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1. Subseasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models

   https://doi.org/10.1175/AIES-D-22-0038.1  

   Weirich-Benet, Elizabeth ; Pyrina, Maria ; Jiménez-Esteve, Bernat ; Fraenkel, Ernest ; Cohen, Judah ; Domeisen, Daniela I. V. ( April 2023 , Artificial Intelligence for the Earth Systems)

   Heatwaves are extreme near-surface temperature events that can have substantial impacts on ecosystems and society. Early warning systems help to reduce these impacts by helping communities prepare for hazardous climate-related events. However, state-of-the-art prediction systems can often not make accurate forecasts of heatwaves more than two weeks in advance, which are required for advance warnings. We therefore investigate the potential of statistical and machine learning methods to understand and predict central European summer heatwaves on time scales of several weeks. As a first step, we identify the most important regional atmospheric and surface predictors based on previous studies and supported by a correlation analysis: 2-m air temperature, 500-hPa geopotential, precipitation, and soil moisture in central Europe, as well as Mediterranean and North Atlantic sea surface temperatures, and the North Atlantic jet stream. Based on these predictors, we apply machine learning methods to forecast two targets: summer temperature anomalies and the probability of heatwaves for 1–6 weeks lead time at weekly resolution. For each of these two target variables, we use both a linear and a random forest model. The performance of these statistical models decays with lead time, as expected, but outperforms persistence and climatology at all lead times. For lead times longer than two weeks, our machine learning models compete with the ensemble mean of the European Centre for Medium-Range Weather Forecast’s hindcast system. We thus show that machine learning can help improve subseasonal forecasts of summer temperature anomalies and heatwaves.

   Heatwaves (prolonged extremely warm temperatures) cause thousands of fatalities worldwide each year. These damaging events are becoming even more severe with climate change. This study aims to improve advance predictions of summer heatwaves in central Europe by using statistical and machine learning methods. Machine learning models are shown to compete with conventional physics-based models for forecasting heatwaves more than two weeks in advance. These early warnings can be used to activate effective and timely response plans targeting vulnerable communities and regions, thereby reducing the damage caused by heatwaves.

    

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2. Stand basal area and solar radiation amplify white spruce climate sensitivity in interior Alaska: Evidence from carbon isotopes and tree rings

   https://doi.org/10.1111/gcb.14511  

   Nicklen, Elizabeth Fleur ; Roland, Carl A. ; Csank, Adam Z. ; Wilmking, Martin ; Ruess, Roger W. ; Muldoon, Laurel Ann ( November 2018 , Global Change Biology)

   The negative growth response of North American boreal forest trees to warm summers is well documented and the constraint of competition on tree growth widely reported, but the potential interaction between climate and competition in the boreal forest is not well studied. Because competition may amplify or mute tree climate‐growth responses, understanding the role current forest structure plays in tree growth responses to climate is critical in assessing and managing future forest productivity in a warming climate. Using white spruce tree ring and carbon isotope data from a long‐term vegetation monitoring program in Denali National Park and Preserve, we investigated the hypotheses that (a) competition and site moisture characteristics mediate white spruce radial growth response to climate and (b) moisture limitation is the mechanism for reduced growth. We further examined the impact of large reproductive events (mast years) on white spruce radial growth and stomatal regulation. We found that competition and site moisture characteristics mediated white spruce climate‐growth response. The negative radial growth response to warm and dry early‐ to mid‐summer and dry late summer conditions intensified in high competition stands and in areas receiving high potential solar radiation. Discrimination against 13C was reduced in warm, dry summers and further diminished on south‐facing hillslopes and in high competition stands, but was unaffected by climate in open floodplain stands, supporting the hypothesis that competition for moisture limits growth. Finally, during mast years, we found a shift in current year's carbon resources from radial growth to reproduction, reduced 13C discrimination, and increased intrinsic water‐use efficiency. Our findings highlight the importance of temporally variable and confounded factors, such as forest structure and climate, on the observed climate‐growth response of white spruce. Thus, white spruce growth trends and productivity in a warming climate will likely depend on landscape position and current forest structure. 

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3. Atmospheric Modeling Study on Convection-Triggered Teleconnections Driving the Summer North American Dipole

   https://doi.org/10.1175/JCLI-D-23-0015.1  

   Bai, Husile ; Strong, Courtenay ( October 2023 , Journal of Climate)

