Climate change will reduce North American inland wetland areas and disrupt their seasonal regimes.

Climate change can alter wetland extent and function, but such impacts are perplexing. Here, changes in wetland characteristics over North America from 25° to 53° North are projected under two climate scenarios using a state-of-the-science Earth system model. At the continental scale, annual wetland area decreases by ~10% (6%-14%) under the high emission scenario, but spatiotemporal changes vary, reaching up to ±50%. As the dominant driver of these changes shifts from precipitation to temperature in the higher emission scenario, wetlands undergo substantial drying during summer season when biotic processes peak. The projected disruptions to wetland seasonality cycles imply further impacts on biodiversity in major wetland habitats of upper Mississippi, Southeast Canada, and the Everglades. Furthermore, wetlands are projected to significantly shrink in cold regions due to the increased infiltration as warmer temperature reduces soil ice. The large dependence of the projections on climate change scenarios underscores the importance of emission mitigation to sustaining wetland ecosystems in the future.

Soil carbon sequestration in global working lands as a gateway for negative emission technologies.

The ongoing climate crisis merits an urgent need to devise management approaches and new technologies to reduce atmospheric greenhouse gas concentrations (GHG) in the near term. However, each year that GHG concentrations continue to rise, pressure mounts to develop and deploy atmospheric CO2 removal pathways as a complement to, and not replacement for, emissions reductions. Soil carbon sequestration (SCS) practices in working lands provide a low-tech and cost-effective means for removing CO2 from the atmosphere while also delivering co-benefits to people and ecosystems. Our model estimates suggest that, assuming additive effects, the technical potential of combined SCS practices can provide 30%-70% of the carbon removal required by the Paris Climate Agreement if applied to 25%-50% of the available global land area, respectively. Atmospheric CO2 drawdown via SCS has the potential to last decades to centuries, although more research is needed to determine the long-term viability at scale and the durability of the carbon stored. Regardless of these research needs, we argue that SCS can at least serve as a bridging technology, reducing atmospheric CO2 in the short term while energy and transportation systems adapt to a low-C economy. Soil C sequestration in working lands holds promise as a climate change mitigation tool, but the current rate of implementation remains too slow to make significant progress toward global emissions goals by 2050. Outreach and education, methodology development for C offset registries, improved access to materials and supplies, and improved research networks are needed to accelerate the rate of SCS practice implementation. Herein, we present an argument for the immediate adoption of SCS practices in working lands and recommendations for improved implementation.

Quantifying the effects of multiple land management practices, land cover change, and wildfire on the California landscape carbon budget with an empirical model.

The effectiveness of land-based climate mitigation strategies is generally estimated on a case-by-case basis without considering interactions with other strategies or influencing factors. Here we evaluate a new, comprehensive approach that incorporates interactions among multiple management strategies, land use/cover change, wildfire, and climate, although the potential effects of climate change are not evaluated in this study. The California natural and working lands carbon and greenhouse gas model (CALAND) indicates that summing individual practice estimates of greenhouse gas impacts may underestimate emission reduction benefits in comparison with an integrated estimate. Annual per-area estimates of the potential impact of specific management practices on landscape emissions can vary based on the estimation period, which can be problematic for extrapolating such estimates over space and time. Furthermore, the actual area of implementation is a primary factor in determining potential impacts of management on landscape emissions. Nonetheless, less intensive forest management, avoided conversion to urban land, and urban forest expansion generally create the largest annual per-area reductions, while meadow restoration and forest fuel reduction and harvest practices generally create the largest increases with respect to no management. CALAND also shows that data uncertainty is too high to determine whether California land is a source or a sink of carbon emissions, but that estimating effects of management with respect to a baseline provides valid results. Important sources of this uncertainty are initial carbon density, net ecosystem carbon accumulation rates, and land use/cover change data. The appropriate choice of baseline is critical for generating valid results.

The domestic and international implications of future climate for U.S. agriculture in GCAM.

