Increase in Arctic Oscillations explains most interannual variability in Russia’s wildfires

Over the past two decades, the escalating emissions of greenhouse gases from boreal wildfires in the Northern Hemisphere have drawn significant attention, underscoring an unprecedented wildfire season in 2021. Our calculations indicate that between 2002 and 2020, wildfires in Russia released approximately 726 ± 280 Tg CO2eqv yr−1. This aligns closely with similar estimates derived from remote sensing data, far surpassing the earlier approximations found in the Russian National Inventory Report (NIR) by a factor of 2 to 3. Notably, in 2021 alone, Russia’s wildfires emitted an exceptionally high amount of 1,700 Tg CO2eqv, exceeding the carbon emissions from the country’s fossil fuel consumption. Consequently, this situation led to an almost complete counterbalance of carbon assimilation by Russian forests. Our analysis attributes over 50% of the variation in wildfire frequency between 2002 and 2021 to shifts in the Arctic Oscillation (AO). This suggests a potential for utilizing AO as a predictive variable for wildfires. It’s noteworthy that the AO itself is influenced by the sustained regression of Arctic sea-ice. From this, it can be inferred that in the foreseeable future, Russian forests might undergo a transition from their role as carbon sinks to the potential net contributors of carbon to the atmosphere

Data_Sheet_2_Increase in Arctic Oscillations explains most interannual variability in Russia’s wildfires.docx

Over the past two decades, the escalating emissions of greenhouse gases from boreal wildfires in the Northern Hemisphere have drawn significant attention, underscoring an unprecedented wildfire season in 2021. Our calculations indicate that between 2002 and 2020, wildfires in Russia released approximately 726 ± 280 Tg CO2eqv yr−1. This aligns closely with similar estimates derived from remote sensing data, far surpassing the earlier approximations found in the Russian National Inventory Report (NIR) by a factor of 2 to 3. Notably, in 2021 alone, Russia’s wildfires emitted an exceptionally high amount of 1,700 Tg CO2eqv, exceeding the carbon emissions from the country’s fossil fuel consumption. Consequently, this situation led to an almost complete counterbalance of carbon assimilation by Russian forests. Our analysis attributes over 50% of the variation in wildfire frequency between 2002 and 2021 to shifts in the Arctic Oscillation (AO). This suggests a potential for utilizing AO as a predictive variable for wildfires. It’s noteworthy that the AO itself is influenced by the sustained regression of Arctic sea-ice. From this, it can be inferred that in the foreseeable future, Russian forests might undergo a transition from their role as carbon sinks to the potential net contributors of carbon to the atmosphere.</p

Data_Sheet_1_Increase in Arctic Oscillations explains most interannual variability in Russia’s wildfires.docx

Over the past two decades, the escalating emissions of greenhouse gases from boreal wildfires in the Northern Hemisphere have drawn significant attention, underscoring an unprecedented wildfire season in 2021. Our calculations indicate that between 2002 and 2020, wildfires in Russia released approximately 726 ± 280 Tg CO2eqv yr−1. This aligns closely with similar estimates derived from remote sensing data, far surpassing the earlier approximations found in the Russian National Inventory Report (NIR) by a factor of 2 to 3. Notably, in 2021 alone, Russia’s wildfires emitted an exceptionally high amount of 1,700 Tg CO2eqv, exceeding the carbon emissions from the country’s fossil fuel consumption. Consequently, this situation led to an almost complete counterbalance of carbon assimilation by Russian forests. Our analysis attributes over 50% of the variation in wildfire frequency between 2002 and 2021 to shifts in the Arctic Oscillation (AO). This suggests a potential for utilizing AO as a predictive variable for wildfires. It’s noteworthy that the AO itself is influenced by the sustained regression of Arctic sea-ice. From this, it can be inferred that in the foreseeable future, Russian forests might undergo a transition from their role as carbon sinks to the potential net contributors of carbon to the atmosphere.</p

Editorial: Achieving sustainable development goal 13: resilience and adaptive capacity of temperate and boreal forests under climate change

