSO, enhancing the potential for earlier and more accurate prediction. The teleconnection depends on a western Pacific ocean-atmosphere pathway, whereby sea surface temperature anomalies propagate from the southern to the northern hemisphere during boreal summer. Analysis also shows an amplification of this new teleconnection over the past four decades. Our work highlights the need to understand the dynamic nature of the coupled ocean-atmosphere system in a changing climate for improving future predictions of regional precipitation.
- Published
- 2018
48. Forest response to rising CO2 drives zonally asymmetric rainfall change over tropical land
- Author
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Kooperman, Gabriel J, Chen, Yang, Hoffman, Forrest M, Koven, Charles D, Lindsay, Keith, Pritchard, Michael S, Swann, Abigail LS, and Randerson, James T
- Subjects
Agricultural ,Veterinary and Food Sciences ,Biological Sciences ,Earth Sciences ,Atmospheric Sciences ,Forestry Sciences ,Climate Action ,Physical Geography and Environmental Geoscience ,Environmental Science and Management - Abstract
Understanding how anthropogenic CO2 emissions will influence future precipitation is critical for sustainably managing ecosystems, particularly for drought-sensitive tropical forests. Although tropical precipitation change remains uncertain, nearly all models from the Coupled Model Intercomparison Project Phase 5 predict a strengthening zonal precipitation asymmetry by 2100, with relative increases over Asian and African tropical forests and decreases over South American forests. Here we show that the plant physiological response to increasing CO2 is a primary mechanism responsible for this pattern. Applying a simulation design in the Community Earth System Model in which CO2 increases are isolated over individual continents, we demonstrate that different circulation, moisture and stability changes arise over each continent due to declines in stomatal conductance and transpiration. The sum of local atmospheric responses over individual continents explains the pan-tropical precipitation asymmetry. Our analysis suggests that South American forests may be more vulnerable to rising CO2 than Asian or African forests.
- Published
- 2018
49. Iterative near-term ecological forecasting: Needs, opportunities, and challenges
- Author
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Dietze, Michael C, Fox, Andrew, Beck-Johnson, Lindsay M, Betancourt, Julio L, Hooten, Mevin B, Jarnevich, Catherine S, Keitt, Timothy H, Kenney, Melissa A, Laney, Christine M, Larsen, Laurel G, Loescher, Henry W, Lunch, Claire K, Pijanowski, Bryan C, Randerson, James T, Read, Emily K, Tredennick, Andrew T, Vargas, Rodrigo, Weathers, Kathleen C, and White, Ethan P
- Subjects
Bayes Theorem ,Climate Change ,Ecology ,Ecosystem ,Forecasting ,Humans ,Models ,Theoretical ,forecast ,ecology ,prediction - Abstract
Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.
- Published
- 2018
50. Using radiocarbon to constrain black and organic carbon aerosol sources in Salt Lake City
- Author
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Mouteva, Gergana O, Randerson, James T, Fahrni, Simon M, Bush, Susan E, Ehleringer, James R, Xu, Xiaomei, Santos, Guaciara M, Kuprov, Roman, Schichtel, Bret A, and Czimczik, Claudia I
- Published
- 2017
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