Agricultural crop yields are susceptible to changes in future temperature, precipitation, and other Earth system factors. Future changes to these physical Earth system attributes and their effects on agricultural crop yields are highly uncertain. United States agricultural producers will be affected by such changes whether they occur domestically or internationally through international commodity markets. Here we present a replication study of previous investigations (with different models) showing that potential direct domestic climate effects on crop yields in the U.S. have financial consequences for U.S. producers on the same order of magnitude but opposite in sign to indirect financial impacts on U.S. producers from climate effects on crop yields elsewhere in the world. We conclude that the analysis of country-specific financial climate impacts cannot ignore indirect effects arising through international markets. We find our results to be robust across a wide range of potential future crop yield impacts analyzed in the multi-sector dynamic global model GCAM.

The steady-state mosaic of disturbance and succession across an old-growth Central Amazon forest landscape.

Old-growth forest ecosystems comprise a mosaic of patches in different successional stages, with the fraction of the landscape in any particular state relatively constant over large temporal and spatial scales. The size distribution and return frequency of disturbance events, and subsequent recovery processes, determine to a large extent the spatial scale over which this old-growth steady state develops. Here, we characterize this mosaic for a Central Amazon forest by integrating field plot data, remote sensing disturbance probability distribution functions, and individual-based simulation modeling. Results demonstrate that a steady state of patches of varying successional age occurs over a relatively large spatial scale, with important implications for detecting temporal trends on plots that sample a small fraction of the landscape. Long highly significant stochastic runs averaging 1.0 Mg biomass⋅ha(-1)⋅y(-1) were often punctuated by episodic disturbance events, resulting in a sawtooth time series of hectare-scale tree biomass. To maximize the detection of temporal trends for this Central Amazon site (e.g., driven by CO2 fertilization), plots larger than 10 ha would provide the greatest sensitivity. A model-based analysis of fractional mortality across all gap sizes demonstrated that 9.1-16.9% of tree mortality was missing from plot-based approaches, underscoring the need to combine plot and remote-sensing methods for estimating net landscape carbon balance. Old-growth tropical forests can exhibit complex large-scale structure driven by disturbance and recovery cycles, with ecosystem and community attributes of hectare-scale plots exhibiting continuous dynamic departures from a steady-state condition.

Pigment-based identification of ozone-damaged pine needles as a basis for spectral segregation of needle conditions.

Air pollution affects large areas of forest, and field assessment of these effects is a costly, site-specific process. This paper establishes a biochemical basis for identifying ozone-damaged pine trees to facilitate efficient remote sensing assessment of air pollution damage. Several thousand live needles were collected from ponderosa pine (Pinus ponderosa) and Jeffrey pine (P. jeffreyi) trees at three sites in Plumas National Forest and Sequoia-Kings Canyon National Park. These needles were assembled into 504 samples (based on the abaxial surface) and grouped according to five dominant needle conditions (green, winter fleck, sucking insect damage, scale insect damage, and ozone damage) and a random mixture of needles. Pigment concentrations per unit needle area of chlorophyll a, chlorophyll b, and total carotenoids were measured. The following pigment concentration ratios were calculated for all samples: chlorophyll a/total carotenoids, chlorophyll b/total carotenoids, total chlorophyll/carotenoids, chlorophyll a/chlorophyll b. The group of ozone-damaged needles had significantly lower mean pigment concentrations (family-wise p < 0.01) and significantly lower mean chlorophyll a/total carotenoid and total chlorophyll/total carotenoid ratios (family-wise p < 0.01) than all other groups of needles. Ozone-damaged needles had a significantly lower mean chlorophyll a/chlorophyll b ratio than all other groups except one (family-wise p < 0.01). Linear discriminant analysis with three factors (chlorophyll a concentration, the chlorophyll a/carotenoid ratio, and the chlorophyll a/chlorophyll b ratio) and subsequent maximum likelihood classification of damaged and non-damaged needles gave an overall cross-validated accuracy of 96%. These ozone-damaged needles are biochemically unique in relation to other needle conditions in this study, and further research is needed to generalize these results.