Tundra photosynthesis captured by satellite-observed solar-induced chlorophyll fluorescence

Accurately quantifying the timing and magnitude of respiration and photosynthesis by high‐latitude ecosystems is important for understanding how a warming climate influences global carbon cycling. Data‐driven estimates of photosynthesis across Arctic regions often rely on satellite‐derived enhanced vegetation index (EVI); we find that satellite observations of solar‐induced chlorophyll fluorescence (SIF) provide a more direct proxy for photosynthesis. We model Alaskan tundra CO2 cycling (2012–2014) according to temperature and shortwave radiation and alternately input EVI or SIF to prescribe the annual seasonal cycle of photosynthesis. We find that EVI‐based seasonality indicates spring “green‐up” to occur 9 days prior to SIF‐based estimates, and that SIF‐based estimates agree with aircraft and tower measurements of CO2. Adopting SIF, instead of EVI, for modeling the seasonal cycle of tundra photosynthesis can result in more accurate estimates of growing season duration and net carbon uptake by arctic vegetation

Lowering water table reduces carbon sink strength and carbon stocks in northern peatlands

Peatlands at high latitudes have accumulated >400 Pg carbon (C) because saturated soil and cold temperatures suppress C decomposition. This substantial amount of C in Arctic and Boreal peatlands is potentially subject to increased decomposition if the water table (WT) decreases due to climate change, including permafrost thaw-related drying. Here, we optimize a version of the Organizing Carbon and Hydrology In Dynamic Ecosystems model (ORCHIDEE-PCH4) using site-specific observations to investigate changes in CO2 and CH4 fluxes as well as C stock responses to an experimentally manipulated decrease of WT at six northern peatlands. The unmanipulated control peatlands, with the WT 2 kg C m−2 over 100 years when WT is lowered by 50 cm, while permafrost peatlands temporally switched from C sinks to sources. These results highlight that reductions in C storage capacity in response to drying of northern peatlands are offset in part by reduced CH4 emissions, thus slightly reducing the positive carbon climate feedbacks of peatlands under a warmer and drier future climate scenario

A multi-scale comparison of modeled and observed seasonal methane emissions in northern wetlands

Wetlands are the largest global natural methane (CH4/ source, and emissions between 50 and 70° N latitude contribute 10-30% to this source. Predictive capability of land models for northern wetland CH4 emissions is still low due to limited site measurements, strong spatial and temporal variability in emissions, and complex hydrological and biogeochemical dynamics. To explore this issue, we compare wetland CH4 emission predictions from the Community Land Model 4.5 (CLM4.5-BGC) with siteto regional-scale observations. A comparison of the CH4 fluxes with eddy flux data highlighted needed changes to the model's estimate of aerenchyma area, which we implemented and tested. The model modification substantially reduced biases in CH4 emissions when compared with CarbonTracker CH4 predictions. CLM4.5 CH4 emission predictions agree well with growing season (May-September) CarbonTracker Alaskan regional-level CH4 predictions and sitelevel observations. However, CLM4.5 underestimated CH4 emissions in the cold season (October-April). The monthly atmospheric CH4 mole fraction enhancements due to wetland emissions are also assessed using the Weather Research and Forecasting-Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) model coupled with daily emissions from CLM4.5 and compared with aircraft CH4 mole fraction measurements from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) campaign. Both the tower and aircraft analyses confirm the underestimate of cold-season CH4 emissions by CLM4.5. The greatest uncertainties in predicting the seasonal CH4 cycle are from the wetland extent, coldseason CH4 production and CH4 transport processes. We recommend more cold-season experimental studies in highlatitude systems, which could improve the understanding and parameterization of ecosystem structure and function during this period. Predicted CH4 emissions remain uncertain, but we show here that benchmarking against observations across spatial scales can inform model structural and parameter improvements

Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations

Natural wetlands constitute the largest and most uncertain source of methane (CH4) to the atmosphere and a large fraction of them are found in the northern latitudes. These emissions are typically estimated using process (“bottom-up”) or inversion (“top-down”) models. However, estimates from these two types of models are not independent of each other since the top-down estimates usually rely on the a priori estimation of these emissions obtained with process models. Hence, independent spatially explicit validation data are needed. Here we utilize a random forest (RF) machine-learning technique to upscale CH4 eddy covariance flux measurements from 25 sites to estimate CH4 wetland emissions from the northern latitudes (north of 45∘ N). Eddy covariance data from 2005 to 2016 are used for model development. The model is then used to predict emissions during 2013 and 2014. The predictive performance of the RF model is evaluated using a leave-one-site-out cross-validation scheme. The performance (Nash–Sutcliffe model efficiency =0.47) is comparable to previous studies upscaling net ecosystem exchange of carbon dioxide and studies comparing process model output against site-level CH4 emission data. The global distribution of wetlands is one major source of uncertainty for upscaling CH4. Thus, three wetland distribution maps are utilized in the upscaling. Depending on the wetland distribution map, the annual emissions for the northern wetlands yield 32 (22.3–41.2, 95 % confidence interval calculated from a RF model ensemble), 31 (21.4–39.9) or 38 (25.9–49.5) Tg(CH4) yr−1. To further evaluate the uncertainties of the upscaled CH4 flux data products we also compared them against output from two process models (LPX-Bern and WetCHARTs), and methodological issues related to CH4 flux upscaling are discussed. The monthly upscaled CH4 flux data products are available at https://doi.org/10.5281/zenodo.2560163 (Peltola et al., 2019)

Earlier snowmelt may lead to late season declines in plant productivity and carbon sequestration in Arctic tundra ecosystems

Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO2 later in the season

Extreme weather and climate events in northern areas: A review

The greatest impacts of climate change on ecosystems, wildlife and humans often arise from extreme events rather than changes in climatic means. Northern high latitudes, including the Arctic, experience a variety of climate-related extreme events, yet there has been little attempt to synthesize information on extreme events in this region. This review surveys work on various types of extreme events in northern high latitudes, addressing (1) the evidence for variations and changes based on analyses of recent historical data and (2) projected changes based primarily on studies utilizing global climate models. The survey of extreme weather and climate events includes temperature, precipitation, snow, freezing rain, atmospheric blocking, cyclones, and wind. The survey also includes cryospheric and biophysical impacts: sea ice rapid loss events, Greenland Ice Sheet melt, floods, drought, wildfire, coastal erosion, terrestrial ecosystems, and marine ecosystems. Temperature and sea ice rank at the high end of the spectra of evidence for change and confidence in future change, while drought, flooding and cyclones rank at the lower end. Research priorities identified on the basis of this review include greater use of high-resolution models and observing system enhancements that target extreme events. There is also a need for further work on attribution, impacts on ecosystems and humans, and thresholds or tipping points that may be triggered by extreme events in high latitudes. Key words: climate, weather, extremes, storms, northern region

Addressing a systematic bias in carbon dioxide flux measurements with the EC150 and the IRGASON open-path gas analyzers

Across a global network of eddy covariance flux towers, two relatively new open-path infrared gas analyzers (IRGAs), the IRGASON and the EC150, are increasingly used to measure net carbon dioxide (CO2) fluxes (Fc_OP). Differences in net CO2 fluxes derived from open- and closed-path IRGAs in general remain poorly constrained. In particular, the performance of the IRGASON and the EC150 for measuring Fc_OP has not been characterized yet. These IRGAs measure CO2 absorption, which is scaled with air temperature and pressure before converting it to instantaneous CO2 density. This sensor-internal conversion is based on a slow-response thermistor air temperature measurement. Here, we test if the high-frequency temperature attenuation causes selectively systematic Fc_OP errors that scale with kinematic temperature fluxes. First, we examine the relationship between wintertime Fc_OP and kinematic temperature fluxes for eight northern ecosystems. Second, we investigate how residuals between Fc_OP and CO2 fluxes from co-located closed-path IRGAs (FC_CP) are related to kinematic temperature fluxes for three different ecosystem types (i.e., boreal forest, grassland, and irrigated cropland). We find that kinematic temperature fluxes, but not mean ambient air temperatures or CO2 flux regime, consistently determine the absolute magnitude of Fc_OP errors. This selectively systematic bias causes the most pronounced relative Fc_OP errors to occur when “true” CO2 fluxes are low and kinematic temperature fluxes are high (e.g., northern ecosystems during the winter). The smallest relative errors occur during periods with large “true” CO2 fluxes and low kinematic temperature fluxes. To address this bias, we replace the slow-response air temperature in the absorption-to-CO2 density conversion with a fast-response air temperature derived from sonic anemometer measurements. The use of the fast-response air temperature improves the agreement between half-hourly Fc_OP and FC_CP for all open- versus closed-path IRGA comparisons. Additionally, cumulative Fc_OP and Fc_CP sums are more comparable as differences drop from 63 %–13 % to 20 %–8 %. The improved IRGASON and EC150 performance enhances the ability and confidence to synthesize flux measurements across multiple sites including these two relatively new IRGAs

