Leveraging 35 years of <i>Pinus taeda</i> research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

Abstract

Abstract. Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model-data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA) focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG) forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO 2 ) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6 × 10 5 km 2 region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions of nutrient fertilization experiments, irrigation experiments, and CO 2 enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments. Predictions of how growth responded to elevated CO 2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO 2 study were allowed to have different mortality parameters than the other field plots in the region. We present predictions of stem biomass productivity under elevated CO 2 , decreased precipPublished by Copernicus Publications on behalf of the European Geosciences Union. itation, and increased nutrient availability that include estimates of uncertainty for the southeastern US. Overall, we (1) demonstrated how three decades of research in southeastern US planted pine forests can be used to develop DA techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters and (2) developed a tool for the development of future predictions of forest productivity for natural resource managers that leverage a rich dataset of integrated ecosystem observations across a region.

Assessing the response of forest productivity to climate extremes in Switzerland using model–data fusion

“…Our study demonstrates that it is possible to integrate monitoring data from multiple networks across a wide bioclimatic gradient into a process‐based forest ecosystem model 3‐PG. The resulting uncertainty in the parameter estimates was relatively low (Tables S3 and S4) and comparable to other studies that calibrated the 3‐PG model (Augustynczik et al, ; Thomas et al, ). Not surprisingly, the monitoring data were most informative for constraining parameters that are directly related to stand structure.…”

A mid‐century ecological forecast with partitioned uncertainty predicts increases in loblolly pine forest productivity

“…Here, we advance the work in Thomas et al. () by developing a forecast of productivity under future climate that includes parameter, process, and climate model uncertainty and compares the relative contributions of these sources of uncertainty to the forecast uncertainty.…”

“…We focus our forecast on planted loblolly pine ( Pinus taeda ) forests of the southeastern United States, a globally important ecosystem that supplies 16% of the world's timber volume (Prestemon and Abt ) and has the data availability necessary to estimate the probability distribution of ecosystem model parameters (Thomas et al. ). Under the RCP 8.5 scenario, the region is projected to warm by 0.5°–3°C between the 1985–2010 and 2030–2055 periods, depending on the climate model and location in the region (Fig.…”

“…Recently, we (Thomas et al. ) used data assimilation methods to develop uncertainty estimates for parameters and model process using an ecosystem model similar to that of Gonzalez‐Benecke et al. ().…”

“…Our approach has been used to evaluate the contribution of experimental treatments to parameter optimization and predict model sensitivity to CO 2 , drought, and nutrient addition (Thomas et al. ). Here, we advance the work in Thomas et al.…”