ISSN:2624-893

Climatic factors controlling plant sensitivity to warming

Abstract Plant sensitivity to warming can be expressed as β or the number of days of advance in leafing or flowering events per 1°C of Mean Annual Temperature (MAT) change. Many local studies demonstrate that β estimates for spring flowering species are usually larger than estimates for plants flowering in summer or fall. Until now, however, neither observational nor experimental estimates of this parameter were considered to be climate or geographically dependent. Here we question this paradigm through reanalysis of observational β estimates and mathematical modeling of the seasonal warming signal. Statistical analysis of a large number of bulk (averaged over species) estimates of β derived from the Pan European Phenology Data network (PEP725) revealed a positive spatial correlation with MAT, as well as a negative correlation with the Seasonal Temperature Range (STR). These spatial correlations of bulk β values as well as interseasonal variability in β were explained using a simple deterministic model of the Thermal Growing Season (TGS). More specifically, we found that the geographic distribution of bulk plant sensitivity to warming as well as the seasonal decline of β were controlled by the seasonal patterns in the warming signal and by average soil thermal properties. Thus, until recently, plants managed to keep pace with climate warming by shifting their leafing and flowering events by the same number of days as the length of the period of weather suitable for their growth. Our model predicts, however, an even greater increase in the TGS for subsequent increases in MAT. Depending on how they interact with other factors such as changes in precipitation and increased temperature variability, these longer thermal growing seasons may not be beneficial for plant growth

Patterns of mega-forest fires in east Siberia will become less predictable with climate warming

Very large fires covering tens to hundreds of hectares, termed mega-fires, have become a prominent feature of fire regime in taiga forests worldwide, and in Siberia in particular. Here, we applied an array of machine learning algorithms and statistical methods to estimate the relative importance of various factors in observed patterns of Eastern Siberian fires mapped with satellite data. More specifically, we tested linkages of “hot spot” ignitions with 42 variables representing landscape characteristics, climatic, and anthropogenic factors, such as human population density, locations of settlements and road networks. Analysis of data spanning seventeen years (2001–2017) showed that during low or moderately high fire seasons, models with full set of variables predict locations of fires with a very high probability (AUC = 95%). Sensitivity, or the ratio of correctly predicted fire pixels to the total number of pixels analyzed, declined to 30–40% during warm and dry years of increased fire activity, especially in models driven by anthropogenic variables only. This analysis demonstrates that if warming in Eastern Siberia continues, forest fires will become not only more frequent but also less predictable. We explain this by examining model performance as a function of either temperature or precipitation. This effect from climate makes it nearly impossible to segregate ignition points from locations, which were burnt several hours or even several days earlier. An increase in secondary burnt locations makes it difficult for machine learning algorithms to establish causality links with anthropogenic and other groups of variables

Climate warming shifts carbon allocation from stemwood to roots in calcium-depleted spruce forests

Increased greening of northern forests, measured by the Normalized Difference Vegetation Index (NDVI), has been presented as evidence that a warmer climate has increased both Net Primary Productivity (NPP) and the carbon sink in boreal forests. However, higher production and greener canopies may accompany changes in carbon allocation that favor foliage or fine roots over less decomposable woody biomass. Furthermore, tree core data throughout mid and northern latitudes have revealed a Divergence Problem (DP); a weakening in tree ring responses to warming over the past half century that is receiving increasing attention, but remains poorly understood. Often, the same sites exhibit Trend Inconsistency Phenomenon (TIP), namely positive, or no trends in growing season NDVI where negative trends in tree ring indexes are observed. Here we studied growth of two Norway spruce (Picea abies) stands in western Russia that exhibited both the DP and TIP, but were subject to soil acidification and calcium depletion of differing timing and severity. Our results link the decline in radial growth starting in 1980 to a shift in carbon allocation from wood to roots driven by a combination of two factors: a) soil acidification that depleted calcium and impaired root function, and, b) earlier onset of the growing season that further taxed the root system. The latter change in phenology appears to act as a trigger at both sites to push trees into nutrient limitation as the demand for Ca increased with the longer growing season, thereby causing the shift in carbon allocation