   The summer North American dipole (NAD) is a pattern of climate variability linked to variations in boreal forest seed production and migration of seed-eating birds. This is a modeling investigation of two teleconnections identified as drivers of the NAD in prior observational work: 1) tropically sourced atmospheric Rossby waves associated with anomalies in the phase distribution of the Madden–Julian oscillation (MJO) (i.e., phases 1 and 6 are anomalously prominent), and 2) a pan-Pacific atmospheric Rossby wave linked to East Asian monsoonal (EAM) convection. Sea surface temperature (SST) boundary forcing experiments were conducted with the Community Earth System Model 2 (CESM2) to trigger convection patterns that align with those observed during EAM and nonuniform phase distributions of MJO. For the EAM case, an El Niño–like SST dipole pattern combined with cool southern Japan SST forcing produced a convection and jet stream shift anomaly over East Asia and the northern Pacific with a positive NAD pattern downstream over North America, similar to the observed pattern when precipitation over East Asia (P_EA) is relatively high. A companion experiment with only ENSO-like SST forcing also produced the NAD but featured a different structure over the Eurasian continent with a response resembling the summer east Atlantic (SEA) pattern over eastern North America and the eastern Atlantic. Simulation results suggest that the southern Japan SST forcing region has a secondary importance in triggering the NAD, producing only a somewhat NAD-like pattern by itself and only slightly improving the NAD produced by ENSO-like forcing. Simulations using SST forcing to induce seasonal convection anomalies with spatial patterns similar to anomalously frequent occurrence of MJO phase 1 (phase 6) produced circulation response patterns resembling the positive NAD (negative NAD).

    

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4. Interannual and Seasonal Drivers of Carbon Cycle Variability Represented by the Community Earth System Model (CESM2)

   https://doi.org/10.1029/2021GB007034  

   Wieder, William R. ; Butterfield, Zachary ; Lindsay, Keith ; Lombardozzi, Danica L. ; Keppel‐Aleks, Gretchen ( September 2021 , Global Biogeochemical Cycles)

   Earth system models are intended to make long‐term projections, but they can be evaluated at interannual and seasonal time scales. Although the Community Earth System Model (CESM2) showed improvements in a number of terrestrial carbon cycle benchmarks, relative to its predecessor, our analysis suggests that the interannual variability (IAV) in net terrestrial carbon fluxes did not show similar improvements. The model simulated low IAV of net ecosystem production (NEP), resulting in a weaker than observed sensitivity of the carbon cycle to climate variability. Low IAV in net fluxes likely resulted from low variability in gross primary productivity (GPP)—especially in the tropics—and a high covariation between GPP and ecosystem respiration. Although lower than observed, the IAV of NEP had significant climate sensitivities, with positive NEP anomalies associated with warmer and drier conditions in high latitudes, and with wetter and cooler conditions in mid and low latitudes. We identified two dominant modes of seasonal variability in carbon cycle flux anomalies in our fully coupled CESM2 simulations that are characterized by seasonal amplification and redistribution of ecosystem fluxes. Seasonal amplification of net and gross carbon fluxes showed climate sensitivities mirroring those of annual fluxes. Seasonal redistribution of carbon fluxes is initiated by springtime temperature anomalies, but subsequently negative feedbacks in soil moisture during the summer and fall result in net annual carbon losses from land. These modes of variability are also seen in satellite proxies of GPP, suggesting that CESM2 appropriately represents regional sensitivities of photosynthesis to climate variability on seasonal time scales.

    

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5. The predictability of near‐term forest biomass change in boreal North America

   https://doi.org/10.1002/ecs2.4737  

   Burrell, Arden L. ; Cooperdock, Sol ; Potter, Stefano ; Berner, Logan T. ; Hember, Robert ; Macander, Matthew J. ; Walker, Xanthe J. ; Massey, Richard ; Foster, Adrianna C. ; Mack, Michelle C. ; et al ( January 2024 , Ecosphere)

   Climate change is driving substantial changes in North American boreal forests, including changes in productivity, mortality, recruitment, and biomass. Despite the importance for carbon budgets and informing management decisions, there is a lack of near‐term (5–30 year) forecasts of expected changes in aboveground biomass (AGB). In this study, we forecast AGB changes across the North American boreal forest using machine learning, repeat measurements from 25,000 forest inventory sites, and gridded geospatial datasets. We find that AGB change can be predicted up to 30 years into the future, and that training on sites across the entire domain allows accurate predictions even in regions with only a small amount of existing field data. While predicting AGB loss is less skillful than gains, using a multi‐model ensemble can improve the accuracy in detecting change direction to >90% for observed increases, and up to 70% for observed losses. Higher stem density, winter temperatures, and the presence of temperate tree species in forest plots were positively associated with AGB change, whereas greater initial biomass, continentality (difference between mean summer and winter temperatures), prevalence of black spruce (Picea mariana), summer precipitation, and early warning metrics from long‐term remote sensing time series were negatively associated with AGB change. Across the domain, we predict nondisturbance‐induced declines in AGB at 23% of sites by 2030. The approach developed here can be used to estimate near‐future forest biomass in boreal North America and inform relevant management decisions. Our study also highlights the power of machine learning multi‐model ensembles when trained on a large volume of forest inventory plots, which could be applied to other regions with adequate plot density and spatial coverage.

    

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