Addressing a systematic bias in carbon dioxide flux measurements with the EC150 and the IRGASON open-path gas analyzers

Across a global network of eddy covariance flux towers, two relatively new open-path infrared gas analyzers (IRGAs), the IRGASON and the EC150, are increasingly used to measure net carbon dioxide (CO2) fluxes (Fc_OP). Differences in net CO2 fluxes derived from open- and closed-path IRGAs in general remain poorly constrained. In particular, the performance of the IRGASON and the EC150 for measuring Fc_OP has not been characterized yet. These IRGAs measure CO2 absorption, which is scaled with air temperature and pressure before converting it to instantaneous CO2 density. This sensor-internal conversion is based on a slow-response thermistor air temperature measurement. Here, we test if the high-frequency temperature attenuation causes selectively systematic Fc_OP errors that scale with kinematic temperature fluxes. First, we examine the relationship between wintertime Fc_OP and kinematic temperature fluxes for eight northern ecosystems. Second, we investigate how residuals between Fc_OP and CO2 fluxes from co-located closed-path IRGAs (FC_CP) are related to kinematic temperature fluxes for three different ecosystem types (i.e., boreal forest, grassland, and irrigated cropland). We find that kinematic temperature fluxes, but not mean ambient air temperatures or CO2 flux regime, consistently determine the absolute magnitude of Fc_OP errors. This selectively systematic bias causes the most pronounced relative Fc_OP errors to occur when “true” CO2 fluxes are low and kinematic temperature fluxes are high (e.g., northern ecosystems during the winter). The smallest relative errors occur during periods with large “true” CO2 fluxes and low kinematic temperature fluxes. To address this bias, we replace the slow-response air temperature in the absorption-to-CO2 density conversion with a fast-response air temperature derived from sonic anemometer measurements. The use of the fast-response air temperature improves the agreement between half-hourly Fc_OP and FC_CP for all open- versus closed-path IRGA comparisons. Additionally, cumulative Fc_OP and Fc_CP sums are more comparable as differences drop from 63 %–13 % to 20 %–8 %. The improved IRGASON and EC150 performance enhances the ability and confidence to synthesize flux measurements across multiple sites including these two relatively new IRGAs

Inferring CO2 fertilization effect based on global monitoring land-atmosphere exchange with a theoretical model

Rising atmospheric CO2 concentration ([CO2]) enhances photosynthesis and reduces transpiration at the leaf, ecosystem, and global scale via the CO2 fertilization effect. The CO2 fertilization effect is among the most important processes for predicting the terrestrial carbon budget and future climate, yet it has been elusive to quantify. For evaluating the CO2 fertilization effect on land photosynthesis and transpiration, we developed a technique that isolated this effect from other confounding effects, such as changes in climate, using a noisy time series of observed land-atmosphere CO2 and water vapor exchange. Here, we evaluate the magnitude of this effect from 2000 to 2014 globally based on constraint optimization of gross primary productivity (GPP) and evapotranspiration in a canopy photosynthesis model over 104 global eddy-covariance stations. We found a consistent increase of GPP (0.138 ± 0.007% ppm−1; percentile per rising ppm of [CO2]) and a concomitant decrease in transpiration (−0.073% ± 0.006% ppm−1) due to rising [CO2]. Enhanced GPP from CO2 fertilization after the baseline year 2000 is, on average, 1.2% of global GPP, 12.4 g C m−2 yr−1 or 1.8 Pg C yr−1 at the years from 2001 to 2014. Our result demonstrates that the current increase in [CO2] could potentially explain the recent land CO2 sink at the global scale